So welcome back, also on YouTube and on Moodle for the people who can't watch it directly. I will put the answers online and I will also add some more description to it so that you guys can look through it. Just relax and make sure that you understand what's going on. But there's two ways of doing this whole analysis. The first way is just to make one big linear model and put everything in. The other way is to first adjust one of the effects out of your data and then test the remaining factor to see if it still has an effect. Alright, so let's switch to the PowerPoint. So we did the assignments. Next up is a slide in Dutch. The lecture today is based on a tutorial by Bodo Winter. He has two tutorials. He has a tutorial on linear mixed models and a tutorial on standard linear models. So that's his first tutorial. And both tutorials are worth reading through. So that was it and I will do it in English as well. The lecture is an adaptation of the introduction by Bodo Winter. He has a very, very excellent tutorial. So it's worth reading the whole tutorial. The tutorial actually is not like something that you can just skip because the PDF is 22 pages. So I want you guys, and this is the homework for today, to read all of the 22 pages and understand what's going on. But for you guys, I compressed everything into 29 slides. So just going through the highlights, but read the PDF. There will be at least one question about the PDF on the exam. Ask questions. As always, if you have any questions, then just put them in chat and say, stop, I don't understand this. And then we will explain. Since I'm having holidays, there will be no Tuesday meeting next week. So sorry, Daniel. I know how much you like our one hour chatting when no one else shows up, but we can't do that next week. So we'll have to figure out how to do that. But I don't think that there will be many questions this week because the PDF is really, really good and it's really in depth. So linear mixed effect analysis, right? So for today, we will start about, we will talk about linear mixed effect analysis. We will do, and I will explain to you what random effects are. I will show you how to do the main R and how to get the significance out of these models. And when we are talking about random effects, we're talking about two different types of models. So we are talking about random intercept models or we're talking about random slope models. And I will try to explain to you guys what the difference is and when to use which one of these models. So basically, linear models are modeling a relationship, right? So you have your response and we have a certain predictor or a set of predictors. So the tutorial of Bodo Winter focuses on pitch, also called frequency. So pitch is kind of what you do with your voice. So you can have a very low voice or you can have a very high voice and that's the pitch, right? So when you do a recording, then based on the recording, you can see if someone has a very low voice or if they have a very high voice. So the data set is here and you can directly load it in R. It's one of the nice things about R. You can directly load data from an URL. So you don't have to download the data set. It's not a very big data set, so you could just download it. So again, the high voice, please. No, I'm not going to do that. But that's the difference, right? So there's a pitch that you normally speak on and you can speak on a higher pitch. I would like to redeem 5000 points if you do the next slide in a high pitch. No, I already did it a couple of times, so we're not going to do this anymore. But it's funny, right? But the whole paper, the whole PDF, 22 pages focuses on the relationship between pitch and different covariates or different predictors that they have, right? I'll add another 5K for low voice. You're already using my low voice, because it sounds more manly when you use your low voice. But the data set is structured like this, right? So I downloaded the data set, I opened up in Notepad++ and you see that there's something called Subject, which is just a person, because they're just interviewing different people. Of course, a person has gender, which is of course a misnomer, because it should be sex. Sex is your biological thing, right? You can only be male or female, but talking about gender, there are many different levels. So there's a scenario and in the data set that they are talking about, different scenarios are different questions that they have people do, right? So the idea is that someone comes into a room and then they get a list of questions and then they have to say these questions in two different settings, and that's called the attitude. So the attitude is you do the questions in a very polite setting, like everyone's wearing a suit and you have to sit in a table and these kinds of things, and there's a very informal. So Paul means the very polite setting, and then you've in, which is the informal setting, so that just means you walk into a room and you read the question, and there's only like one person around, but this person is friendly or you know it. And then of course we have the frequency, which is the response variable, which is the pitch of the voice. So very basic data set, it's very small, but it's a very interesting data set to model, because as you can see already, we have female one who did question number one, but we have the same female one who does all of the different questions in different settings with different pitches, so there is repeated measurements in here. So we need to use linear mixed models to deal with the fact that there are repeated measurements. So the first linear model that they introduced is just a basic linear model, right? So the hypothesis of Winter in Gravunder in 2012, they talk about this more explicitly in the PDF, is that the frequency, so the tone of your voice is dependent on the setting that you are in. So if you are speaking to the examination board, your voice will be different than when you're speaking to your friends. That was the hypothesis, right? So the attitude in this case was treated as a categorical factor with two different levels. So formal setting versus an informal setting, and the idea was is that this setting will change your frequency. Of course we have to extend this linear model, right? Because we have males and we have females and everyone knows that males on average have a much lower voice than females, so of course we have to include the gender into our model.  But of course now things get a little bit more complicated because in the way that they did their modeling, they have multiple measurements per subject, right? They don't have just one question, they have multiple questions. So you have the same person going into a formal setting, then you have the same person going into an informal setting and then having to ask a couple of questions. So by design or the way that they set up their study, there are multiple measurements per subject and that means that we cannot use a standard basic linear model. We have to use a different type of model because there is a grouping factor in there. So the random effects come in. So every person has a slightly different voice pitch and this is going to be a factor that affects all responses from the same person, thus rendering the different responses interdependent rather than independent. And this is just because if I'm speaking, my voice is different than someone else's voice. So if someone else's voice is, for example, 233 hertz average, my voice could be a little bit lower, like it could be 210, right? And this is the example that they give. So subject one may have a mean voice pitch of 233 while subject two may have 210, right? And this is very logical. I think that everyone understands this setting is that different people have different voices and you can't just compare the voice of one person to the voice of another person. You have to compensate for this kind of effect, which is a random or which is an interdependent effect. It's not an independent effect, right? Because the same person has the same voice and you can't really change that. So there is something which is user or which is person specific. And of course, when we plot the data, so the thing that I did here is to just take the different subjects. So we have nine females that participated in the study. We have seven males. And then here we see the mean pitch. So the mean pitch is just the average frequency or box plot of all the frequencies that were measured for all of the different individuals. And so we can see that there's the females, which on average have a much higher voice or a much higher pitch than the males. You can see that there's this one outlier where one of the females had like a voice which was more or less a male voice and also that occurs, right? Because if you're doing research and you have people coming in on different days, doing different research studies, then it's not always going to be exactly constant. And this is just the day at which someone had a cold, so their voice was much lower. But you can see very clearly that females have a higher voice than males. But what you can also see is that even for males, they don't have the same average pitch. So male one has a much higher voice than, for example, male five. So there's a big difference there. And of course, we need to compensate for this in our model when we are doing these kinds of models. So we need to extend our model. So we model the individual differences by assuming different intercepts for each individual. So we talked about intercepts. So an intercept is where X is zero at which point does the line cross Y, right? It's kind of the mean of the data set for a certain individual. So you can do this in R by building up a model which looks like this. So you're saying that the frequency is based on the attitude. So the setting that we're in, formal or informal, it's based on if you're a male or a female. And there is a subject specific component. So I say one by subject, right? So the first part is the slope and the second part is the intercept. So I'm saying that every individual has the same slope, so the same kind of directional coefficient. But the average speaking voice of every person is allowed to be different. So the first thing that the model will start doing is calculate the mean for each subject and then it will then do all of the other effects relative to the mean of that subject. And this is more or less how you would write it down as well when you are writing a scientific paper. The study is a little bit more complex, right? Because we have different questions. So similar to the case of by subject variation, we also expect by item variation, right? The same question for different people, it might be that the same question has a difference in pitch as well, right? It could be based on the amount of consonants in there or the amount of like O's or A's, right? Because an I is of course much higher than an A in a way, right? So Heather might be something special about excuse me for coming too late, leading to an overall higher pitch compared to asking for a favor. Because the A and the O are relatively low letters, but the I is a relatively high letter, right? And this is of course irregardless of the influence of politeness. And this might not be due to which setting you are in, but this is just due to the fact that every question has different letters in there and these different letters all carry their own pitch. So the average pitch across the whole utterance is probably different from a voice thing like excuse me for coming too late compared to asking for a favor. And when we plot this, so here we see the different items which are the different questions. So they ask every person seven different questions and you can indeed see that even if we regard the fact that there are males and females and that everyone has a different voice, you can already see that for example question number two on average has a much higher pitch than question number seven. So there is not just per individual variation, but there is also per question variation. Is that clear that there are two sources of like variance which we kind of want to control for and we're not really interested in? Because these two things are more or less nuisance variables, right? We talked about nuisance variables and generally you want to kind of block these out or you want to randomize them away. But if you do a study like this where people just come in, there is no real randomization that you can do and there's also no blocking that you can do to kind of get rid of this effect. So you just have to compensate for this effect in the model. So how do you do that? Well, we just account for this thing in the model. So we say that the frequency of your voice is based on the attitude that you are speaking in. So a formal setting and a polite setting, your gender, of course every person is allowed to have a different mean voice, but every question is also allowed to be having a different mean compared to other questions. Alright, so this is then the more or less extended model that we have for the data set that was provided. So in R, there is no default support for linear mixed models. R has support for linear models, but linear mixed models are not supported by default. So you need to install the LME4 package, so the linear mixed effect package, version 4, which is the current version, might be that soon there will be a version 5, but this package provides a single function, and this function is called LME-R for linear mixed effects regression, and this function is very similar to the LME function. So if you know how the LME function works, you can just use the LME-R function. So how do we now build up this model in R? Well, first thing that we need to do is we need to load the LME4 library. You have to install it as well. If you haven't got it installed, then you have to do an install.packages LME4, but if you have it installed because you only have to install a package once, then you can just use library LME4. So the first thing that we're going to do is just read the data set online and put this into a variable called politeness. So that just has the data compared to what I just showed you. And then we want to boxplot, right? So the boxplot that I just showed you, which is in the gender, is one of the standards. If you get a data set, do some plots to see how the data looks like. So the first thing that I wanted to do was just do the basic model right where I say that the frequency of the data set is dependent on the attitude, so on the setting and based on your gender. But if I use the LME-R function, it will tell me that I cannot do that because the LME-R function is only for linear mixed effects models. So you have to have a random effect term in your model before you can use the LME-R function. So if you're ever in a situation and you see like no random effect term, then that means that you just forgot to specify it. So we can specify the random effects here, so we can say one by subject and one by scenario. So then we just say LME-R, the frequency is determined by the attitude plus the gender of the person, and then we have the personal specific variance, so the variance where for every individual, we allow every individual to have a unique voice and we allow every question to also have a unique frequency. And of course I specify that use the data set politeness, which I just loaded in, and then I'm just going to make a summary of the whole model that we have. So if we do this in R, then this is how it looks like. So it tells me that we have a linear mixed effect, a linear mixed model, fit by a reduced maximum likelihood, by a remel, and then it gives me back the formula. I left out sex, because with sex it's a little bit different, but I just left it out here just to have a slightly shorter, otherwise it wouldn't fit on a single slide. So the reduced maximum likelihood converts that this number, which is not that important, it shows you the residuals, which is also not important. The thing which is important here is the fixed effect, right? So we can see that for the random effects, random effects cause some variance, so you can see that the scenario catches around 219 points of frequency, while the subject catches around 4000. So you see that there's a big difference between different people and that the difference between questions is not that big. It's only like 1 in 20, but we already saw this from the box plots as well. It will tell you now the number of observations, and it will tell you the groups. And this is relatively important, because the number of observations is the amount of degrees of freedom that you have in your model. So in total we have 83 observations, but these 83 observations are grouped into 7 scenarios and 6 subjects. So we're only looking at a single sex, right? Because I left sex out of the model, I'm only looking at a single sex, and I'm looking at the males, because we have 6 males and 7 females. And here it will now tell you the fixed effects. So again, very similar to a standard linear model, it will give you the estimate of the beta coefficient, so it will give you the intercept, right? So that's the average voice after correcting for the individual and the question. And then we have attitude poll, so this is attitude and then the polite setting, and what it shows us that in the polite setting, your voice seems to be 19.6 or 19.7 hertz lower compared to the informal setting. So that means that when you're speaking to something like the exam committee, your voice tends to be lower than when you are talking to your friends. And then it also shows us the correlation of the fixed effects, which will become interesting when you have many of these different fixed effects, because it will show you how much correlation there is, which is a measurement of how linearly related the different fixed effects are. But in this case, there's almost no correlation between the attitude and the intercept. So how do we now get the significance, right? Because the summary here, it only tells us the T value, it tells us the standard error and the estimate, but it doesn't give us any probabilities. And this is very different from linear mixed effect models compared to linear models. In linear models, because there is no random effects, we can directly use our T statistic to go to a P value, like we saw before, right? Because we have the standard error, we have the estimate, and so we can calculate the critical value, and using the critical value, we can find the probability, so the P value that belongs to this effect. But this is not how it works in linear mixed effect models. In linear mixed effect models, you are always doing model comparison. So you're comparing one model versus another model, right? So if we want to get the significance, we have to define a null model. This is the model without the factor that we are interested in, and then we compare this to a model which has the factor of interest, right? So here I see my L, M, E, R, which is gender, and then the other, and the two random effects, and I want to compare this to the model which has attitude in there, right? Because the difference in these two models is going to tell me how significant the effect of attitude is on the frequency. Another thing that you have to remember is when you are comparing two models, you have to set the restricted maximum likelihood to false. So restricted maximum likelihood is different from maximum likelihood in the fact that it only estimates the total variance explained once, and maximum likelihood will do that multiple times. It's not that interesting. Actually, if you forget to set the remel to false and you do the model comparison between the two models, then actually it will automatically rerun your model, setting restricted maximum likelihood to not be used, to use real maximum likelihood. And this is just because of the fact that by not re-estimating the variance, you get an overestimate of the significance, so you have to set maximum likelihood. So you have to use maximum likelihood. But if you only want the effect of attitude on the model, why don't you do it without the other independent factors? Well, we want to get the effect of the attitude, but of course we do have to correct for the fact that we have males and females. And we do have to correct for the fact that different individuals have different speaking voices. So if I'm only interested in the attitude effect, I cannot just do a model saying frequency by attitude, because that doesn't compensate for the fact that the same individuals measured multiple times. And it doesn't correct for the fact that the same question is measured multiple times across different individuals. So I have to put in all of the factors that are nuisance variables, so that cause variance in which I'm not interested in, so things like gender and subject and scenario. And then I compare this model, which has the thing of interest, so my variable of interest, to a model which does not have that. So in linear mixed effect models, you are always comparing models. So it's more or less always a model selection problem. So when I have these two models defined, how do I now get the significance? Well, the way that I get the significance is now just doing an ANOVA test. So I'm saying do an ANOVA, take my null model, and then compare it to the politeness model. So now what it will do, it will do the ANOVA test, and it will give you the AICs. So it will do a model comparison. It will tell you that the null model takes 5 degrees of freedom. The full model, which has the attitude included, takes 6 degrees of freedom. You see that the AIC drops down from 816 to 807, and you can see that there's a significant improvement when you add attitude to the model. So we learned two things. One thing is we learned that when you are in a polite setting, your voice changes by around 19 hertz downwards, and this change has a relatively big significance of 6 times 10 to the minus 4. So 0.0036. So really, really significant. All right, so now we compare these two models, right? We know now what the effect of attitude is, based on the summary. We then also know what the significance is, based on the fact that we compare our model with the model, which does not have the scenario, but the attitude in there. So now, of course, we kind of have everything that we need. So if we would write a scientific paper, then we could now write more or less our conclusion. So the conclusion in this case in your publication would be that politeness affects pitch. So you mentioned now that this has been tested by a chi-square test, which has one degree of freedom, right? Because the one degree of freedom is the difference between the degree of freedom of the null model and the model that you're looking at. It will also tell you here, right? So the df of the chi-square is 1. We have a chi-square value of 11.6, right? So the chi-square value, one degree of freedom, 11.6, which corresponds to a p-value of 6.5, type 10 to the minus 4, or 0.00065, lowering it by around 9.7 hertz, plus minus 5.6, right? And that is just the standard error that we can also get from the summary. So that is kind of how you do linear mixed effect models, right? So if you have repeated measurements, you have to deal with these repeated measurements, otherwise you are massively overestimating your effect, right? Because if I would have two individuals measured and they would have a difference of something, right? Then the significance of this is not very high, because I only measure two individuals. But if I now would measure the same individual 100 times, and I would measure the second individual also 100 times, then now all of a sudden in a linear model, it would see it as 100 measurements versus 100 measurements. But this is of course not true, because you only have measured one individual versus one other individual. So the significance doesn't change based on the number of measurements that you do. So the power of linear modeling and of regression comes from the fact that you have multiple independent measurements. And of course having 100 measurements of the same individual do not make a more significant effect. All right, so random slopes versus random intercepts. So a random slope versus a random intercept. So you see that each scenario and each subject is assigned a different intercept. If we look at the model that we get, we see that for each scenario, so the seven different scenarios that we have, we have every scenario has a different intercept. Every subject also has a different intercept. But we see that this 19.7, so the influence on the attitude, the politeness attitude, is always estimated as being minus 19.7.  That is what you would expect when you give this model the one-by-subject and the one-by-scenario, because it takes them into account the by-subject and the by-item variability. However, it might be that we are expected, it might be that there is a relationship, right? That based on the scenario, so based on the question, the politeness attitude might actually change, which is a relatively normal assumption saying that, well, the politeness might be influenced by the question that you have to pose, right? Some questions might not have a massive difference, while other questions might have a much bigger difference, right? So then you are talking about a random slope model. In this case, we're just doing random intercepts. So we say that every individual is allowed to have their own intercept, every scenario is allowed to have their own intercept, and of course, the effect of politeness is equal across the different scenarios and is equal across the different individuals that we are looking at. So a random intercept model means that the fixed effects, attitude and gender, are the same for all subjects and all items, because our model is a random intercept model. In this model, we account for baseline differences in pitch, but we assume that whatever the effect of politeness is, it's going to be the same for all individuals and all items. However, in a random slope model, it might be expected that some people are more polite or less, right? So that the politeness has a bigger effect on the frequency, and for example, you can imagine that if you are someone who has a very high voice, you might drop more than 19 points when you are in a polite sitting, while someone who has a very low voice might drop only like 7 hertz down, while your voice is already low. So what we then need is a random slope model, where subjects and items are not allowed to have different intercepts, but where they are also allowed to have different slopes, so different beta coefficients for the different scenarios, so for the different politeness levels. So how do we do a random slope model? Well, if we do a random slope model, we write it down like this in R, right? We say that we have the frequency, which is the thing that we want to predict, is based on the attitude, it's based on the gender, and now for every subject, we allow every subject to have an intercept, but we also allow every subject to have its own attitude, right? So subject one can have a drop of like 25 hertz, subject two might have a drop of only 10 hertz. And of course we also allow this, we also do this attitude by scenario, right? So if we look at this way, so we also do a one plus, and that just has to do with the fact that the way that the model is specified in R, but the notation one plus attitude, dependent on subject, means that you tell the model to expect different baseline levels of frequency, the intercept represented by one, as well as a differing response to the main factor in question, which is our attitude. All right, so if you want to learn much more about random slopes and random intercept models, then read the tutorial. The tutorial is part of the exam, so you will have to read it, otherwise you will fail at least one or two exam questions. But today I told you about random effects, so random effects are effects which are more or less repeated measurements.  For the assignment, I want you guys to read the tutorial and of course practice, practice, practice, practice, because the only way that you will be a good programmer is if you spend the time on answering programming questions. All right, so any questions so far, and I would bet there are some, but we'll see. So, I have another presentation for you guys lined up, which is not really part, but I just wanted to show you guys what I have been doing with random slope and random intercept models recently, just to make it a little bit more practical, right? Because, like, this is based on frequency and pitch, and I hope I explained everything well, that people have different voices and that the voices are also different based on the questions, because questions just have different letters, and every letter is a little bit different in pitch as well, and that you can assume things like, well, it might be that the politeness is just a single influence, or that politeness might be different based on which individual is speaking, but I also have kind of a more or less similar example, which I also wanted to show you guys, that the BodoWinter tutorial is definitely worth it. Also, check out this tutorial number one. Tutorial number one is just about linear models, and then tutorial number two, which you have to read for the assignments today, is based on the linear mixed effect models. All right, so just a shout out in chat. Do you have the idea that you kind of know now what the difference is between a standard linear model and a mixed linear mixed model? So the difference between an LM and an LME or an LMM, depending on how you shorten it? Yes. Okay, that's the first one. Okay, then I achieved part of my goal. Testesartos says no. Okay, so what don't you understand? Another yes. I do think it's a little bit sad, test though, that you don't have the diamond anymore.  At least you have your diamond back then. I don't want that.  You just completely desubscribed after I muted you for five to ten minutes. I don't understand why it is different. Isn't it only a more precisely linear model? No, no. Because if you are so imagine a case where I have measured three big mice and I've measured three small mice. Now I can do a t-test between these two groups. But if I would start measuring the big mice over and over and over again then it would look like I have a whole bunch of data. It would look like I have a lot of power to detect effects. But it is actually not true because the power comes from independent measurements. And if measurements are not independent you cannot treat them as such. Because then your p-value will artificially be lower and lower and lower. A linear model has no concept of things that might be measured on the same individual. But a linear mixed effect model you can tell it that this is the same individual after 10 times. And then when it calculates the p-value it will not treat this as 10 independent measurement but it will treat it as a single measurement. So that is the big difference between linear models and linear mixed models. Because a linear model cannot deal with repeated measurements while a linear mixed effect model can deal with these differences. So that is the main difference between these two things. And then we will have another break and then after the break I will show you another example. And then we will go a little bit more in detail and it will be again based on our own Berlin Fetmau which I am always trying to promote. So it will do kind of the same lecture again but now on data that I collected myself and oh, an anonymous cheerer. Thank you for the cheer. Thank you for the one bit. So good. So Topias was the last one, right? So the break now will then be insects which is the one which you already saw last time but then we only had two hours so we didn't really have the break screen. So I will see you guys back at like 4.05 So in a 10 minute break I will stop the recording


the discipline of education. KK Handic State Open University. Priyohik Kharti Khokal. Ajir Alusonaloy. Apunalukak swagatam janaisu. Aru Aziyami. Snatak Pratham Hanmahikar. Block 2. 12 number Uniter. Jitu Unitasil Gandhi and his educational thought. Mohatma Gandhi Hrithir. Abodan. Tar Hrithyongkhok. Itimayche Ami Prathamongkhok. Gandhir Porisoy. Teor Khikkhardharana. Dithyongkhok basic education. Babunyadi Khikkhak. Aru Paikchokram. Bunyadi Khikkhak Paikchokram. E homporki Ami Alusonakoriru. So the presentation outline of today's discussion is method of teaching prescribed by Gandhi. Secondly, free and compulsory education. Third one, religious and moral education. And the last one, women education. Orthat. Jihikkhonar padthoti homo. Gandhije khikkhat antar bhukto parar kothakpoisil. He khikkhon padthoti homo harbihoye. Binamuliyaru baikthatamulok khikkharbihoye. Thormyo khikkharbihoye. Aru. Gandhije matbukkhon kora jih women education mohi la khikkha. Tharuparat Ami Ajir Alusonak ek alukpa kori. He khikkharti hokal. Ahok Ami Ajir Alusonak arambhokoru. Protshoma jih Ami Ahim. Method of teaching prescribed by Mahatma Gandhi. Jih Mahatma Gandhi yongji khikkhardharana diesel. Khe dharana homo atmi iti mo ithe janilu. Aru khe dharana homo bus stop khetcha trupanchori to kori bo ar karane. Ati koi gurutcha punna kathatu hoise, anghatu hoise khikkhar je khikkhon padthoti. Ki padthoti khikale, khikkharti hokale, tyao dibobisora hokalugur ayatcha kori bo. Aru khikkhar bhalfal Ami aha kori bo pari. Taar karane, Mahatma Gandhi has prescribed some methods of teaching. The first method was correlation. Artha, khoha humbandha. According to the views of Gandhi, knowledge should be correlated with craft. Je Gandhi's khoha humbandhar niti puhokata kori sila. Aru tyao byakcha kori sil, je manuhar gyaana haran ek punu hilpa lagat jodi humpokita hai, tetya e jan manuhi ati khunkale. Aru bebohar rupajugi sram buddhidiptaar pradharhananar jugedi. Taar he gyaana haran kori bo loe, khohas paai. Aru khe karane, taar koi sil je ek bhara khain mo punna praikai horir manau atmar khustha bika hi hoise hi kha. Aru e hi kha, basto khetrot rupayon karar karane, tyao je niti puokata koi sil, khe niti hoise, khoha humbandhar niti ba ingrasip koi the correlation method of teaching. Teo ek je asoni rupayon kori sil, khoha humbandhar niti khala prakho kori par karane, the scheme was known as nahi talim, nahi talim buli e asoni aikha diya koi sil. Taar basate, the next one, the method of teaching, that was given by Gandhi, teaching through craft, kutir hil paar dara, Gandhi opend at, handicraft is not only a productive work, but also develops the intellect of the students. Taar mane, je ami bika hor ba be, kutir hil paar hi khaar maitha hum, hi sape je tya Gandhi je, tyao grahan kori sil, tyao koi sil je, e hi khaar dara, akol utpadan hil khoma tai britti karana hoi, taar juge di, hi khaar ti ho kolaar, ji manu hi khaar ti, taar u bika hum ba bhava.  Therefore, he prescribed the most important method of teaching that was teaching through prout, or taat, kutir hil paar dara hi kha. The last one, methods, he prescribed that learning by doing, jitu aajir, amar hi kha be basatabto, manu boi kha ni kha kole, hei, karma dara hi kha, learning by doing, in itir upara gurukto diyaar, such education is neither derived nor depends on books, it is related with our practical life, or taat, ji learning by doing karma, jukeri hi kha, e hi khae, jibaanar, bebinna, prakhaar, amar situasional, bebinna, dihaat, amar khohai kore, karon e hi kha, ke tiau okal, bukish naales, or taat ji, amar hi kha, aajir hi kha be basatabto, amar kene ko, odhikaan khoise, kitapar machote abastha, ami kitap parhi su, parhi kha di su, bhaal paal laf parhi su, hadharan manu hei, hadharan te, heitu ke hi kha buli, bhabe, obihita kore, kendo hi kha hoise, che horbo to di hor di ka, ami jodi, hi khi to, amar ho kalu di hor, bhi kha, hobo lagi bo, manu hi sape, amar mental, emotional, social, every kind of development should be possible to education, then we can say this type of education as true education, taar karone, hei katha upolabthi kori mahatma gande, koi seelche, hi kha ke tiau puthi kato, gyanar parate, parhi hobo na lage, aru, jibonar, paikto puthi, parahe ami, hi kha labh koro, jodh kuno arthaar praejaan nahi, jag, bakti pra kunu hokti e karhi lobon warne, hei karone, on the basis of practical life, gandhi, press pride to provide education to the indian child, ekini asile, matter of teaching, che kini, gandhi e disile, hi khanar puthi khamo har bhi khe, taar pasote, anek, kurutya puno, jeti, ami, gandhi hi kha bebusthabar uti, anek, kurutya puno, katha, ami, jani bolagi bo, che free and compulsory education, midamulio aru, baikto tamulok hi kha, arthaar, bharat borhar, baikto tamulok hi kha ji, iti o amar, batta manabostha to amar, ase, hei, hi khaar, oninotom, ek prabokta, hi sape, ami, mohat ma gandhi, abhi hito koribao paru, because recommended to give free and compulsory education, 7 to 14 years, through his basic education policy, je, teo, basic education, baabuniyadi hi kha, ji, ami, palu agar alusana, tar jughe di, khator pras, uttobasar, baikto tamulok hi kha, di bolagi, je, teo, midamulio batta tamulok hi kha, di bolagi, te, hei, amar, bharat borhar, arthaar, arthaar, ami, sa tu, jathes to, pari mane, yar, rabhab, ase buli ami ko, baparu, arthao, koi seilze, khikha nusthan, hamu, hok, pro-hikha nusthan alo, unita kara, aru, khikha karu, jate, pro-hikha pradan, koribao le, paajukto pari, baikto li, bapara, jai, taru paro, kurutto di sile. aru, rastroy, khikha, khakalu, im vo pros hot k capture tareping threw,and mohatma-gandhe, mohat bukhan, korisil, ji, ek, tongi, he, khikha karonar, aitthar, ek, grucha pulna onghte sepe, ami, Paregani char hidden paru free, and compulsory付ugación. tarpa sar teo an, ek religious & moral education arthaad, darliar, naitikhe kar upo,ryoaisya brifara, teo gruchto di sile. teo khodari gandatya tag, homashewa, oh-imarkha, bahama-sartcha, who have the education and and education. Women empowerment, women education so this is the burning topic in the society. According to Gandhi man and women our complimentary నవ్నికోలి ప్ఠ నారలూధారి bound休 the Bustopела alignment. so thank you students so, again, we'll meet in a next discussion with a new topic and hope you'll be benefit by watching my video


Welcome to Inventing Our Future and Think Ta Kauai. I'm your host, Brittany Zimmerman. Hi, and I'm your supposed to be co-host, Richard Ha. Wonderful. And today we are going to do a deep dive into our D category. So how are you doing, Richard? Oh, pretty good. Nice day, Sora. Wonderful. Good, good, good. Yeah, so Richard is going to help us on our category this week. I'm really excited about it because our D category is digging into dirt. So we are gonna have a deeper conversation about some of the things that Richard Ha does with dirt. And I think Richard has done a thing or two with dirt before. Is that right, Richard? Yeah, various different kinds of dirt and rocks and all kinds of stuff. Awesome. So we have the pleasure of getting Richard to have a long conversation with us today about banana farming in particular, which I'm really excited about. I know very little about banana farming. I've been to a single banana plantation area in Panama one time, and that is the extent of my entire banana-knowing knowledge.  How does that sound? Oh, that sounds good. Yeah. That's great. Awesome. First, what got you into banana farming, Richard? Where's the banana connection? Well, you know, I went to a U.H. and I flunked out of school and I went to Vietnam. Then when I went to Vietnam, I became an officer and we had this relationship where we took care of everybody and that lasted for the longest time. But when I came out of the Army, I decided that I wanted to go into some kind of business. So I decided that I would major in accounting because not the way to green visor, but to keep score. So I had a, and that was the purpose. So I had this accounting degree. And when I first came back, my dad asked me if I would help them market the eggs from the egg quail, which was going on at that time. So I became the core manager and I started interacting with the supermarkets and stuff like that. And then at that time, Chiquita started sending bananas into Hawaii and I was noticing that and saying, you know, gee, we can do that.  So I started collecting banana boxes and just stashing it under the house. And little by little, you know, we started looking into it and say, gee, maybe, why don't we see if we can get up to scale because they're sending in a lot of Chiquita bananas. So that's how it started. You know, we got interested and then we, we were operating off the Wakioka chicken farm. And there was 20 acres on the side that was not being used.  And so we started, okay, let's figure out how we're going to do this and no equipment now, nothing except my Toyota Land Cruiser. So what we did was we started looking around for banana kiki and we got some and then we had to start planting and deciding how we're going to do this. So the way we did it was we took the Land Cruiser and drove it to mark the lines, you know, knock the California grass and then we sickles cut a hole, you know, and we didn't even notice spacing, but we did that and then we stuck it in the ground and then we just went to growth. So just waiting around, you know, punching bag, lifting weights, all this kind of stuff as time went on, but that's how we started.  We didn't even need, that's how much we knew. We didn't know very much. But nevertheless, we started, you know, it started growing and then just being resourceful, so we had to control the grass. So back then what you use was called Paracoa. There's a scull and crossbones kind of pesticide. Oh, yeah, yeah, yeah.  Right.  Anyway, so. That's a really good PPE right there. Yeah, yeah, I'll protect you, I'll hold your breath. Yeah, yeah, absolutely. And so that became second nature, you know, I knew what you got to do, yeah? So anyway, we started growing and then little by little we got some bananas to sell. So the way we would do it is with harvest, cut it up into pieces of hands and put it on where we used to have a chicken wire. And then we'd select the hands that were ripe to put in the box to send it down to where we were selling eggs to, in this particular case it was food fair supermarket. So we were doing that for a while. And little by little, you know, we started to realize, you know, in Central America, they actually use what they call banana gas to ripen the bananas. So yeah, because we, you know, I was a co-op manager, yeah, of the egg co-op. And so that's how I knew all these people. But they wouldn't talk to me when we were selling eggs but when we're talking bananas, now all of a sudden they actually talk to me. So when I was over there bringing the bananas, they'd say, gee, something wrong with these bananas.  And I would go, oh, I don't know, we'll see what we can do and stuff like that. And I felt like a kid going to the principal all the time getting scolded for the bananas. So that's when I started looking into it and saying banana gas, okay. So we went out to gas pool and asked them, you know, do you have this special gas that they use for banana? And the lady told me, are you looking for banana gas? I said, yeah, that's what I wanted. So I had no idea what it looked like. So we brought it back and then you have to put it in. They actually had it. They had it on Island, the banana gas. Yes, yes, they did and nobody was using it. They just, they had it. Yeah, so we brought it home and then we know that you'd have to, you know, release it into a room to get the bananas to ripen. So we turned it on and holy smokes, it worked. So then we realized that you needed to have some kind of refrigeration to, and we didn't know why, but you know, so we got a little air conditioner and we started ripening the bananas. And one day we opened the door and the whole room, the air conditioner had frozen up because there was so much heat that it overpowered the ability of the air conditioner to handle. And it was, you know, we lost 25 cases. We were tiny, you know, we were small operators at the end. But then that's what it set us on this course. Oh, okay, so now what that means is we got to have insulation, we got to have the proper sizing, et cetera, et cetera. So we, and that's how we started. And from there we went to a couple. And because my dad's friend was a fireman and they had some extra land and stuff like that and they were growing papayas on it. And that was where the syndicons, if you go down there, the syndicons is down there and it's just where the pavement ends coming back toward a hill. So we started planting there and they had papayas growing. So they ripped the papaya, they ripped the lava and we planted it in the rips because it was real dry, yeah? And, but it actually worked. So we stayed there for a while, but as time went on, it was all hand labor, you know? And, but before that, I learned about when we started getting banana cake, we got it from Akusani Kamele down at our Maku property. He was noted for having the best watermelon. And I used to go over there to learn and get the banana cake from him, but mostly to learn, yeah, to see what he was doing. So what he did was, you know when the watermelon was like golf boss size, he would put a stick in the ground and see, you know, right down when it was that size. And then from there, he decided when it was time to harvest, not only visual. You know, like something to say, you knock on it and then it'll tell you whether it's ripe or not. He didn't do it like that. He timed it and he associated the different seasons longer during the winter, shorter than the summer. So we adapted that to bananas and how we did that was we put, we had to put bags on to protect the bananas, especially from, you know, the cinder was going off and anyway, we had to protect the bananas. And then we also had to determine when was the right time to harvest?  You end up a lot this week and nothing next week. So what we did was we adapted what Akusani did. We put on colored ribbons, colored ribbons for each week. So during the winter, we let it go 18 weeks. During the summer, we go less than 16 weeks, you know, just by one primary color and two secondary colors. You know, so we had a... And why, what that did was it made it so you could see when you needed to harvest. If this week was already, then you only pull the ribs, you know. Oh, interesting. So what's the equivalent of you said you would mark it by time, right? When the watermelon was the size of a golf ball, when would you start marking the bananas? Is it right when? When you put the bag on. Oh. And then you tie the ribbon to the bottom of the bag. Then you don't have to go close to look at the bunch. All you gotta do is be far enough away to see the color. Oh, so you have a lot of time walking on the lava road. Yeah, yeah, yeah, yeah. So it would be really, really a practical, yeah? Yeah, yeah. Oh, wow, that's really cool. That's a really great example of adapting knowledge from other types of agricultural practices and finding ways of utilizing that, right? And molding that into bananas. It's like a watermelon to banana. Yeah, and that's how it transformed the banana industry because before that, there wasn't an organized way to do this. Yeah. Yeah, so we transformed the banana industry. So another thing we did was going from there at Kapuho to coming into Kao and into, you know, Kapuho was all rocks here with cinder. Yeah. And then when you get to Kao, it's soil mixed in with the rocks, but primarily rocks. And then when you go to Kao, it's all soil. It's totally, totally different. So, you know, so when we moved into Kao, it was almost the same, you know, just kind of rocky and we did the same sort of thing. Yeah. But when we moved to Pipikil, now all of a sudden there's deep soil and it's muddy and stuff like that. And so we had to figure out how, and we never had that problem while we were on the rocks.  So how do you fix that? So what we did was, and we used to plant, you know, 250 banana plants per acre.  So that was the population. So we decided, okay, what if we went 750 per acre, but in a straight line, 700 individual plants. And that way you could mow the aisles. And that's where you could get traction. So that worked, you know, so we adapted again because we had to, yeah. But what was really interesting about that was think about this now. Instead of 1,000 bunches, you get 750 bunches, but the same amount of production is not more per acre. So the labor per bunch is much less because now you're only dealing with 750, bagging, you know, all this other stuff. So we did that kind of stuff, it was kind of fun, it became fun, you know. Right. And this was true during the time that I spent looking at some of the banana plantations down in Panama, but I was so surprised to learn that you only get one bunch of bananas per tree. Is that true for all varieties of banana? Yeah, yes. Except for, and this is very unusual, we were doing tissue culture and we had to go do tissue culture because we were, you know, well, we had to go do tissue culture. We made our own tissue culture lab and we started cold, we didn't know anything about it. So we did it. What we didn't know was that after you get the seventh generation, you start to get mutants. And when you start to get mutants, one of the mutants we got was a short apple banana with two bunches on it. Wow. Isn't that something? I mean, I'm just starting to comprehend of what you could do because of the, in other words, you could go find disease resistant plants like they do in Taiwan. Yeah, but it's a diversion. I, you know, you asked me if they were, we just one bunch and it is true. It's just one bunch for a plant. That's so cool. And I know there's a lot of other fruits where you can take the fruit and you can utilize that fruit and actually bury it and it'll grow a new plant fruit. Was that similar with banana?  Not in this particular instance. You know, it wouldn't grow from Cavendish bananas. Yeah, so you would have to get the suckers that's off the side and plant those. Got you. I have a little offshoots that come off the bottom. Those are the things I need. Okay, those are the cakey. Yeah, exactly, exactly. Yeah, I was thinking some of the two biggest issues that they were facing down in Panama with the banana plant patients. Number one was pests. So when they will go to harvest the bananas, they'd have like these massive spiders or snakes that were harming the people who are harvesting it. And I heard there's not too many snakes on the island. Did you guys experience anything similar to that? No, you know, we're really lucky. Even once in a while you get a spider, but you know, that's not a big deal, right? Yeah, it's not poisonous spiders or anything. And we don't have snakes, so we didn't have that problem. However, there was a problem of the banana can catch fungus and stuff like that and deform it a little bit. So what they did in Central America was they had pesticide infused bags. So there's pesticide in the bag. But the problem with that is that it would make, if you did that, your workers would be constantly in contact with the chemicals. So we just decided not to do that as an industry, yeah? So we never did. Yeah, so in any case, just. Yeah, speaking of the chemical side of things, I was seeing also that after you harvest a hand, you know how they have like where it's cut, they would actually have like a chemical that they would dot on the end of where the banana stem was cut. And I think in order to keep it fresh or something along those lines, the tour was done in Spanish. So bear with me here on translation. But is that something that you guys, how do you keep the banana fresh? Did you guys also use this thing or was there other ways of tackling that? No, we never had to do that. And I noticed that that's what they do for organic bananas that they sell in the market for Central America. But we had to do that, no. Oh, wonderful. All right. And then I heard that there was something funny related to red Flintstone on your banana farm. Is there a story behind that? Yeah, you know, when we're in Kabul, everything was done by hand. And so we were trying to figure out, okay, how can we make this a more efficient operation? So the first thing we did was we asked my brother-in-law to help us build a structure so that we can do the packing.  So we did get that. Then we needed to get it into refrigerated containers. So how do you get it from there and to fill up the containers? So we, and with no money, yeah? So what we did was we bought a point lift that had one of the tires had a big part of it broken off. So every time the tires spawned, you'd hear, come on, come on, come on. But, you know, it saved the guys a lot of work. Yeah. So it was pretty funny. The French limestone was the name of the tractor. Yeah. Of the forklift? Yeah. Yeah, the forklift, yeah. Could you see their feet underneath? You know, so we were evolving, trying to become more efficient and more efficient with, and in a way it was good, you know, because we didn't have to spend money. We were very, very careful about not spending, overspending because we didn't have the money to start off with. So in other words, we didn't have money to lose, yeah? Yeah. Yeah. And you guys grew a lot, right? I think you were telling me that at some point in time at the height, you guys were producing millions of pounds of bananas, yeah? Yeah. At the height, we were at six million pounds annually. But at that time, of course, we had a ripening room, regular forklifts and trucks and this. And we just made these floors. Prime Flintstone was on vacation. Yeah, yeah.  We had a truck, but it was funny because it leaked and it was old and it was cheap, right?  Oh, wow. Oh, no way. For fun, yeah, you know what I mean. Yeah, yeah, absolutely. And you had mentioned before too that you guys, you guys brought a lot of students, right? You guys really into the education side of things. You guys would host tours and go talk. Can you tell me a little bit about the education piece? Yeah, and we were always wanting to, you know, share with the schools and what we do. So we'd have kids and my daughter, you know, one day was giving this group of kids a tour and they, you know, a bundle of energy. So she's talking to them and she's, okay, now students, which way does the bananas grow? Facing up or facing down? And half the kids would say up, half would say down, then they jumping all around and they didn't know, but they just had an idea. And then at the end of the day, you know, Tracy thought, okay, now students, when your mom and dad goes out shopping and you know, she made the case that the kale banana is the best tasting bananas. Yeah. When they go shopping, what will you tell them? What will you tell them to buy? They all yelled in unison, yellow bananas. And we quit marketing the young kids. As long as it's yellow, we'll buy it. Yeah, yeah, it's real funny. That sounds great. Richard, then I've seen people open bananas and eat bananas in really strange ways. Did you have a preferred way of opening a banana? I think mine was pretty traditional. Now I, my favorite is apple bananas, yeah. So it's the normal way, you know, where the stem is that attaches to the hand. Yeah, yeah. And then they open it up and, but what were you thinking? Did you hear others? Man, I've seen people open bananas in the weirdest ways. A lot of people say like that you open upside down. So I know some people who open it up, they'll like hold the stem at the bottom, almost like a way to hold the banana, right? And then they'll peel from the top. Almost what I would think of as upside down. And then I've seen some people eat bananas in strange ways too, where I wish I had a banana so I could show this, but where you poke in the middle and it always separates into three. Ah, okay, okay, okay. You know what I'm talking about? And then they'll actually pull down the one third, the one third, and then the one third of the banana. I mean, I feel like I'm archaic or something. I mean, I just, pfft. Yeah, yeah, I know. Yeah, I know. Yeah, I know the experience of that. I haven't seen that, but I can see somebody doing that. Yeah, yeah. Awesome. And then you said, you were kind of inspired to do all of this because of the amount of chiquita bananas that you saw being imported. What was the impact on chiquita for you guys getting up and running? Was there still a lot of chiquita being imported? Or did you guys end up taking a lot of the market here in Hawaii? Well, at one point when we were doing six million, we were supplying quite a bit, more than half of the total number. Bananas is a tremendous volume that's sold. Yeah. So we did make an impact, but we didn't have 100% of the market. Yeah, understood. Did chiquita ever call you? No, not really. But when we, there was a time when they were trying to certify the Central American bananas talking about chiquita banana once, an engineer came through that he was going over to the Philippines. And then he called me because we had this certification, equal OK certification. And then I realized what they were trying to do is they were trying to extend the life of the bananas to get at a better taste like Hawaii because we're north of the equator. It's not three longer than in Central America. So the taste is more complex. So they were trying to do that by doing elevation. And I wouldn't say all bananas were better than chiquita bananas before that. But after that, I was sure this is a mention there because that's what I thought. I didn't know the reason though, but that's the reason. Yeah. Got you. Oh no, that's really, really cool. And so I was thinking, I don't know if this would be helpful. How much does the soil and the health of the soil impact the banana, right? The quality of the banana. Yeah, it's huge. The bananas need a high amount of potassium and of course phosphorus, not as much. And they need nitrogen to grow. But they also need acid soil traps some of the nutrients and makes it less available to the plants. So you need not, you don't need acid soil, which we have predominantly on our coastal. So what I'm interested in is trying to see if there's a way we can, how can we do that without bringing in stuff from Kauai High to throw on the soil to change the pH? I don't know, maybe biochar, I just don't know yet. But just keeping that in mind, yeah. Yeah, and I think that might be interesting. How do we feel about doing and digging into dirt part two where we go into the soil science? Yeah. Yeah, I think that'd be super cool. Yeah, we'll bring on one of the soil scientists and we'll do a digging into dirt part two where it looks into. Yeah, what's actually going on in soil? What is soil, right? What's happening in the dirt? And how does that affect our cultivars, our crops and life numbers? Yeah, because if the soil is rich with compost and stuff like that, it makes a huge difference in the size of the bananas and stuff like that. Yeah, yeah, absolutely. OK, let's do that for next time. All right, any other last minute antidotes? There are things that you think we really should know about banana farming. Oh, I can't think of anything right now, although there's a million things that I really can't do. You know what it is, is we've always had to plan from day one, 10 years into the future. You know, it was always OK, where do we want to be 10 years from now, not where we want to be today, 10 years from now. That way, you can make small changes to move toward that objective rather than all of a sudden get hit with it and then you're in big trouble. So we've been doing this forever and it works pretty well and that's the biggest thing I think. OK, yeah, no, that's really exciting. And I think there's a lot of lessons learned that we're going to be able to pull not only from the banana. I think other episodes will get to talk more about how that translated into tomatoes and then indoor agriculture and a lot of stuff that we'll be able to garner from that. So we'll kind of utilize this as a foundation. He is for what we can build on. And I'm really excited about that because I think it fits really, really well with the long-term thinking that we discuss often and that you bring up fairly regularly with the Keoki and Mamia organization. So really excited to learn more. Thank you so much for sharing some of your banana anecdotes and knowledge with us, Richard. It's good fun. All right, then with that, we have to wrap up. We're out of time here. So I wanted to say thank you to everybody. This is Inventing Our Future on Think Tech Hawaii. Thank you to our viewers for watching. If you want to get on our email advisory list to see a complete listing of our shows, you can sign up for them on ThinkTechHawaii.com. And we will be back in two weeks, so please tune in. And we can deep dive into our Digging into Dirt Part 2 on soil sciences. Until then, I'm Brittany Zimmerman. Thank you so much for watching ThinkTechHawaii. If you like what we do, please click the like and subscribe button on YouTube. You can also follow us on Facebook, Instagram, and LinkedIn. Check out our website, thinktechawaii.com. Mahalo.


Good to see I don't know. I've stayed here many times many times It's actually gotten better. There's a updated a lot of been a lot of updates in New Brunswick All these truck stops have really stepped up their game. Good to see her on what few nice days They have out here in northern New Brunswick Must be pretty beautiful out here right on the water. It's just they have so much miserable weather up here like They're just troopers for sticking it out.  I believe they're a group of people French speaking Different from Quebec very nice people up here. Oh, look guys got firewood for days Yikes that guys your guy must get very bad weather coming off this water over here all the time I know they have a a ton of snow here in the winter time a ton Like a four-foot snowfall is nothing that's just that's just another winter day So we're being loaded up right now And you notice the lack of straps and chains. I'm using Diesel all I gotta do is go and close the doors and we're off Feels like we should do more than that right I'll have to make sure the freight is secured. I'll probably put two straps inside the trailer Just to make sure that it doesn't fall back, you know, but Other Nat good to go as soon as we're done here. They've just been like racing in and out of the trailer though Loading me up. I hope that they're not gonna put more weight than they're supposed to in there I'm supposed to make sure that they don't put any more than 42,000 pounds in the trailer because we have a bunch of extra equipment on these trailers for special specialty loads that they're a little heavier than the average drive-in So I have to make sure that usually we can haul 44 to 45,000 pounds But in these trailers, I got to keep it to around 42,000 or lower They were supposed to know that but I guess because of the social distancing virus and everything going on They want to interact with me as little as possible. So I've been trying to get their attention I have to stay in my truck. I'm trying to get their attention to tell them. Hey only 42,000 pounds But they're so busy.  I Wish they would have at least come to talk to me and interact with me a little bit first before starting just to say a little Hello, this is what you're getting How much weight can you put on? And now I just backed up to the dock and they started going This place and kind of a big mud lot. They were actually not even in the office. They're Minimizing their time at the office here. So no one's in the office So when I got here, I had to call a phone number and then they came in to load me This whole virus thing has got everybody so on edge and so scared and so Filled with fear, you know It's gonna take a long time for things to For people's mindset to go back to normal I think because they've been they've been so scared putting to such a big state of fear over this, you know like worldwide and You know Can't blame them for that can't blame them at all But just for everything to for that to get out of people's minds that that may never leave this generation's mind, you know It is what it is We're gonna keep trucking no and hopefully he's not gonna put too much weight on me and have to take something off Hope he comes and talks to me before he parks his forklift at least He's gonna bring me paperwork sooner or later, right? And then I've got to look at the paperwork and I wanted before they print the paperwork off I want to make sure that the weight is right Yeah We'll be okay, at least we don't have to chain it down I paid my dues I guess This is a lot easier Okay, we're golden. I got I got his attention We got to keep our distance, but I can sort of yell at him from a distance or talk loudly and Just let him know hey, I can only take 42,000 pounds apparently And he already knew that and they also have a scale on site here So we're gonna scale myself before leaving the yard Which is awesome because a lot of these places that we come to in northern New Brunswick to load The load is always very heavy and a lot of them don't have scales and the closest scale to here is three hours away So if we're overweight we drive three hours there and then I have to come three hours back I gotta follow him now So they put way too much weight on the drives And it's you can't account for it by sliding the axles forward any further So it's way too much weight in the front of the trailer. So he's got to rearrange the freight But this Truck, you know the only criticism I had of this truck was the transmission right and this sort of ties into that as well The clutch is so jumpy. It's very hard not to slam into their dock Because you just feather the pedal right you just want to slowly ease towards the dock But it wants to grab right away and just wham slow you I've been able to avoid slamming the dock here But I can just it's really hard to get a smooth Smooth reverse, you know, I'll have to get used to it. It's just truck. I'm not used to the transmission. I'm not used to but It's kind of annoying You guys experience that and automatic transmissions before too they just they want to grab and just go a little bit too Hard. It's very hard to just sort of feather it We're all loaded up and locked in Okay Let's go. We're at seventy eight thousand four hundred pounds Gross and we're allowed to be up to eighty thousand pounds gross in the US Of course as soon as I start rolling, that's when our new message arrives It's just how it works, right? You are cleared to cross at Holton, Maine That was fast. Wow. I didn't even left yet. I'll say thanks They know I got the message Let's give her Back here in Salisbury, New Brunswick, and I'm gonna go over the cat scale here's the closest scale to where I loaded other than the Scale at their yard, but honestly that scale was uh, I Want to double check it on a certified scale.  They are amazing truck stops if you come to New Brunswick You're gonna have to stop a one They're in New Brunswick, Nova Scotia and Newfoundland, Labrador. They're like a maritime Truck stop. They're better than Flying J and that like I was saying last time was a huge Huge compliment coming from me because I'm a big fan of Flying J But for me my Flying J fuel card works here the one we use so We're gonna wait for this guy Let him see how big he is first and then I'll get on the scale I'm not going anywhere right now Karen. I don't care what you say. I'm the boss We're gonna check to see how fat we are We gained a lot of weight today Pretty impressive for that old scale back at the at the shipper a diesel I'm allowed 12,000 pounds on my steer axle I'm at 740 we're good. I'm allowed 34,000 on both my drive axles and my trailer axles 33 820 and 33 600 For a total of seventy eight thousand six hundred and thirty pounds gross. That's full of fuel Not bad. Not bad. Who's over there diesel? Who's over there? I Think it's one of those creatures they call a dog Pretty sure me very similar to a weasel We made it into Maine USA And it's pretty dark and late by the time we got here, but we're just getting ready to start our next day So that means we got to start tomorrow's vlog. Hope you guys tune in for that. It should be a lot of fun We're headed down around the whole New York area down the east coast of the US through New England. It's gonna be Well seeing as there's not as much traffic on the road right now due to this current virus in this situation Maybe we'll have Maybe we'll have an easier time getting through There's only one way to find out you'll have to tune in on tomorrow's vlog Hit that like button hit the bell so you don't miss it if you want to see it early There's a good chance it'll be on patreon for the patrons Videos are posted early there as soon as I have them upload you could get them a couple of hours early or you get them up to seven days early Whenever I have them put together and ready before I even put a thumbnail or title on them I put them up on patreon for those people there to see it early Link is down below in the description other than that. I'll see you tomorrow


Your Coca-Cola bottler presents Claudia Claudia based on the play and novels by Rose Franken brought to you transcribed Monday through Friday By your friendly neighbor who bottles Coca-Cola Relax and while you're listening refresh yourself have a coke and now Claudia Oh No, that's I was lonesome already you deserve a scolding I do why because I'm lonesome for you can't help it No, that's not it. What else have I done? You don't trust me at all. I don't since when you have Absolutely, no faith in me whatsoever not a bit. Why? Do you think that I'd leave this house in the morning and go to the office without saying goodbye to you yet? You're right. I would the wood you never have I haven't well remind me to someday.  I'm almost a mother. This is one of the longest almost I've ever heard about it's less than five months now A little bit more than four to go Make it sound so Complicated, but you'd never know would you you'd never know what but I'm going to have a baby I know mama knows birth of knows Fritz knows Roger knows Reggie and I know they all know But they know because they do I met the man on the street the man on the street. What? He doesn't know Hope not. I mean you couldn't tell this by looking at me. Could you of course not really no modern science and marvelous Here I am practically about to be a mother and the man on the street doesn't know He's not looking at me darling. It's not modern science. It's your imagination. What is skip it I can't wait for the end of June to creep up on it. Well nine o'clock is creeping up faster I better be getting underway. We've been married over six months darling and it gets more wonderful all the time Just think by the time we're 60. It'll be so wonderful. I'll just bust well That's something to look forward to it is Will you call me when when you're 60 and bust You know what I mean darling. What's gotten into you this morning first Do you think I left the house without saying goodbye and then you think I'm going to spend the whole day without calling you And I've never committed either one of those crimes. I think got into me But I love you Will you call I will not I don't blame you I wouldn't call such a forward girl as me either I've got some nerve asking you haven't you have will you call I won't David you're joking. I'm not All right, then don't see if I care. I'll save you care now Bye-bye. Bye-bye.  I wouldn't be anything else Goodbye Claudia isn't there anybody who sells the Simmons nearer to where we live nobody just because David called you six times today And because you love him and he loves the Simmons I have to trap all the way over to 1st Avenue and heaven knows what street in order to get them for any objection not one I think it's wonderful Mama what now how do I look? You look fine. Why is that all just fine? You prefer to look some other way. No, but I mean you don't notice anything about me Claudia What are you talking about nothing if you don't notice that's all I wanted to know As a matter of fact, I've never seen you look so well Having a baby seems to agree with you. I think it's having a baby that makes me look well I think it's a very good reason. Don't you I don't know David says I don't look any different at all. David's very sweet. I don't feel a bit different No reason why you should and I don't intend to until I'm practically in the hospital Well, nobody can say your attitude isn't an optimistic one. Well, here we are mama. Here's the fruit store Tiny practically only a window. Good morning. Good morning. What can I do for you today for Simmons, too? Two for 25 and the right you can eat them tonight. Oh, they are wonderful and soft and cushy They'll be delicious. I give you three for 25 three But there's only two of us. Mama doesn't like them. I give you three one for your husband one for you and One for the baby for the baby for the little baby that will come with my compliments for good happiness I get you the best Mama, how did he know I didn't tell him needed an eye and David said I Mama, how do I look tell me? You're going to have a baby in four or five months. You look very well Oh, then I do look do what? Well, then it does make a difference. It's bound to it should Never thought it did David hasn't said and I didn't know this. It's all right. It says it should be there you are three He will be beautiful Come again soon. We will and thank you very much Claudia what's the matter? Nothing. Come on. What's eating you? I'm I just realized the baby will make a difference. Of course, it will. It'll make a very big difference in every way I'm starting to look different.  What did you expect to be there on? Change things between David and me won't it in a way, but I don't want them changed I want them just the way they are.  I know you're Awful, but I I'm a little scared. I Think that's the nicest part of it. I don't This morning. It was just the two of us David me. It was wonderful But now since even the fruit man knows about the baby, it's not the two of us anymore and it's sort of different I wonder if David will mind I should anything be the matter. Well, you've been so quiet hardly said a word since dinner And I hadn't noticed. Well, I have These less verbal moments of yours are quite an occasion. Good or bad Terrible. I prefer you nice and talkative even if you don't say anything Just you just the way you are What way am I David? Oh? Not very bright not very beautiful not very quiet But very just the way I want you That's nice Aren't you feeling well clubbing? I'm feeling fine. Why shouldn't I feel fine? No reason except I I just want to be sure David why didn't you tell me tell you what? Nothing come over here. Why sit on my lap Darling you don't have to I I'm too heavy to sit on your lap. Oh since when just since Must be since today Last night you didn't think you were too heavy last night. I didn't think well. You don't have to feel so bad about it You don't usually now come on.  Why didn't I tell you what? But I'm going to have a baby that you're what remember you told me and this has been going on for months I didn't mean it that way. I meant that Why didn't you tell me that it's changed me? well Who has it David of course it has you don't have to pretend The little Greek man who saw me the persimmons he knew Well, he's not blind is it I have changed it does show David. You never told me Darling if you must know the truth. I haven't noticed I'm so used to looking at you that I don't even look at you anymore In a way, I don't know if I mind that or not. All I do know is David is it going to make any difference to you? Of course not What my being a mother and not just your wife? well, I Don't quite understand should it make a difference. Well, it might won't be just the two of us anymore And I won't look like me for a while There'll be lots of other sorts of changes too. Claudia darling Is it going to make you feel different towards me me towards you? Mm-hmm. Oh David.  I'll still But it'll be different no matter of what we say Of course You know Claudia I've been thinking the the same sort of things What sort of things? Well If it's going to make you feel any different Toward me. No, that's absurd Well, is it? You start thinking about it now Taking care of the baby will take a lot of your time We won't be as free as we are now and You'll have someone else to look after And love as much as you love me Perhaps more never more It might make a great deal of difference. I won't let it David. You'll have to let it and And I want you to let it we both will At least I think we both will I guess that as long as it's both of us willing it together Then it's all right You're it's all right Anything that's both of us darling is wonderful. It's the best Funny this morning. I knew I was having a baby. I knew it But I never thought about it or thought about how it might change you and me. I Didn't think anybody else could tell now tonight Now I know Everybody can know All the difference in the world what kind of a difference of a good difference. What do you think? Oh David he's gonna grow up to be tall and strong and Just like you and no matter how much I love him. I'm sure it'll make me just Love you more Me too. You know what? I've been thinking all day long what darling. I Wish I wish that we wouldn't hurry up and have it. Oh hurry up I'm hurrying up all I can oh darling. It's months and months and months yet. Oh David why does it have to take such an awfully long time? This broadcast of Claudia was supervised and directed by William Brown Maloney When unexpected company drops in you heave a sigh of relief if you know there's plenty of Coca-Cola in the house It's mighty sensible to keep a good supply of coke on ice now that there's plenty to go round again For nothing spells hospitality as surely as delicious ice-cold Coca-Cola and Nothing could be easier on your pocketbook and your energies Every day Monday through Friday Claudia comes to you transcribed with the best wishes of your friendly neighbor who bottles Coca-Cola So listen again tomorrow at the same time And now this is joking saying or if war and remember Whoever you are whatever you do wherever you may be When you think of refreshment think of Coca-Cola or ice-cold Coca-Cola makes any pause The pause that refreshes


Hello everybody, Andrea Mayesky here with dental tutoring. So I had another question from a YouTube watcher, which is awesome I love those asking how to really What's the words I'm looking for how to Schedule appointments for a new patient depending on how much plaque they have how much charter Do you do all of the quads in one appointment? Do you do half the mouth? Do you do a little bit, you know everywhere and then have them back the next time? So this can be I think the trickiest thing for a new hygienist, especially I know for me It took me a good couple months to feel comfortable with knowing when to book the patient back So I was always a little bit worried to have a new patient in my chair because then it's it's my Responsibility to tell the patient and to decide how often they should be coming in for a cleaning, right? And as a new hygienist you're you're also probably thinking well if I can't you know clean the entire mouth Is that a bad thing are people looking at me wondering why I couldn't clean? The person's mouth, you know like why couldn't I do all of it where she might have been able to or whatever, right? But try not to think like that But a good rule of thumb to kind of give you guys a good starting off point is and this is what I use and Sometimes there are exceptions, but this is a good rule of thumb So what I use and what I even tell patients is that if it's been over if it's been two years or more That you've had your teeth cleaned. I might not be able to finish your entire cleaning two years I can probably do half the mouth today half the mouth next time So what I would do is I I would pick quads one and four so the right-hand side so depending on where You live depending on where you're watching this your tooth numbers are different But so I I would do the right-hand side first and then the next time I would do the left-hand side and Then after that so it takes three appointments And then after that we like to call it the re-eval appointment where we look we you know check all of the teeth to see if we had left any any Tartar behind which happens and there's always always plaque at that point too, right? So you're pretty much doing a cleaning again But obviously not that intense because a re-eval is usually done four to six weeks after their last appointment Sorry guys. I just heard my dog's barking. Let me just check why Tom isn't home. So I'm expecting a lot of packages. So I just want to make sure sorry But there's nobody here. So never mind So the re-eval appointment is typically done four to six weeks afterwards because you want to give the gums a chance to really, you know Heal up as much as possible, right? So that's why we do it that way and and at the re-eval Why are they barking?  I'm just gonna pause this one moment There everybody's sorry about that So Tom isn't home and I had to answer the drawer because my packages came in with all of my supplies So I had to order like masks bibs toothbrushes all of that Because I am starting my own mobile hygiene business if you didn't know and I got most of my stuff in But I was just waiting for that. So that's really exciting So sorry guys, okay, so I had to pause it again because I had another package But I think we're good now because the exciting thing is is now I have everything that I need Yeah, it took time because I had to get in my sterilizer and that took time and then I had to get in my Dental chair my equipment my supplies and my instruments took the longest and I kind of need that before I see patients I have my first official non You know friend family member patient on Monday. I'm very excited. Anyway, sorry You guys probably don't want to hear about that you want to hear about how to book appointments, right? Okay, sorry, so where do we leave off? So I left off at the re-eval appointment, right? So at the re-eval appointment what I like to do and again, it could be different depending on the office You you might have a different It's called a soft tissue management program in place every office is a little bit different But that's kind of how we do it. Now sometimes so let's say the patient hadn't been in for two years Sometimes I can do half the mouth in the first appointment the second half of the mouth Or sorry the other half of the mouth the second appointment and I might be able to get the polish the fluoride and I guess that's it Done in the second appointment too, but if I can't because there might have just been a lot there Then what I do is I do the first half of the mouth in the first appointment the second half the second appointment and then the third appointment we kind of group it all together as the re-eval appointment and The polish and the fluoride at that time So I do not polish after every appointment if it's been a while since they had their Clean I can but as we've all learned if the gums are inflamed we shouldn't we all do it But you know, I think that that's just why we don't we don't do it at Every appointment if they're coming back anyway to finish up with the cleaning and another Reason for that is because we want them to come back to finish the cleaning if we do a really really good job The first time I hate to say it, but they might not come back the next time right so that's typically how I book those types of Appointments if it's been two year or sorry. Yes two years or more and we also Irrigate at those appointments too, which I forgot to mention so I apologize So we irrigate the first appointment so the half of the mouth and then we irrigate the second appointment for the other half Sometimes we give a local and Local anesthetic to that depends on the patient now the office where I'm in now She likes to give local anesthetic on Everybody if it's been two years or or more because she says that she wants the patients to understand that okay They're there there must be a lot there too much plaque too much Charter if my dentist is telling me that I need local Anesthetic to have my teeth clean, you know because that's not typical so she wants them to think okay Let's not wait two years next time right, but I my thinking is a little bit different I don't like to just give local anesthetic to anybody because I mean I'm a type where I don't like having local anesthetic I don't like having been the needle So if a dentist told me that I needed one I would probably go somewhere else But that's just me so everybody kind of does their own thing So depending on the office where you are in they might have a local anesthetic as part of the treatment So just keep that in mind too now if the patient hasn't been in for let's say five years You might only be able to do a one quad per appointment, you know that can happen too So that can be kind of hard to really Determine so at the new patient exam You would be checking around any way to see how much charter they have if you can tell they had heavy Charter in every single quadrant then you will probably do one Quadrant per appointment of about 45 minutes to an hour per appointment. That's possible too So then if you if you think about it, you know one two three four that's four Appointments and then even five appointments to do the re-eval at the end with the polish and the fluoride So think about that too, but as I'm saying this you you might be surprised where a Patient might tell you that they had their two clean five years ago Yet you're checking and you're like wow the things look pretty good You don't want to tell them that because they're just lucky, but you you might be thinking okay Five years will probably have to do a quadrant per appointment, but then you're you know, you're in there thinking okay Things actually look pretty good. I can do it all in one appointment. That's possible So you see how there's always exceptions or I've had patients come in where they insist that they had their teeth Cleaned a year ago. Yeah, there's so much there where I'm thinking okay I'm gonna have to do a quadrant per appointment if the gums are just so You know in bad shape if you're doing the probing and there's deep pockets Even if it's only been a year so they say you might have to do a quadrant per appointment So does that make sense? Um, I hope I explained everything so soft tissue Management some offices have it in some offices Just just you know ask you to do as much as you can in one appointment But hopefully not because sometimes you just simply can't and how I see it is If a patient hasn't had their teeth clean in five years You don't even want to do it all in one appointment because you kind of want to teach them Okay, we can't wait five years. There's a lot here and that's not healthy So I want them to know okay. I have 14 years experience But even I can't do it all in one appointment. So let's do two appointments or three appointments You know, let them know that okay, it takes longer It it costs more money the longer you wait to get your teeth clean, but then also always let them know to say After we're all done. It will be easy come in every three months every four months And then we can do everything in one appointment imagine, right and they go. Oh, well, yeah Really like that's possible. Yes, it is, but you have to come in more often So I hope that helped it took me a little while to do this video. I apologize I think Somebody had asked the question on soft tissue management a couple weeks ago So sorry it took me a little while, but I will post this now right away If you guys have any questions, please let me know and thank you so much for watching


Fantastic. Welcome back to the Independent Investor Channel. Roll out our top 12 for the month going into April. This value proposition has changed. It certainly has. You're going to see a mix up in this list. I'll highlight some of the sectors I feel like are overvalued as we work through this list. For full disclosure, I do use a fair market tool and evaluator that was incepted by a couple of gents that were a hell of a lot smarter than me. It is called Treface. It is a proprietary tool that is available to users of the Merrill platform through Bank of America. This is where some of the numbers that I discuss and disclose to you guys. Secondary, I own 11 out of 12 of these. The one stock pick that I made the list based on fair market valuation and is still a good buy. I sold prematurely and now I'm kicking myself. I'll highlight that pick as we work through the list here. What you guys are going to enjoy starting at number one here, Google as big tech has kind of fallen out of favor. The search engine of things, Google fair market value here at 2160 represents about an implied upside of just over 20%. So implied upside here is anywhere from 11 all the way up to around 30%. You guys are going to understand and see that some of the value propositions have fallen off. I'll treat a group being one of those just to earmark a few. Some of the stocks that have made my list previously, had you entered into positions in those and they found places in your portfolio, you've done well. You've made money. I don't track that stuff.  That's silly. But some good quality names here, some good quality stuff that didn't make the list as well. But Lockheed Martin has previously been on the list it didn't make at this time. But Google and big tech followed right behind with Amazon. Amazon probably one of the best picks on the list. I'm happy to own the stock here. Absolutely implied, just shy of $4,000 at $39.46 implied upside at $27.5. Guys, if you've ever wanted to take a position in some of these large transformational companies, right, they don't give you opportunities very often. Okay. So you need to look at opportunities like this and say, well, is tech falling off a cliff? Are companies like Amazon and Google going away? The answer is no, they're not. They're just providing a rotation opportunity. Some folks like me are holding true through the downturn. I'm completely fine with that. I'll look to take strategic buy ups as I haven't Google, not Amazon specifically through this. And the one stock that I don't own on the list here is financials, but it is the best bargain out of financials. And it is BlackRock. Okay. That BlackRock is BLK implied upside of just over 10% it's run up nicely. Here's sitting at about 750, just a little over, but unlike some of its counterparts in the space, Bank of America and Wells Fargo, specifically being overvalued here, they are. You need to be kind of careful getting into this value trap or proposition. I don't think they're presenting traps just yet, but with the rotation to value, they've run up fairly quick. And with value stocks, 3, 4, 5, 6% of a run up is pretty significant move in some of these names that don't move very quick and don't move very fast, very often they slowly go up over time. So you want to be careful trying to rush into some of these value plays that have been in favor here as of late. Okay. Next on the list, Alibaba implied at 323 implied upside here, the largest of the group in the 40% of 42.3 Alibaba along a lot like Google and a lot like one other pick that I'm going to earmark here on the list is like shooting fish in a barrel. This is easy stuff. Alibaba is easy to justify here in the discretionary space, get you some exposure there. It does for me a round out a rather difficult space for me to fill and discretionary gets it done next to salesforce.com ticker symbol CRM implied at 276. I believe the stock is trading in the low 200s right here, but representing an upside here of over 30% and it will make it. I believe the stock based on its earnings projection will be a doubler in five years. Love to own the space premier cloud computing for everything consumer relations management software. The next three on the list, honestly, I could just earmark the entire sector. Okay, CVS did not make the list, nor did Johnson and Johnson, which I feel is best in breed. Okay, but healthcare is a buy all the way across the board. Now there's a few selective you got to be kind of careful with. Okay, but the next three made the list easy money. Merck, Pfizer and Bristol Meyer squib. I own all three. They've run back with a vengeance. Pfizer being probably the most discounted out of the name Merck with the most implied upside here at $99 fair market value implied upside of close to 30%. These are silly numbers. Valuations on these have really been recessed. And I think from a sector perspective, healthcare is number one on the list. I throw big, big cap tech in the list I do, but you've got to be selective with that group as well. As I do feel like some of the darlings of the tech sector, especially Apple and IBM and Intel are probably those that you might want to stay away from. And some companies more along the lines of a Cisco or a visa, you might want to gravitate to. All right, but Pfizer, Bristol Myers and Merck round out that all implied upside of around in between 25 and 30% here on the list. So really, those stocks that you could buy and hold forever can't really go wrong. I own them all. What can I say? Fantastic companies. The number one buy on the list here hands down, which is run up as of late is Facebook. Facebook in the big tech category implied value at 360, it's undervalued here. And it has been for a long, long time, many months, even going on years. I know the pandemic low gave you a really interesting entry into the stock, but not a whole lot of people were racing to buy stock there. They should have, but they didn't. They would have caught that really good entry into Facebook, if I remember correctly, but an implied upside here of just over 20% in Facebook. Next on the list in the discretionary space in other sector, the stuff to fill is McDonald's, but McDonald's has got a nice implied upside of around 15%. Fair market is just shy of 250, 257 or just over at 257 in big tech where Apple's not the buy Microsoft is. Mr. Softy, it's provided here this rare entry. And again, like some of these other names, they don't provide you this entry very often. So you want to pounce on these names that are cornerstones in the portfolio. You can grab them, you can own them and never sell them. You're getting your entry. Are you getting your optimal entry? Well, I don't know. If you're looking for optimal entry, you've come to the wrong place. I'm just telling you that these companies are ones that I'm happy to own myself and ones that if you are void of owning, I think you can probably justify a position in the portfolio for all these names, especially Microsoft. And last but not least on the list here is Disney in telecom communications. Fair market value puts it at about 205, not getting a heck of a good buy here at only an implied upside of about 11.1%. But undervalued nonetheless, that's sold off in the last couple days of market volatility. And Disney is providing you at least as it comes down in price, a better entry if you've ever wanted to gain some exposure, maybe supplement your existing holdings and Verizon and AT&T in the telecom communication space. Really hope you appreciated this exhaustive list. Top 12 going into April. Guys, as always, do your own due diligence. As I've disclosed, I own 11 out of 12 of these. The data and analytics that I do provide is a proprietary of Bank of America Merrill, which is the Treface tool, which helps me understand fair market value. Really, you want to make your own decisions. Not all of these stocks are going to work for all visitors to the Independence Investor Channel. If it fits into your portfolio and you can justify some sector exposure, these are the areas of emphasis to look. And I've earmarked some areas where you might want to stay away from as well as this volatility heats up in the stock market. And we continue on this rotation that we've got going from large tech that has run up aggressive growth, the SPAC space, and finding its home in other areas of the stock market. Guys, if you appreciate the information, then make sure and subscribe to the channel. Leave your comments at the bottom of the video and share the message. With those folks out there that you know might appreciate my value-oriented aspect as these do change every month. These are entertaining. These are some of the most popular videos I make. I put my money where my mouth is as I own the lion's share of all of these.  I am a stock investor and a wealth builder myself. Looking to share my story with you guys. Thank you so much for tuning into the video and good luck in your investment future.