So welcome everyone also on YouTube and Moodle if you're watching this today We will be doing regression analysis, and I'm really really excited about it because Regressions and novus and these kind of things are kind of my specialty in a way. It's where I did my PhD in so I I'm quite familiar with how to do regression and I think a lot of you guys are actually really wondering when will the statistics start? We already did a little bit of statistics right like t-test and nonparametric t-test, but I think Now we're starting to do regression right so now it becomes a little bit more serious in the statistical part And I will just go through Let me see what do we have for today, okay? So yeah first off the exam will be on the 21st of July at 2 You Can't register yet in Agnes I think because I got a mail from someone after the last lecture saying I looked into Agnes And it's not there yet, but that is because you can only register in Agnes two weeks before four weeks before the exam so four weeks before the exam means that on the 2nd of July it should be possible to register so if you've Looked in Agnes and you haven't found the exam yet Then this is the reason The re-exam will be on the 9th of the 9th again via zoom Moodle and the best grade will count So for everyone who says I can't really study for the first exam So we'll do the re-exam don't just do the exam because if you do the exam You can always do the re-exam to improve your score And if you only do the re-exam then of course you only have one chance and having two chances is better than one chance So yeah, it will be online on Moodle I hope the 2nd of July and then you should all be able to register So yeah, I will I will keep track of it and people who can't register just send me an email And then I will let you know how to register So the assignments from last week, I I hope people were able to do it So hands up in chat or just shout something in chat if you were able to I Have registered for the exam yesterday. Oh nice. So then it is already in Agnes. Okay, good Then the guy who mailed me I will mail him back to to Make sure that he checks Agnes again because he checked us Agnes last week and it wasn't there last week But good good. That's very very good news. Thank you for telling me perfect So yeah shout out in chat if you were able to make a package. So did you build your first R package and were successful or Didn't because of some error I'm really curious to see how it went My name is Mausie. You're also not a VIP.  Why are people that are here like week after week not VIPs? So but you were able to build a package That is very good. I like your name by the way. My name is Mausie There you go, and you are also a VIP. So you got your little diamond Good so two people were able to build the package I know Daniel tried as well and failed halfway through and asked me some questions Like by email. Are you here Daniel?  So shout out in chat for Daniel Where are you? Like you're finished with your work, right? So you should be able to attend the lectures now But I can show you guys like how I made my package But I made it exactly the same way as as I told you during the lecture. So I can I can show you So let me open up the correct files. So it is docx Then we go to our course and then we have your package name So first off The description file. So this is the description file that I used more or less very similar to the Description file that I showed you during the during the lecture So it just tells you or it tells are what you need to run this package, right? So it gives a name a version when it was created a title the author they maintain her You need are and at least version 3.0 it has a description and a license and of course it doesn't have to be in this order So you can change the order around if you want to and that should all be perfectly fine Then there's a namespace file which of course Initially used to be empty But during the lecture I told you that if you want to give a function to the user Then you should export it. So my package is exporting two functions one Which is called my first package function just like in the lecture and then export the call test C from R Which is the example which I showed you guys on how to call C or C++ code And of course since we are using C code which gets compiled We have to load the dynamic library So we have to load the compiled code first and that is also done using the namespace file So this loads the compiled DLL under Windows or the SO file when you're under Linux and then we export to functions I can show you the functions that I wrote So I have one file which is called my first package function dot R Which looks like this. I also made an internal function I see now just so that I have an internal function to annotate And I just made a function which does nothing and of course this one just brings something to the screen But this one is not exported to the user. So the user cannot call it Or gets an error when they try to call it and it says that no such function is found There are some ways that you can still call it And then we have the call Call C from R test file and this is again the same file as that I showed you during the during the lecture We have some manual files, of course, so we have the call C test from R So again, very similar to what I showed you And of course we have the my first package function Like this and had this is kind of the minimal amount of things that you need to mention Yes, so you need to mention the name the alias the title you have to give a description Even though the description can be like very short You can have a usage section arguments details values and examples and then the keywords have to be method So I'm hoping that everyone was able to build it because kind of these are the four Minimal files that you need to build a package for R and like I said like building or being able to build a package in R is very useful because There's only a limited amount of people who can program R and from the people who can program R There's only a limited amount of people who can actually build a package So being able to build a package for other people when they ask you Yes, so if you if people know that you can program in R Then they you can tell them on but I can also build a package And then in the future when they have some nice code that they wrote to do an analysis Then you can help them build their own package And this will it's a nice way to get co-authorships on publications and just by Contributing your skills as a package or you can get some nice Publications or some co-authorships. Hey, you won't be first author, of course All right, so kind of that's everything that I wanted to say about how to build a package So you need and like you need a description file a namespace file You need some our files where there's some code and you need some manual files to describe What code you are going to give to the user? And then if you did everything correctly and you put everything in the right folders Then if you do an RC in the check, it should just run through it without any issues And you might get one or two notes about like minor things But if everything went correctly, then that that's how you do it So if there's no other questions about package building, then I think we should just start with the lecture I'll wait a little bit so that people can ask questions in chat But I hope that everyone was able to build their own first package. I think it's a nice Halfway point right because we're like last week we were halfway through the lectures and Halfway point is really nice that you are at a point where you can write your own code or write your own little functions and Have the ability to kind of give this code to other people And head of course, there's much more to it right if you want to get your package on cram Hey, then you have to follow their system So you have to submit an email first saying that I want to upload a package with this name Then they sent you back a link and then on that link you have to upload your package But that is all quite straightforward. It's just following the kind of manual line All right, I see nothing in chat So everyone was able to build their package, which is really really good and I'm really really happy about that. So There will be some questions about package building on the exam All right, so for today regression analysis, I have an overview So what will we be doing or what will we discussing today? So we will be discussing basic regression So first we will start with single linear regression And talk about things like confidence intervals and how to plot confidence intervals and residuals and these kinds of things Besides that I wanted to talk to you guys about multiple linear regression, which is actually my kind of my PhD study field And then I wanted to say a few words about quadratic regression Because this fits really nicely into the data set that we will be analyzing and then a few words about model selection And how can you how can you compare different models that you have right? So you have a phenomenon for example Precipitation that you measured and besides that you have all kinds of variables, which are predictors for the precipitation For example, are there clouds? What's the temperature? What's the air humidity and then all of these things you combine into a single model but of course you can combine them in many many different ways and you want to have a kind of Way to decide this model is better than the other models And that also means that there's one slide in Latin for me today So that's a slide that I'm going to give you for free. So you can listen to my beautiful Latin speaking And I'm curious to see how it will work out. I I Officially had Latin in high school But since it's a dead language, no one knows how it's spoken. So which it should be fine. No matter how you pronounce it It's always correct, right? So these are the topics for today But I want you to ask questions, right? Like on this slide It is an introduction for you guys on how to use the LM function and how to use the ANOVA function and some of the Helper functions around it. But like I said, I have a lot of experience in linear modeling But I try to keep kind of the lecture short Latin died my condolences. Well Yeah, the only one who speaks it is the Pope, right? And he's the authority on it and Yeah, officially, there's no No way we don't have any recordings of people speaking Latin in the old days. So But that like today we will have a very short Lecture it's like 41 slides. So I'm hoping to fill at least one and a half hours with it But if you ask questions, we can have a much longer Discussion today like I'm here for you guys until five So ask unrelated questions if you want more details So today we will do basic linear modeling, but don't worry next week We will talking about linear mixed models and a week after we will be talking about generalized linear mixed models So head because it's a massive field linear models So I made a little bit of an overview slide for you guys so This is more or less everything, right? So here we have what we are going to talk about today. So today we are going to talk about linear regression About a nova and about multiple linear regression the analysis of covariance I will kind of skip over because it's very similar to the analysis of variance And these are called general linear models. So that will be the topic for today And they they are really nice, but they have very severe limitations. So Things like repeated measurements cannot be handled by standard general linear models So next week we will be talking about when you have repeated measurements or when you have time series data And how to use mixed models to kind of analyze these kind of structures have where there's Structure between the individual measurements that you have But for today, we will only be talking about when you have Randomly measured things from a population and you want to calculate things which are valid for the whole population Well, and you can do the same thing of course with mixed or linear mixed models but the linear mixed models allow you to kind of Optimize your PV or optimize your p-value sounds a little bit like p-hacking, but it allows you to deal with things like repeated measurements Things like random effects where for example certain individuals have a or share a father or they share a mother How to deal with those? And then the week after we will be talking about generalized linear models And we will be talking about things like logistic regression Binomial and log linear models So in the case that the input variable that you have is not a Kind of a continuous value variable. So when it is for example a yes, no answer or When you have very specific amount of levels or if you have a very very weird Finer type structure or a very weird measurement structure that you have but for today general linear models, so very basic very short introduction and Ask as much questions as you want I can talk about this shit for like hours and hours on end Which I actually do during my work But since it's a lecture and I don't want to overload you guys with all kinds of like little details I try to keep it kind of high level, but if you're Interested in think like oh, but have the data that I have has this kind of a quirk to it How do I deal with it? Then just let me know right because then we can I can just make an example in R And we can just talk about it and since I only have a limited amount of slides We have enough time to to deal with your questions All right, so very basically regression analysis is the statistical process for estimating or kind of finding relationships among several Variables that you have and there are many techniques for analyzing these several variable models Have but the focus is on the relationship between the dependent variables and one or more Independent variables, so had just terminology speaking the dependent variable is the output or the effect So the thing that you have measured and want to explain and the independent variables are thinkers like the input or the cause And those are the things to which you want to distribute the variance to right so in my mind In there is variance in the dependent variable, right? So you did like a hundred measurements of a certain thing and now based on other variables You want to distribute the variance that you see in the dependent variable on to the independent variables to kind of Assign variance, so it's kind of a sign-to-blame game right because you want to say well This genetic marker is responsible for a change in this genes expression And then not only is this genetic marker responsible for it, but there's also some environmental influence on it, right? So you are trying to Take all of the variance that is in your dependent variable and assign this variance somewhere so the basic regression model looks like this so the basic regression model is like why which is our Dependent variable right the response and that is then predicted by a function and in this function We have our independent variables and these independent variables are then coupled to something called beta and Beta is the regression coefficient for X So if you have five independent variables, then you will have five beta values Right, so there are some constant which are not mentioned in this model But hey you have n which is the number of independent measurement So that is the length of your of your vector y so of your dependent variable, right? So I might have measured a hundred cows or I might have measured milk yield from a hundred cows so then n is a hundred and then k is the number of unknown parameters So that is the number of kind of independent variables Right, so I can have I have measured milk yield in cows And now I for example have five other measurements like the body weight the length of the tail the does it have horns? Other measurements and I want to assign the variance in the milk yield to these different independent variables I Hope that's clear so hey, we have beta which is the thing that we are going to compute Then we have X which is a matrix more or less But if you have only one then of course it's just a vector But X tends to be a matrix of different things and then we have Y Which is the dependent variable and that is the thing that we are trying to predict the thing that we are putting our variance Where we are taking the variance and distributing it across the different vectors of X alright, so regression comes with a lot of assumptions and I Kind of want you guys to know all of them right because if any of these assumptions do not hold Then your regression model is not valid, but of course there are some which are more More important than the other ones So I highlighted in red the things which often go wrong and the things that are really important for a valid regression model So the first assumption is is that the sample is representative of the populations for the inference prediction Right that means that if I want to make a statement in the end, right? So I do my modeling and in the end I want to make a statement like Humans who have this gene tend to be bigger Right then I have to have randomly sampled from the human population That means that just doing the study in Germany is not good enough because Humans is much broader than just Germany right there's people from Africa as well and there's people from Asia And there's people from America and they are not German and this is one of the things which is Like in science goes wrong most of the time Right so people draw Conclusions and make it seem like these conclusions are valid for all cows on the planet or all humans on the planet But then when you look at how they sampled they did not randomly sample across the planet They only sampled Eastern Europe or they only sampled Western Europe or they sampled a very small area So the sample that you are taking has to be representative of the population that you are going to do the inference on and This is the thing that goes wrong the most in science and in many many scientific papers Really nicely written papers very good data Relatively good results, but then they their conclusions are way too broad and this is because of the first assumption Underlying linear regression or underlying ANOVA analysis the second one is very important and that is that the error right so the error is the thing which remains right so if I have like my My variance in the thing that I want to predict and I have assigned Variants to all the different things that I have used to kind of catch this variance then in the end I'm always left with some kind of error Because of course no measurement is perfect. No measurement machine is perfect however, the errors in The error is a random variable with a mean of zero conditional on the explanatory variables So that means that if I want to rephrase it It just says that if I look at my model and I take my my phenotype of interest and I fit it All the different things that I am interested in or want to look at what the effect of this is on the on the phenotype that I measured then I'm left with an with a Additional term and this is my error term also called the residuals of the model and the residuals need to have a mean of zero Conditional on the explanatory variables. So of course the residuals don't have to be normally this They have to be normally distributed with a mean of zero. That's kind of the idea behind it The independent variables are measured with no error Also, this goes wrong a lot of the times But it is not that important because a little bit of error never hurt anyone and that's kind of what I think But the independent variables normally should not be measured with error And so for example, if one of my predictors is the sex of the animal that we're analyzing, right? Then of course, I cannot make mistakes there I cannot say this is a male while it's actually a female and I cannot say this is a female while it's actually a male, right? So so I have to be very careful when I define my independent variables because these independent variables The model is much better when you have no error in these measurements. Of course errors always occur So you you can't guarantee that there are no errors But then this is one of the assumptions underlying the and the regression model is that there there are at least a minimal amount of errors Then one of the things that often goes wrong as well is that the predictors are linearly independent it is not possible to express any predictor as a linear combination of the others and This is called co-linearity Yes, so that two things are co-linear and this is something that is really hard to avoid and generally almost impossible to avoid There will always be some linear dependency the question is just how much is there and We can we can go back to this later but this is one of these things that is really important and which is something that you can actually check by just Building your model and then taking your independent variables and start switching them around Because if you if you have like three independent Explanatory variables, right? And you have several p-values for them or several beta coefficients When you start moving them around and the beta coefficients start like changing by a lot Then of course then that means hey Florian welcome to the lecture Have if you start changing them around a lot Then the problem becomes that you cannot assign the variance because then you cannot uniquely say well being clouded has a certain effect on the raininess because the cloudiness is coupled to the temperature Errors are uncorrelated which means that the errors cannot be correlated to any of your predictor variables Generally, people don't even test this but it is one of these assumptions that is there and then the last one which is very important but a Lot of times overlooked because the word is just too hard to pronounce is that the variance of your data should be Approximately equal across the range of your predicted variable values And this is called homo's cladasticity or absence of hetero's cladasticity I'm not even going to say it if this is not the case log transform or other methods might be used instead Right, so but this just means Let's just make a little graph of that, right? I have my board here So why not just use it once in a while like I haven't really used it in a long time So I don't think we ever used it yet. So let's go to full screen and I will draw a nice.  That's my pension fund right there I'm in Germany. So you get money for bringing back bottles All right, so homo's cladasticity or hetero's cladasticity. Let's remove the attack of the lockdown as well And just like make a little graph, right? So linear regression like looks like this a little bit so on one side we have our independent variable in deep and Here we have our dependent variable, right? And now what we want to see when we do something like this is that there is a relationship So imagine that my independent variable so that the thing that I measured has five levels, right? For example Human height let's say that we divide human height in like five groups, right? So we have people who are less than 140 Then we have people who are 140 to 150 centimeters 150 to 160 and so on right and then you have people who are larger than like two meters Now if we have our dependent variable for example the body weight then of course the first group has a certain Variance right so we have some measurement points and then we have more measurement points here as well We have more measurement points here. So that's three groups then we have four groups and Then now we have five groups Right, so the the haters clodicity. Do you use our studio? Well for the lectures we generally use our Standard so the R GUI, but I have used our studio in the past. It's just our right. It doesn't really matter which you use but haters clodicity or it's such a bad word or homo-clodacity Is the fact that when you fit your model right because we are going to fit their best fitting line So we want to do a regression line, but the thing that we don't want to see is that the variance in the first group is Much smaller than the variance in the last group, right? So if the measurements here go all the way like this right in the last group And now the variance here is much much bigger than in the first group and this means that now your model is not valid If you see a pattern like this And this is something that people rarely check they rarely check for haters clodicity and there are ways to solve this But generally you have the standard that had the bigger your measurements become also the bigger the variance becomes But this means just that the line the regression line cannot be estimated very correctly Because in this case had a regression line like this would also fit quite well and a regression line like this would also very well And that is because the the the points that are here at the outside of the distribution Have a much larger influence on our regression line than the points here, right? So points here are much closer to the regression line So they don't really influence the regression line that much but in a heteros Heterosclerosis situation it ends up that these points here because of the variance being much bigger in this group And this is very very detrimental to when you are doing a regression model And this kind of invalidates all of the predictions because when I'm now starting to predict things for people who are larger than 3 meters And then of course you can see that these lines are kind of going very broad So it is it is very big You see this in fermentation experiments when you measure the od 600 of cells the error from the od measurements Effective reading at a lower value compared to a larger value. Yeah. Yeah. No, that's that's true You see this in a in a bunch of measurements and it's actually like every kind of Measuring machine suffers from this right because you have a certain dynamic range But large numbers tend to be associated with larger variants So it's not something that will completely invalidate your regression model It's just something to be aware of that this is happening and Florian is pressing buttons again Florian, I can't do the next slide in three different languages like pick one. Which one do you want Dutch or German?  So face cam All right, it's better, right? So you guys can see me again All right, so these these are the assumptions for linear regression Right, so there's a lot of assumptions and we generally need to check them all the ones in red are the ones which often go wrong So if you ever need to do a journal club And you have a paper where they use linear regression Make sure that you check these assumptions and the first one is the most important one because this is what very very often goes wrong in research that people make Conclusions which are overly broad, right? They only measured people in a very like narrow area of the population and now they try to generalize their results to everyone Right, which is often what you see Hey, which is also probably the major issue with phase three trials for medicine Which is usually done in like one country or like two or three countries And then they conclude well the vaccine is safe for everyone on the planet Which actually Is it right you can lock? Yeah, yeah, so it also says here right so the last part the homosclerosity you can lock transform to get rid of this effect There are other ways that you can use it to kind of minimize the effect of these like extreme points on your regression line But lock transformation is one of the most common methods But there are other ones like you can use square root transformations as well And there are some other transformations which also affect kind of the higher range much more than the lower range So but and you can have it the other way around as well Right, you could have a large variance in this group and a small variance here Which means that you're unable to predict down, but you're actually pretty able to predict up But that really depends on your measurement machine and where the error is but making a plot like this is always always good to kind of Check if your data is valid All right, so those are the assumptions I'm going to do this in the Netherlands for Florian so Florian here we go when n is bigger than k and this goes back to the slide here where we have n is the number of Undependent measurements and k is the number of unknown parameters that we want to estimate so the number of beta's when you have more Metals then have independent variables and the measured error is Normally distributed, so it is a Gaussian Distribution, then we speak that there is an excess of information So an overflow of information And this and this can be calculated by just saying that we have a hundred measurements and we have five Beta's that we want to estimate and if we subtract that from each other then we have ninety-five Freedom degrees less or more it's not exactly freedom degrees, but if we If the assumption is that everything is linear, so that we just don't have groups, but that we just have a steep linear effect then we can the Overflow of information to make statistical predictions about the unknown parameter So that was in in Dutch and I will read it now in English so the n right which goes back to n and k So the number of independent measurements that I have Related to the number of parameters that I want to estimate Had the difference between these two is the amount of Excess information and the excess of information is kind of expressed as n minus k Of course, this assumes that you are looking at a linear Relationship and not at a grouping relationship like I did here So if the if the independent variable is just a linear Or it says just a continuous variable and your dependent variable is also a It's also kind of a continuous variable than every Every k measurements or every beta that you estimate takes away one degree of freedom So when I have a hundred measurements, and I'm trying to estimate five independent variables Then I have 95 degrees of freedom and the more degrees of freedom I have the more certainty I have or the more statistical power I have to make predictions about the beta parameters that I'm trying to estimate so about the unknown parameters and That is where the power of regression comes from This also means That when I'm trying to estimate too many Unknown parameters with a very limited amount of measurements, right? If I have only 20 measurements, of course, I cannot estimate 30 different parameters because then had 20 minus 30 is minus 10, which means that I actually actually have a lack of information So I don't have enough measurements to make any statistical Or to say anything statistically valid about my unknown parameter. So about my independent variables all right Linear regression in R is done via the LM function So I'm here using the air quality data set which we already saw during the plotting lecture So just for you guys to remind you the air quality data set has ozone temperature, I think wind speed and What's the last one because it has another one? data air quality Let me let me actually just load it and then the data set is called air quality, of course Solar radiation, that's the last one. So it has the ozone concentration Solar radiation the wind speed the temperature and then the month and the day at which it was measured So very basically if I want to predict the ozone temperature Like I'm like I'm going to do here, right? So I'm saying that the ozone in the air is Predicted by the temperature. So ozone is my dependent variable Temperature is my independent variable. Of course, you can directly see that one of the assumptions is broken in this model And if anyone can tell me Which one of these which assumption is broken in this model? You will have my like eternal gratitude or no, I'm not gratitude like I will be very very proud of you guys So had just very basically think about the air quality data set, right? So it has a couple of different measurements We can make the model by just saying linear model Take ozone as our dependent variable Temperature is our independent variable and then say data is air quality and then we just store this model If we want to see what's in the model, then we just do the summary function for the model and then have we throw in the model So let's go back to the assumptions. So which assumption is broken? So There's 16 people viewing so at least one of them should be able to do a guess And there's no wrong answers Like you won't get points subtracted on the exam when you don't know the answer Like it's just something like hey, if you just think about how it is Which ones of these might not be valid for the current model when we say that the ozone is dependent on the temperature Go go go. No one come on people Is it number one is it number two is it number three is it number four number five or number six No one number one. Maybe number four. Okay. There there come the guesses All right, so the number one there is something to say but the problem is is that for this data set we cannot know What we are doing an inference for number four the predictors are linearly independent We only have one predictor and if we have one predictor the predictor is always linear independent, right? Because there's no no one to be no the the issue here is number three. You cannot measure temperature without any error That means that like the temperature can be forty five point three three three seven two one one eight seven three But no matter how you write down your temperature You're always wrong because there's always one more digit behind the comma That you could have measured that you didn't do so there's always a little bit of an error Right, so the independent variables are measured with no error But that's impossible because you cannot measure temperature without an error You can measure sex without an error, but temperature. No Because you can say well, it's forty three degrees, but it could be thirty nine point nine eight and then there's zero point zero two Error in your measurement, so no matter where you put the digits no matter how many digits you write before me It's a small error, right? And that's why I'm why I highlighted this one in black because this is of course almost impossible Because there are no machines that do not have any error in measurement even a basic thing like a thermometer Has a little bit of error So the assumption which is broken is assumption number three no error in the model Of course the linear independence doesn't come into play here because we only have one Independent variable and it cannot be linearly dependent on any of the other ones, but it's good that you think about it, right? Alexander Alexander is their assumption for error in model Um Well, yeah the assumption for the error right the error term in our model here Have where we do kind if you would write this down on a board, right? And you would you would want to publish your model here Then the model that you're doing is looking something like this So and you would write down is this visible by the way if I just do it here So you would write down something like ozone is Predicted by the temperature plus an error Right, so ozone ij is temperature on i error on j Right, this is the way that you would write down your linear model in a paper So the the error term here, which you always hide or almost never mentioned right those are the residuals So when I when I regressed Ozone on temperature then there is of course variance left and this variance here goes into the error term And then when I look at the error term Then when I plot the error term it look should look like a normal distribution With a mean of around zero, but if we look at the summary from this model very basic model We say that the ocean concentration in the air is dependent on the temperature that we measure So and of course it doesn't fulfill all the assumptions, but that's okay You can never fulfill all the assumptions of the model. It doesn't make the model invalid It just means that the model is less valid than it could be And of course if you take a better thermometer, right? If you have a thermometer which measures on five degrees accurate head and of course changing your thermometer by one Which is like accurate to two digits behind a comma will already improve this like third Assumption right the less error the better you can never avoid all errors together But if we do the summary right in our then this is what our tells us So the first thing that our does when you do the summary of the model It gives you back the formula that you used which is useful in some cases because if you if you build like six different models In our and you store them all in variables called a b c d e which you should not do right because you should do a Model you should give variables good names, but people generally don't It's good to be able to see which model you fit So here you see some information about the residuals and you see that the residuals here are like Alexander said they're not Actually zero right the median is slightly negative And you see that the minimum or the first quantile range is from minus 17 to plus 11 So this cannot really be a good Gaussian model, right? So I think Alexander is a little bit right that the that the residuals of the model are not a Gaussian distribution But we can't really see this from the slide But based on the numbers here we can assume that that the mean is definitely not zero or the median is definitely not zero It's slightly negative and it seems that the that the negative side of the normal distribution is way longer than the positive side Because it had the first quantile is at minus 17 while the third quantile is at 11 So there's a like a six-point difference here The thing which we want what I want to show you guys is actually the estimates right because the estimates are the beta estimates So the the estimates that we want to predict, right? So what do we learn we learn that if the temperature would be zero The ozone would have a value of minus 147 round it right and then for every Degree of temperature increase the ozone seems to increase 2.4 points And this is of course a little bit strange because that would mean that at a temperature of around like 20 degrees Celsius You would still have kind of a negative ozone concentration Which is a little bit weird, but this is just the data. It's just the regression model I don't actually know why it does that because it should not be because the ozone values are always positive But it's just a bad model That's the thing that we will conclude in the end that this is not a very good model and that it's kind of overfitting at the At the lower end. Yeah, but this these are the things that we learned so the the intercept is the location where Temperature is zero so all the all the Independent variables are zero and then this is what the prediction is for the ozone concentration And then temperature itself gets a bet of 2.4. That means that every Degree of temperature increase leads to around a two and a half point increase in the ozone concentration And then here we see the multiple r squared the multiple r squared is 0.48 Which means that around 84 percent of the variance in the ozone concentration is Determined by the temperature which is relatively high. So it's not the model explains a lot It's just probably that the model isn't really valid But in our regression you can do via the LM function if you don't want to show the results You have to use the summary function All right, so and if we now want to plot this model Right, then we can do that very easily Because are when you do the plot it allows you to just input more or less a linear model.  It's not Maddie two shoes. Thank you for subscribing Why wouldn't you be doing R on Twitch, right like it looks so beautiful like let's open up the R window like It's our it's our as in awesome. Oh, okay, not. Oh crap like no I just got away from our and now I'm on Twitch and there's even more are Yeah, well, we've been doing this for quite some time so this is actually lecture number nine So there's eight more lectures on Twitch that you can look back and like learn all All right, so hey in our Have we can use more or less the same way of writing down a formula to also make a plot, right? So if we want to make a plot which has ozone as the Dependent variable and then air quality as ozone as the I always mix them up So this is the predict this is the predictor and this is the response Let's switch the terminology a little bit strange. I hadn't found you yet, but I'm glad yeah No, well welcome to the to the lectures like we're also on YouTube But no no no we're in season three already. Yeah. Yeah. Yeah, we also did a bioinformatics course last winter semester And this is the second time that I'm doing the our course on Twitch. So Yeah, so we can use the plot function We can just use more or less the same strategy as before the only thing that is different from the model here We say data is air quality But in the case of the plot we cannot add data is we just have to say take from air quality the column ozone and Then regress this are and regress this on the air quality data set take the column temperature. I Do PCA it's 19 because I want to have like these filled dots right because I don't like the open dots Which is the default for our and then I say color is blue because I just like blue so we make a blue plot I missed everything. Oh, you didn't miss anything like If you know what are is then like you're already ahead Okay, and now when I want to plot the regression line also in our it makes it very very easy So I can just say from LM temp, right? Which is my my model that I built here. So from Ellen temp I take the coefficients and then I say take the intercept, right?  So straight line in the plot I'm going to just write it down for you guys because like I know a lot of people Don't do a lot of mathematics. So let me just do like So a straight line in our are a straight line in any mathematical formula says that why Right, which is the why position of my point is determined by something called a Right, so the intercept so plus B X Right, so X here is my X coordinate B is my Is B is my directional coefficient and a is my intercept So if I want to plot a straight line if I want to pull a straight line in our I have to get the a coefficient, which is the Intercept and then of course I also have to get the temperature coefficient Which is my B? So that is my how much do I increase with every increase from my air temperature? All right, and then I just say use the upline function say a equals a b equals b color is red line White is 2 and then it will plot the regression line in my plot and here we can also see why now all of a sudden my like Intercept becomes so massively negative is because at a temperature of around 60 degrees Fahrenheit There's almost no ozone in the air But of course the linear model doesn't know that negative ozone concentrations cannot occur so it just Extrapolates a straight line and here you can directly see one of the drawbacks of modeling and linear modeling is that often your linear models They holy shit. No. I don't want to become famous block all these people help. Where's my moderator? No, don't become famous. I'm already famous enough Anyway, and so here you can see the intercept is negative because just the linear model doesn't understand that negative Negative ozone values cannot occur right. There's also always some ozone chill. I'm on it. Yeah. Thank you. Thank you You're the best moderator ever But just plotting the line and this is generally what you want to do in in a publication, right? Of course, you want to make the plot look a little bit better Hey, you might want to remove a couple of these outliers. You want to use GG plot or something like that, but in the end just Use the upline function use the plot function and you can make really nice plots and I like the fact that You can actually specify your your dot plot very similar to the plots Are very similar to the LM function The only drawback is that you have to every time say air quality dollar ozone air quality temperature But of course we've I've told you guys that if you use the with function, you can just say with air quality plot ozone Squiggly line temperature and then of course you can do the same thing So and like in the plotting lecture We already showed you how you don't have to start or how you don't have to repeat air quality all over again all right, so Confidence intervals, right? So if we look back to the model, we see here that every estimate has a standard error right, so Because we are just doing an estimate of a certain relationship We cannot be sure that the the temperature Exactly rises with 2.4287 points every temperature right because sometimes it will be a little bit more sometimes it will be a little bit less so every every variable or every predictor variable that you estimate comes with an error So how do we do this? So when we calculate our confidence interval right where we where we have 95% of our data in The first thing that we want to do is calculate the margin of error, right? So in standard regression, we can use the t statistic, right? So the standard error from the LM summary we need But that is given to us and then we need to find the critical value, right? Because the critical value is the value at It's the value which we define because we want to have a 95% confidence interval or we want to have a 90% Confidence interval or we want to have a 99% confidence interval and just to Give the researcher some wiggling room in to say how certain he wants to be, right? So the first thing that we say is we want to define our critical value and generally in biology our critical value is 0.05 right very similar to the alpha value But in this case since we are looking at confidence intervals, we have to do this two-sided So because it is two-sided we have to define our probability boundary as 1-alpha divided by 2 So that means that in this case our probability boundary for our 95% confidence interval is 0.975 I need to know how many degrees of freedom there are fortunately the amount of degrees of freedom is also mentioned in the Well not in the summary, but it's measured in the ANOVA test But we can get the number of degrees of freedom quite easily because it is the N value So the number of measurements that we have minus 2 y minus 2 Well, we lose one degree of freedom for estimating the intercept and we use one degree of freedom for estimating the temperature component right because we're estimating two Two parameters not just the beta for the temperature, but also the intercept And then I can just use the Qt function So this is the the t statistic function and they can say well give me Qt of 0.975 and minus 2 so this is the Quantal t distribution we can use and then we get our critical value And now the margin of error at each point of the line is just Calculated as the critical value times the standard error. This is really difficult. So I made a little example But it's three. So I will take a very very short break. I had some coffee somewhere But yeah, you guys take a break as well This week break number one the animated GIFs I did not select So the animated GIFs were selected by Daniel I'm really happy that people actually are interested in the lecture and actually want to contribute something like animated GIFs Did you record it? Yeah, yeah, I'm recording so Everything everything fine. Chill. I'm on it anyway, so You guys can enjoy some animated GIFs for around five to ten minutes I will be back at three ten and then we will continue with an example of how to calculate the Confidence interval and how we can then make a nice plot Hey, because of course in the regression at just plotting the regression line is not enough We want to have some nice confidence interval Surrounding it to show people how good our model is and of course after that We will start expanding our model and making it more and more complex All right, so I will see you guys at three ten and enjoy the animated GIFs


Good morning my beautiful internet friends. I just wanted to show you real quick what peak athletic performance looks like This is what I look like the majority of the time. Let's go on adventure So I was recently sent the Traxxas e-bike and I want to take you guys on a little bike adventure today and talk to you about something That's been on my mind I used to do videos like this all the time that were more like vloggy follow me around day in the life And they kind of went out of style on YouTube, but I felt like doing one today step one You guessed it put on real clothes for public consumption. Also get my leg on She's so cute. No one's gonna be listening to a single word that I'm saying. We're gonna do a little something different for today's video I wanted to take you on a biking adventure and I was gonna find some like picturesque place to sit down and talk about this thing That's been on my mind But I got to that beautiful place and there were other people around and the thought of like doing the Influencer thing and setting up a camera and a mic it just it just felt like it would be ruining the experience for everybody else So what we're gonna do instead is I'm gonna take you on the adventure as I am talking about this thing Okay, something kind of cool about my leg This is a pin lock socket meaning all I have to do push the button pop it off And previously I've made videos about having a separate biking leg I no longer need that because I have a pin lock for every day It's not as like bulky around my knee And so I am actually able to pedal without having to bring an entirely separate socket which that is a relief fine mom I'll bring my jacket. Do you hear my dogs very upset in the background that they aren't coming with me Don't make fun of me for the way My helmet looks it's safe. So a couple of months ago. I made a very Knee jerk decision to move out of Colorado Springs where I'd lived basically since I was 10 years old I was in a relationship for a decade married for seven the entire time we lived in the Springs And there were additional traumatic things that happened in that city And I always took this perspective of I don't want to leave if I have a bad feeling towards a place But after one conversation with someone where I was like I really I just feel stuck here right like emotionally I feel stuck. I'm just I'm gonna do it and so very quickly. I found a place I signed a lease so that I would like force myself to actually do it I was genuinely very scared to move for a lot of reasons. I talked about that in my moving video But it was I think the best single decision I have ever made for myself in recent years Because oh my god, I have been able to start actually Breathing for the first time. I want to say in decades One of the many things I like about this Traxxas e-bike is that it has that throttle So that especially when I'm like getting started, right? I can just hit that throttle and then like find my balance get pedaling from there It makes a surprisingly large difference in the ease of getting started I like many of us have always had a very complicated relationship with my emotions I learned at a young age suppress those suckers, right? You don't want to feel things Feelings are scary. They're dangerous. They're bad. Don't do it Just say no which led to some very unhealthy and somewhat dangerous coping mechanisms throughout my life And in the past couple of months with the help of therapy for the actual first time in my life I've started feeling emotions without judging them or always trying to intellectualize them And one of the most interesting things to me is that everything has been so deeply bittersweet And I think that's something I used to fight like if I was in a good situation, I'd be like I should be happy I'm judging myself for not being better. If something felt a little sad I might completely dive into like the depths of despair and in this process of making Peace with what I feel Everything feels bittersweet and I've begun to actually grieve so many things that I think I desperately needed but had no idea Having to walk away from a relationship that I thought was forever I invested every bit of my being into I did everything that I could there is so much to mourn There is so much sadness. There is so much anger. There is so much Confusion at times and moving away from all of those memories having some space even from people who I love very very dearly But we're so entwined with all of that. I've begun to feel happiness that is just happy I've begun to actually process things. So I just stopped to adjust my seat height because it felt a little low I was like on the side of the road. I was pulled off plenty of room But two different bikers were like, hey, are you good? Hey, are you good? So lovely when people are nice and watch out for each other It is turning into quite the beautiful Colorado day. Alright, so I've made it about 15 miles so far We're in a pretty little dirt bike path. I no longer hate myself for having emotions I think this is so like this sounds so hypocritical for me to say out loud because I have always very much promoted Feeling things publicly personally whatever I think the reason for that is because I struggle so deeply with that in myself So I preach it very loudly. It's okay to feel things. It's okay not to be okay It's okay to take a break from life for a while, you know be angry. That's important all of that But in reality that I like 32 years of my life I've just hated myself for feeling things deeply and like denied that and it is absolutely life-changing To be able to be really angry and feel that fully and be like I am friggin Piss and then it like actually moves through my body. I don't have to do anything about it Right, I don't have to act out in anger But I can be mad and be lifting weights and be thinking all kinds of angry things and it doesn't make me a terrible person And then maybe 20 minutes later I am genuinely happy about something without it all feeling so deluded because I'm not denying myself feeling So I recently started weight training again, which is something I very much enjoy however I do find it extraordinarily knowing that I'm now hungry 24 seven like I can't go Two hours without eating something. I feel like which is why I bring protein bars everywhere I want to talk about this it feels very self-indulgent right being a youtuber is pretty self-indulgent sometimes But to encourage you if you are someone who resonates with anything that I said Allowing yourself to feel things even if it's for two seconds at a time that might extend to four seconds next time Right, even if it's in little pieces I think is such a vital part of our human experience that not many of us are taught giving yourself mental space Feel that acknowledge it grieve it be mad about it cry about it I believe does allow us peace on the other side and I have begun to experience Peace in my life since moving and since doing some of this work for the first time ever It's revolutionary for me to actually feel okay While also being deep in grief and deep in anger and hurting feel safe with myself That's a new one the one of the things that I find bizarre about being an amputee is that if I'm cold Right if I'm a little chilly. It's like 40 out. It's not bad at all I don't even need a winter coat and my legs are a little chilly when I'm biking and there's something about Phantom sensation that gets wonky with temperature where if my meat leg is like a little cold, right? My non-existent phantom foot will be ice frickin cold fully immersed in ice bath just freezing So as I'm pedaling it feels like one of my feet is like getting frostbite. Thankfully. It's not as it doesn't exist There's some quote from a movie that's just been ricocheting around my head that goes we cut off So much of ourselves to be rid of things faster the idea that we're hacking off emotions and hacking off pieces of ourselves Because it's uncomfortable because it's difficult because we want to be through it want to be done We want to be better. We want to not feel this way and in doing so we disable ourselves We deny ourselves humanity feel the things ask for help connect with people be vulnerable Where to say for you to do so? That's what I've been trying to practice. It's been working very well And we are safely back home I did underestimate how cold like 38 degrees feels when you're moving in the wind because by the time I got back I could like not feel my fingers less than for next time Also, I just want to say a quick word about the e-bike that I'm using Thank you so much to Troxas for sending that to me for sponsoring today's video if you have ever considered getting an e-bike I would highly recommend you check them out link in the description the model that I have is their fat tire It's the links one I'll put the link to the actual one that I'm using in the description as well But if an e-bike is ever something you've thought about getting whether you are an adaptive biker like me or not I think they are absolutely amazing I could not bike without them because as you guys know sometimes my leg is a little finicky It's a little painful and I get really scared of being stuck places Stranded on the road somewhere and using this e-bike makes me feel safe because it has assistive pedaling So if I want to pedal it's gonna help me with that and if things go to hell and it's way too uncomfortable I can literally just throttle all the way home this bike in particular I believe it has at least 33 miles of just pure throttle no pedaling necessary as someone who really likes biking I think they are the bees knees now. I know I know real bikers Don't like them, but I am a okay with that if it allows me to still go outside and do stuff Thank you so much for going on a little adventure with me today. I hope you're hanging in there I hope you're taking care of yourself. I hope the new year brings beautiful things for you and I will see you there Thank you so much to my patrons for supporting these videos. Thanks again to Troxas for sponsoring it and to you Lovely viewer. Yes you watching this video right now Thank you so much for spending a few minutes out of your day here with me today and Leo scratching his ear meaning that my camera is probably Shaking you could be anywhere else in the world Doing anything else what you chose to hang out with me here for a few minutes, and I really appreciate it I love you guys. I'm thinking about you and we will see you in the next video. Bye guys


Hello everyone, Steve here back with Retro Tech and I've got another monitor ready for us to take a closer look at and today we will be taking a look at the 54Q series. So this series came out in about 1995 and it was a huge revolutionary step for the PVMs line of Sony's monitors. This particular monitor was the first to introduce the service menu, well this line of monitors. So you've got the 54Q which is this is the 19 inch 1954Q. This one is from 1995 and this one had a medical unit brother almost and that's the 1953 MD. Now if you're in other countries it might be 2053 and but that was the medical version of this monitor so this was kind of a representation of a PVM that was made for other uses besides medical so they separated the medical unit and then they had this unit for maybe video editing or some other type of work that didn't involve medical equipment specifically. So again this one had the very first example of the on-screen service menu with the geometry settings you can you know adjust on screen in front of the screen rather than turning potentiometers in the back of the monitor. So let's just go ahead and get right into this thing. I want to show you some of the specific features as well as what this particular unit did differently. So first we're going to go ahead and take a look behind the monitor and see some of the hardware. Shielding and you've got your power supply board over here now on the left and then all the regular yoke settings but let's take a look down here specifically at the deflection board. So we've got our flyback here and this is still an overall so there's the bottom one if you turn it up it'll actually make your screen kind of blow out and really bright white and you might even see some lines show up or vice versa if it's too low it'll just go black. So if you're seeing something on your screen and it's not working sometimes you actually need to make an adjustment on there but the problem is here it's very hard to access back here you don't have a lot of clearance I'd probably say less than two inches. So the one on top of that is your focus. There is one more potentiometer that's behind my colored cables right here that's this one and this you can't see it right from this angle so let's lean forward a little bit. This potentiometer this will again that will adjust your screen center.  So just wanted to show that that's pretty much standard on all these monitors. I do want to try to keep these convergent strings in place because they were set originally professionally and that is one of the hardest things to adjust on a monitor's convergence I feel. So down here talking about convergence we're going to go and look at the neck board closer and you'll see another potentiometer right here and this is going to control our overall screen convergence so if you have colors that are separating all across your screen you can turn that convergence potentiometer and it'll line it up. Those are the only potentiometers that are going to be in this monitor anymore and everything else has been moved to the on-screen menu. Just a quick run-through on the back here this this is something that should be noted if you're looking at this particular monitor. For some reason on this one they've got three standard lines where it's A, B and C. C has the S video the other two are just standard composites and then you've got one RGB slash component input so this this will use RGB or component it'll allow it it's switchable on the front but you can't use both simultaneously because you have to either come up with a solution to change the signal and then change the setting on the monitor which is doable. Now please note it's just got one input here some of the 1953's actually have two inputs for RGB and component which is really nice which would have been at a different button setup these are this is also the first time you're going to see these particular knobs added to the CRT in the monitor. So first let's take a look at our button setup down here we've obviously got our power button which goes in and out like any other and if we get a power signal we get an LED indicator there now if you see this remote if it says an orange light shows up here then there's a setting where it's on remote settings and you just want to try to turn that remote setting off that just means that it's someone at the past was using it in remote sequence with other monitors and maybe had a remote control unit controlling a lot of the adjustments from somewhere else. First we've got our volume knob and we've got a contrast knob and a phase of chroma brightness and aperture so just to note settings such as s-video and even component then you'll be able to use chroma and then phase and aperture are really more for composite signals trying to clear them up so they might not do anything on some of the other buttons and talk about it. So you see obviously up top it says line A line B and line C and then under that it says RGB component SDI line slash RGB and X sync so if you just saw what I did so example whatever is lit up is the line you are on so if that's the only one lit up that's the A line that's the B line that's the C line there is no SDI card installed in this monitor but from the manual it says there was an option to add that so that's not going to be very common I've never seen one come with the SDI but that is what that means so most of the time you're going to ignore that one but for example to get it in RGB you have to first have it on A and then you have to go down here and you have to put see what says line slash whatever RGB for example that means you have to push that RGB to turn RGB and then you need to if you need X use the sync you'll have to have that pushed into so this example is a setup for RGB if I was using component instead I could use the same setup for that if I did not need sync for example then I could turn the sync off and I could use my component and my line button and that would be the only one pressed in so just keep that in mind that's how you use your setup for your inputs the bottom down here has our degauss button which is just a standard degauss then there's some other calibration tool setting so you've got a blue only which if you press it will only let you see blues which kind of shows up as just grays and that helps for a grayscale calibration of colors we've got an under scan mode that allows you to go to an under scan size on your screen and then so if you're not if you're seeing something and your screen looks small check and make sure that's not turned on we've also got an hv delay which is kind of for if you want to separate your sinks and you need some kind of sink I don't really use that normally but that is what that does and then you've got a 16 by 9 feature which like I said this was for use in video production more so this is a 16 by 9 photo it'll actually squeeze your picture down which is pretty useless but that's the way it goes and then you're going to insert enter the menus here by using this menu button let's back out and take a look at the screen we'll still go ahead and go through the patterns for you of how to do it we're going to go back to our test grid and we'll start from here so I will kind of turn off or try to turn off the inputs if I can so you can see the menu better but again we'll press menu on here we can set up our promo we don't want to do that that's done with a light probe I believe and then everything else the user configure that's where you might turn on your remote settings but and you can do some other smaller controls but the this one is pretty important so we talked about in the past adjusting your color temperatures if you want to adjust your gains and your biases so your gains are going to be like your color intensities and then your bias might be the grayscale of each color so the game you know if you need to do a little color adjustment you can go in there but this is the actual color temperatures again we got the 6,593 that's in nearly all the other monitors I have it on the 93 because that's my favorite and same way as the other menu to get into the sub menu we're going to press degauss and enter at the same time while we have that up and let's go through our settings again this is so if we go up and down we press menu to go up we press enter to go down and let's go I'm sorry menu to go down so it's the opposite of that menu press that to go down and enter to go up and we're going to start here and look at our settings the first let's just concentrate only on the first 17 settings because those are only those are the only ones that are going to be of most importance here for 95 percent of the time making adjustments on your monitor the first one for me I'm using 60 hertz frequency so I don't need the 50 I need to get to the first 60 which is my horizontal frequency and that's my centerness that is the center horizontal adjustment on my screen so if I press up I increase that value and my screen goes that way if I press down my screen value goes that way and if you notice here while you're moving this you'll see let me show you an arrow block that will point to whatever setting is set in the menu right now so if you need to make a one-time adjustment for a single game you can do that and then press the input your on button again it'll take the menu away and you can just use it temporarily and you can turn the monitor off right now or you can press degauss and it will actually send it back to whatever setting is stored inside the menu if you're making a permanent I'm sorry if you're making a permanent setting on here then to write that setting you're going to hit the enter button when you've made your adjustment you hit the enter button I'm sorry not enter you hit degauss you hit the degauss button you'll see it display right on the screen and if you hit it again you'll see a star show up next to your number right here right above the right you so you hit that twice quickly it writes that into memory and that's how you make an adjustment and keep it then you can go up from there and you can go to the next setting which is video phase which is a little bit it's just the same thing as horizontal frequency you have a lot of control over your horizontal centerness on these monitors so you can get it dialed in correctly and same thing to write it and change it it's all through up and down arrows and degauss twice to write your value and if you don't like a setting again I said you could get out of it and hit degauss and it'll go back or just turn it off and turn it right back on and it'll go back to the original settings pull your menu back up and start working again number six starts our vertical size on there and then our vertical center is next so you can see that move down the screen and up the screen and then our next setting is the again horizontal size and okay so now we're going to get in here to our corner adjustments first we've got pin phase and I'm just going to show you that's this ninth setting here you can see what that does that's more that hexagon trapezoid where we're trying to get the uh the screen moves this way towards it and backwards so if there's something really crazy look on the screen check this to try to get it balanced out correctly next we have pin amp which is your whacked out screens in the corners where it looks like a globe or the opposite of a globe uh so if you see something like this consistently at the corners you can tell that you need to make an adjustment in your pin amp so these were all settings that were in the previous model to this but you had to do them through potentiometers and a screwdriver in the back of the monitor while you were trying to adjust it up front so this one has a lower pin amp setting that allows you to do the lower curve as well as the upper pin above that number 12 so you've got the complete control of the corners it's not as uh the L series has more settings where it can let you get in the middle so sometimes on these monitors it's very difficult to get things in this area and this area and vice versa to get them perfect they're always going to look a little bit wonky uh I mean just a little bit so just keep that in mind with this because that's not something that's controllable that is controllable in later models of PVMs now we're going to get the sexy setting which is just a weird little thing that lets you control your center whether it's not lined up for some reason or you're trying to help center the lines down uh from the corner and that's the sexy setting uh vertical linearity so let's change to a different screen here for second and look at these circles linearity there's some monitors will have two linearity adjustments available this one only has a single one where you can go up and down in that linearity to try to help and you're trying to make your circles as round as possible you don't want any ovals but that's the goal of linearity is to try to help you get all your circles here lined up in a circular setting and then our next setting of importance is our vertical bow and then we have a lower vertical bow and finally a vertical angle so let's start working we're going to need to do the rest of these settings from the other screen vertical bow let's go ahead and just show you that that's just a lean on here where you can tell I'm doing this from one setting to the next so you can see how it uh it shifts that screen and that's that's what that one controls and the next is lower vertical bow so this is just working on the lower end of the screen you can see your corners moving so if one of them looks crazy you can move it around that way and the last is vertical angle which is the angle of your access point so you're you're turning that one way or the other on its axis like if you're if you're looking this way if your screen's looking a little crazy like that that's what that adjustment is so again you have to go through and try to use those settings that's about 12 or so that you will be able to individually use for the actual geometry on your screen on this model of pvm then you'll have to use a combination of all those to try to clear it up as best as possible and again if you don't like the settings turn it off and turn it back on and it'll give you back to what you were it didn't write you know your your other settings so that's something that you need to think about on there while you're working again this one does need a little bit of adjustment still but for the most part we're going to get out of this stuff and there you go so it's a very great monitor 600 tv lined rgb component a lot of inputs and it was an advancement for pvms but that's just kind of a close look at how to get it adjusted correctly your screen quality everything else is pretty much standard to pvms i hope you enjoyed this video and please be on the lookout for future features on single monitors as well as other crt content please give the video a like if you enjoyed it and have a great day


Why 254, imagine? Welcome back to the broadcast. My name is Yvonne Annula. Tonight we talk about ideas for investment with Sylvia Apondi who is CEO of Sylvia Crew Cleaning Solutions. Remember can be part of our conversation tonight by simply tweeting us beginning with the word why news at Yvonne Annula. 254 channel the hashtag to use is why 254 updates beginning with the words why news. Now Sylvia, you are so much welcome to the broadcast tonight. Thank you. Sylvia Crew Cleaning Solutions, how did this start? Okay, Sylvia Cleaning Services which is abbreviated for Sylvia Intercleaning Company. It's a cleaning company which started when I was still working with Sirma Company and Advocates. I had a passion before I was also still working with Strami then from Strami I started working with Sirma Company Advocates. When I was working with Sirma I had that passion before I had that passion but by that time I wasn't like I had the ability to move out or rather to have something that can help me out also to make the dream come to reality. So when I was working with Sirma Company Advocates is when I started looking on this thing deeper. Yes, I have a passion for cleaning services. Why can't I try this? So this is basically passion based? Ya, it's passion based. You didn't go to school? No, I never went to school. It is something that is passion based. Let's talk about capital. It's a major problem especially when young people want to start something. Is this a business that I can start with a few coins? Ya, it's a business that you can start with a few coins, whatever you have. You don't have like maybe must be saving like a lot of money or rather be having a big capital for you to start. But it's something that you can start so long as you have passion. Passion is what will drive you to have maybe something big or rather something you are dreaming about each and every day. So you have been in a position so far to employ and you employ young people or old people or it doesn't matter so long as you know the job you are good to go? Okay, personally you eat most people that I do employ they are young people because most of them they are self driven. There are people who like they are looking after they really want to know as in they want to build themselves. Most of them will find others they are part time students. You will find others they are just from form 4. They are saving some amount so that they can also go to maybe tertiary for tertiary education. So most I can say most of my employees they are young people. So I am interested in knowing the recruitment procedures that you use. Do you like have an agency that does this for you? No, what I do is we always have a training that is based in our office we have a training. Most of the time we have a training like we can have a training for 2 or 3 weeks where people are trained how to use different kind of chemicals where they are used. Which kind of materials to be used? FPS and everything. Ya maybe also you will find like you can employ some of the people that have not even ever tried out cleaning services or whether they have ever worked with cleaning services. So what you do you have to teach this person how to handle them up. What to do even the PR we also do the training of PR like how they have to deal with the clients because we not only deal with maybe the common areas but also deal with offices.  How long does it take them to consume that information and be able to work with your machines and the detergents? The detergents. Most especially cleaning services it all about passion. Most especially the person you find or rather the employees if someone is so much interested to work or rather they know what they have come to do at work most of them they would take long. If you can notice a person will take maybe 1 week or rather 3 days the person has already known a lot of things. So for the period that you have been in the industry most young people of course go for white collar jobs are young people really willing to take up this particular line of business? Not really. I haven't met a lot of young people who are really interested in taking this job. Yes but I can tell young people to take this job because it really needs young people because it really needs our minds strength energy people have to move from one place to another so it really needs young people so that they can move and do the business. Talking of young people maybe being competent does that mean that the most young people that you've employed or not young people rather the most old people that you've employed are not that competent enough? Not that they are not competent enough they are competent I cannot say that they are not competent enough but at the same time most especially you will find like young people these are jobs you can move from one place to another you can find like in a day you have like three jobs so you need a group that will work from this place after the first job the first town has ended you go to another place you move to another place so it is all about the energy you find these people that are not that much swift when the old people are not that much swift compared to young people How do they do the coordination? Ya I do the coordination we have the field supervisors we have also the people who are in charge like the crew who will be in charge the field supervisors and the crew who is in charge that the people who monitor the work and maybe if at all there is any job that is an outside job that the people who monitor how people are going to work the materials and everything Alright Silvi maybe you can just give us an outline of how your day starts and how maybe it ends Okay my day Okay my day starts as per the day that is the day starts different I can say my Monday starts the week Tuesday will start my Monday can start maybe from as in with the field work my Monday can start with the a day can start with the field work a day can start with you being in an office or rather being in a meeting Alright let's talk about this as far as your job is concerned My job is concerned most of the time I'm taking not in field work like marketing Personally I'm the one who is marketing it most of the time I'm the person who is marketing it so most of my time after I put to the office what I do is marketing moving from one place to another looking for tenders in business Alright now let's talk about maybe customer relations how have you been able to relate to your clients some maybe have gotten services that maybe didn't make them happy how do you deal with such cases with cases like maybe the customers unsatisfied if you find a customer who is unsatisfied in fact you should take it as a point for you to grow because a person will come and tell you silver haven't done this for me rather you haven't met the standards that I was looking for that's the best client so with such kind of a client what we do we have a guarantee that's maybe our uniqueness or rather that's we have a guarantee if our job won't satisfy you we can redo it again So that deals specifically with retaining of the customers Yes retaining of the customers and also not only retaining of the customers but also the customer to satisfy you to other clients alright you have been in this line of business for quite some time who are better ladies or gentlemen ladies ladies ladies maybe it's just something that is inborn I can say because ladies are the people most especially they do cleaning of the house, the cleaning we just know from day one ladies are the people who are supposed to be doing the cleaning and everything but what I can say ladies are the best ladies are the best when it comes to cleaning to come to those needy gritties things that are supposed to be removed So let's go back to like Sylvie crew cleaning solutions what do you specialize with specifically we specialize in cleaning of corporate places we specialize in cleaning of upholstery that is sofa deep clean carpet deep clean we also specialize in post construction cleaning fumigation and pest control industrial cleaning we also specialize in one of cleaning that is maybe for comprehensive house cleaning or rather just need a light cleaning of your house structures about fumigation fumigation yes fumigation there is a difference between fumigation and pest control fumigation is what is done agrenist maybe in an industrial place if those industrial things which are supposed to be exported but pest control these services is done agrenist the pest like cockroaches bed bugs mice yes talk to us about maybe the challenges that you have seen so far in this line of business in this line of business yeah there are not a lot of challenges compared to advantages most especially as a youth youth were given like an upper hand compared to those other maybe the other people who are like in this line of business but they are old because when you go to someone maybe to a client the client will prefer you as a youth compared to someone else yes so as a youth some of the challenges that I can say maybe it's just exposure like for me maybe if I go for someone for a client who is more corporate we want to know how as in the experience in the field we will also want to know if at all I've ever worked with a corporate company before of which maybe not maybe I can meet the standards of that place but because maybe I don't have experience or rather I haven't worked with any corporate company I might be denied the job alright remember you can still be part of this conversation tonight but simply treating us beginning with the word we are talking about ideas for investment so Sylvia let's talk about a young person that wants to start this kind of business so where do I start from the logo requirements to maybe customer satisfaction and so on where you start like maybe business registration yeah take us through that okay first of all you must have you must register your business no you name such a business your business name if it goes through you register your business it will depend on you if at all you register it as a limited or rather limited liability or rather just as a business name then from there you are good to go it doesn't need a lot of things coming up with the name like other instances where some names have been rejected maybe simply because they are not that catchy enough or a name won't be rejected because it is not catchy a name will be rejected because maybe someone else have already used it alright so in the next 3 years where do we expect to see Sylvia cleaning solutions Sylvia in the next 3 years yes in the next 3 years Sylvia Kru I have a goal like in the next 3 years we are going to be in East Africa should be serving in East Africa not only in Kenya because for now we are in Nakuru we are in Limuru and also there is a client at Narok but we are expecting in the next 3 years we should be out of Kenya Kenya, Uganda and then East Africa apart from that I am also having a passion of support services like maybe for transportation like not only just dealing with office and messenger but also having a support service like that for delivery like maybe you can have a rider yes what would you recommend the government to support you to promote this particular sector what I can say about the government is to lower the legal fees for someone to do fumigation there is a fee that they have to pay which is so much approximately 8,000 yes for someone to do garbage collection you know you have papers but for you to transact that business there is a fee you have to pay so maybe for even youth to feel like they can start a business or rather in grow themselves they should lower these legal prices but right now that we are talking about Sylvie Crow what is unique about your business unique about my business there is a lot of uniqueness about my business of which I have already said but there is a uniqueness about my business is we are 24 hour in response other companies don't do that no, it explains myself about it in that for us we have people who are stand by like if we are working for you we have a problem with the being we have a problem with whatever services we are offering you we can respond in the little time we can then something else about us is we have tailor made structures or rather tailor made services we don't just go for a client because the client needs these whatever we used on the other client this is what is being used on the other client no, we have tailor made for each and every client that means every client will meet will meet the expectation so friendly as we wind up is this a business that any young person out there can take up yeah it's a business that any young person can take up so long as what cleaning needs is passion that's what I can tell so long as a person has passion the young person has passion I don't think even man is a problem so long as a person has passion the passion and the hard working because when you start you really need to have to be in the field rather than in the office so thank you so much Sylvie for coming through we really appreciate it there has been Sylvie who is the CEO of Sylvie crew cleaning solutions and for her it only takes a passion and it will be good enough to go in this particular line of business thank you so much for being with me my name is Yvonne enjoy the rest of your viewing


Hey everyone Shaber 1000 here today. I'm on the river and Monkey had a couple things to do so She's gonna go do them and I'm gonna sit here and fish for a little bit. Hopefully we'll catch something I got my body camera on today Hopefully it holds out monkey dropped it onto the pavement. So It turned on and everything I did a test recording Check check one two one two three four check Monkey dropped my camera, so Just checking it so hopefully It'll be alright Tell you more about that later. It was kind of cute so anyway, let's get these poles baited up and want to use hers in mind and Let's see if we get anything stick around guys Okay, so it's got some chicken gizzards here. I do have some livers in there If I want to switch off, but this is monkey's rod and reel right here so I'm putting a kind of a big chunk on it and I'm gonna set it out there and Geez what's going on here? There we go We'll set it out there and Just leave it Then I'll fish with mine. I'll work with it. I'm not going to use any lures today As always, I know a lot of you guys want to see me do some bass fishing I do have some lures. I'll have to get some lures and all When I get the boat straightened out I'll take the boat out and we'll do some We'll do some bass fishing this summer.  I will do that for you No biggie. I Just don't do it a lot her Her reel is going to need taken apart and cleaned up. I think it's got a little rust in it. It feels kind of funky So but let's get this one cast it out Yeah, see it it stops because that's about all the further she casts and So, you know, if you're casting out say 20 yards And you do that for a few times like go out fishing a few times and you and you keep and you keep casting about say 20 yards It it gets used that and it'll like stop Not every time but it'll get and that's where you start reeling it in So it kind of gets tight on the real inside of here and or on the drum or whatever you want to call it But what it does that then it's you know, it kind of wants to stop So like mine, I always mine before I Before I cast and start fishing I'll cast out two or three times Get the line, you know get the line Straightened out more. They do need restrung And I don't know if I strung them last year. So that's better. That's about halfway out. So that's good. All right now Want to get mine? I just got a little piece for mine So I'm gonna get mine baited up and get it casted out. I'll kick you back on here in a minute. Hey guys So I got mine casted out Just in here waiting Hasn't even been five minutes yet. I opened up a pop lit a cigarette Yeah, she uh, I brought some stuff down. I walked back up to the car through this little maze thing here they got and And I seen something fall I've seen her bend down and pick it up I've seen her wiping it off and then I've seen her looking back at me. I said, did you drop my camera? Yeah, so well, that's great. It's okay because it's your body cam. So there's still a camera Anyway, that was kind of funny I Gotta get a couple rods. I have a couple more reels at the house. I gotta get two or three more rods and Then I've got her dad's deep-sea fishing one You don't cast it, you know, it's just like you take your boat out and you drop it down or you control with it It's like a 275 pound rod But yeah down here in Florida you can fish with three four five poles if you want rods In Ohio while when I left six years ago anyway It was only allowed to have two rods per person in the water at any given time and one time Max and I was fishing this little area and We got casted out and everything and She'd walked back to the car, which was only honest man 30 40 feet max she went back to car to get something and She had hers in the water and she was just fishing with one and The game warden rolls up, you know, you're doing any good. I said, I don't know. We just got here like five minutes ago He says well says, you know, you can't have more than two rods in the water. I said, I know that one's hers He says yeah, but technically she's not right here with it if you're watching it for you're actually fishing with us Look, you know, just brought me the wrong way and it was his attitude I said look, I'm not even gonna argue with you.  I say that she was right here already I said see she's right here. She was like 30 40 feet away. That's not what I'm getting at I'm not gonna argue with you.  Whatever, you know, have fun with that one You know waste the court's time because you know my ex walked 30 40 feet back to the car to grab a drink, you know You know, I ain't got time for I'm not gonna argue with that's what I just told you I'm not gonna argue with you right me up and I'll deal with it in court right now I want to fish He didn't well there's huh Turn around walked away and didn't didn't say anything. I'm like man, you know, and he was sitting there We seen him sitting there when we came in what he was doing was using his binoculars checking people fishing I mean he had to see us. I mean no more did we get casted out and she goes. Oh, I gotta get my drink I think it was and then here he rolls up, you know trying to write me up $50 fine or whatever for more than one Rod night and you know, it's it's a it's a each rod is separate So like if you would have four rods out there, I could rate you up for two of look. I'm not gonna argue with you, you know So that's story for today But uh, it's still early in the afternoon yet Water's really low really low today. So Someone painted where the end of the ramp is over there on that on that wall And I really don't think I Might be able to get the boat in here if I wanted but it'd be close Because the water's so low So, you know, that might help us out a little bit because I know when the water's up there they're back in the woods, you know what I mean, but uh Yeah, I see a fish swimming over there It's about this long because the way the sun's shining and suddenly like I don't know foot deep Over in that area. So All right guys, I'll be back with you. Let's see if we catch anything. Okay, so switched over to some chicken liver I'm getting bites. I just had one on my rod.  So I was just talking to a gentleman paddled up here a little Little kayak that's why I'm here. I know I've only been here about 30 minutes. Yeah, he was telling me he's got his dog with him Last week down here gum slew. That's kind of diagonal from where we go Well, it is diagonal across the river from where we go fishing at or camping at and I've had you there a few times on our boat Last week I Had my canoe not the kayak.  That was probably five six pounds. Wow.  She's Nuts when you hook a fish She hit the gun and when she did my battery slid over and that was all it took momentum Flip the canoe over. No way.  That's the Yeah, the edge the rail the boat.  He's trying to get his dog was up under the canoe He said got everything back except these batteries, so that's good. I didn't ask him if he got the fish But I figured he probably did But uh, yeah He said the bass are hitting real good in here now be right now because it's really low, so Yeah, and I'll be the time to do some bass fishing for you guys. I Don't know if my body camera was rolling for that story or not Yeah, so if it was I just told you a story for nothing You heard it twice. I don't know. I Don't know if it's rolling now. Yeah, I got a rolling now. I'm just getting a few little hits nothing major Nothing big enough. I think To get the bait and hook in its mouth, so But that's okay. We're still out fishing, right? I got something messing around with my rod here This one here, so I Think there's something on the end of it, but I'm not sure Acting a little strange. Let's see.  I hate that. I always catch a turtle down here. Oh, yeah, always Well, let's wait let's see what he weighs I got it on pounds. Oh Crap. Yeah, he came out of the net. Let's see if I can get him back in the net. We'll wait 5.62 5.62 all right Now let's get him off there He went around the side of the camera. I thought he's gonna drag the damn Camera I try to get the hooks out of their mouths whenever I can I think I'm gonna have to cut this one. I hate doing that, but it'll rust so There it goes Let me get hooked back up here See what else we can do. Well, I've been down here for several hours. The only thing I've caught Was that turtle but I mean they're biting. I'm getting all kinds of bites, but they're small They got to be little fish Hope monkey gets there pretty soon because mosquitoes are out. I don't have any repellent with me. So They're not biting yet, but they're flying around me, so I Figure she gonna I figure she making her mom supper and then she'll probably eat too before she comes and gets me So yeah, nothing nothing major. I mean a couple good bites, but like this one here is this It's hitting but that's about it It's like that and then they'll usually leave sending that's That's it Had a couple good runners, but you know by the time I grabbed the rod, you know, they were gone But they are hitting Well, it's 834. I'm starting to get bit now like mosquito bit Uh Monkey's not here yet. So hopefully she'll be here soon. It's getting pretty dark. I know it doesn't show it in the camera But it's getting pretty dark Still just getting bites nothing big enough to take the bait or anything like that so This is probably gonna be it if I catch anything after this It'll be bonus footage. I'll let you know in the video somewhere that I have bonus footage coming up, but So I don't know I Thought you know A lot of catfish would be hitting but it's really shallow like right in here out about 30 feet you can see the bottom You got to get out towards the middle if you want to go deeper into the water, but Yeah, it's still fun still fun day at least I got a video up for you. We did catch something So anyway guys, thanks for watching. I appreciate it She bears the myth of being a legend gone for now Hopefully this doesn't turn into a solo overnighter Because I didn't bring any food or anything. So I haven't eaten yet today either Getting a couple bites again, but Nothing major. He's probably a little bluegill or something. Maybe some crappie I've seen a turtle smaller turtle out here swimming around. I'm trying to stay away from him Hate catching turtles All right, we'll see you guys in the next one if there's any bonus footage It'll be in right now. If not, I'll see you in the next one. Bye. Bye guys Well, it's 902 about 903 now. I think she just pulled in.  At least we didn't get skunk. See you in the next one. Bye. Bye


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