today's session. Today's session starts from 12 o'clock until 2 o'clock and I should just welcome everybody to your second session. And today's session we are covering study unit 3 which is your descriptive analysis. It's a build up of what we did yesterday. So remember yesterday if we can recap on what happened we understood the basic concept when it comes to statistics. We looked at what we mean by statistics, what are the branches of statistics which are your descriptive and inferential statistics. So today we are doing descriptive statistics and we also looked at the the terms that relates to what we will be doing in statistics that are your population and the measures that come from the population are parameters and the population if is too big we select a sample and the measures that come from a sample those are your statistics. And then we also said once we have collected that data but we ask questions we look at variables. So what are those variables? Variables are just characteristics that describes the population or the sample and within a variable we get a data and that is what we use. It's a measure or a value that corresponds to your variable. Then we also looked at if we know those that we have the variables and we know that the variables can be qualitative and can be quantitative and we said if they are qualitative we can put them into categories and also if they are qualitative it means they can be categorical variable. And we also said they can be also numerical variable which we call them the quantitative variable and those variables can either be discrete or they can be continuous. If they are discrete it's something that we can we count. If they are continuous it's something that we measure and that is what we're going to be doing today is continuous discrete quantitative variables. We're going to be using those for the analysis today. Then we also went on and look at the visualization. I'm not going to go deeper into the visualization because I just wanted to remind you about the variables that we use and the types of variables to introduce today's session. So today by the end of the session which is the two hour session we're going to use your calculator. We're going to start things with the manual calculations as well. Then I'm going to bring up some calculations where I show you the easy way of using your calculator to do this scientific or the state's calculations using your scientific calculator which makes life easier. It saves you a whole lot of time and you will see during the session what I mean by it will save you a whole lot of time but you need to understand the basics because what if sometimes they don't give you the calculation but they ask you content. They ask you questions relating to how you do things. How do you calculate them? You cannot rely on only your calculator. You need to also understand your content in this regard. Okay so by the end of the session you should be able to describe what the properties of the central tendencies are which are your measures of central location or locality. We should be looking at the variation how variable or how far apart your data is from your mean. We should look at the distribution of your data. Remember we covered this yesterday as a snapshot when we looked at the histogram. We looked at the distribution of your data whether it's symmetric, whether it is left skew or right skew. Today we're also going to cover that to tell you when you use measures of central tendency or you use measures of variation how do you check that your data is skewed or your data is symmetric. We're going to not do the box plot and construct a box plot that will be on Friday next week. So only for today we're going to look at the measures of central tendencies and the measures of variation and look at the distribution of your data. And to start off with when we look at the measures of central tendencies there are three measures of central tendency and what measures measures of central tendencies like I said it is the measure of locality it tells you where your data is located it gives you the location of your information your data by means of the spread is it do you have the average do you have so it tells you a lot about and how many number of then you repeat them so more than the others whether you have a middle number all those things that's what it covers and or it gets covered under this measures of central tendencies and we're only going to cover those three which the first one is the mean the mean which is the most commonly used measure of central tendency it measures it takes all the values that you have and we divide them by how many they are that is the mean so if I have one two three four five I will add one two three four five and then I will divide them by how many they are there are only five values that I'm using so I will just divide them by five and that is what the mean is about the mean is affected by extreme liars yes what do I mean by that for example let's say I work in HR let's say in HR we are giving people salaries let's say one person and ten runs one person and ten runs the other one ends 20 the other one ends 30 and the other one ends 100 rent if I calculate the mean which tells me the average which gives me the average values of the salaries if I need to calculate that so I will have to all of them remember is the sum of all of them so I will say 10 plus 20 plus 30 plus 100 and that will give me 160 so if I add all of them they and divide them by one two three four so divide by four which is what I am saying there is the sum of all values divided by the number of values so the number of values is how many they are so they are 160 divided by four divided by four and it equals to 40 rent so when I do the analysis and I have to report back to the executive and I tell them oh in this country in this company that we have we are paying our employees good money on average we pay them 40 rent but the data tells us otherwise so that is what we say the mean is affected by extreme outliers because then it doesn't give the the correct picture because of this outlier and this is what we call an outlier or an extreme value it's a value that drags everything out out of proportion going forward so we can calculate the mean remember we are able to calculate the values from a population and from the sample so to calculate the mean using the sample information we use the formula x bar which is the mean which is the sum of all the values that satisfy the observation under the sample side the sample unit or the sample size divided by the sample size so if we select the sample and we do some analysis from this we're going to if we analyze the text score or the exam score the average exam score of students who registered in 15 in 1610 and those who only study at the western Cape then it's just summing all the scores of all the students in the western Cape region and dividing them by how many number of students are registered in the western Cape or who are registered for STA in the western Cape region that is the mean for the population is the same thing we use the mu which is the population parameter remember this is a statistic this is a parameter so let's go back to that so this is what we call a statistic because it's the measure that comes from a sample and when it comes from the population we call this a parameter which is the sum of all observation that satisfy the population divided by this population size and you can see that the two formula looks exactly the same the only difference between the two formulas is that for the sample size we use a small letter n and for the population we use a capital letter n but in a nutshell the mean calculation is the same the sum of all the observation divided by how many there are I did the example using the extreme the outlier so if I have the values 12, 13, 14, 15 and I want to know what is the mean which is the average then if these are my salaries then it means on average we pay our staff the team rent or if this is in thousand rents it's 1,300 if it's in 10,000 then this will be 18,000 rent and to get that dating we just add all of them divided by how many they are there are only five observations it's 65 divided by five and they're stating and that's how I calculated the average the mean measure of central tendency is the median the median is the middle value don't get confused with the mean and the median sometimes when your data is or your data has a lot of outliers we prefer to use the median as the average because then it tells you the middle position of or the middle value of your data set so the median is your middle number and it's not affected by extreme outliers however if you have for example not however actually when you have a lot of data in your data set it's easy to calculate the median because or if you have a smaller data set let's say for example let me not confuse you let's say for example we have one two four six seven when we went with the median as well our data needs to be sorted from lowest to highest when we work with the mean nothing doesn't matter how your data looks like because we don't care about the order but when you work with the median the order is important if I have a small data set like this it's easy to identify my middle number because my middle value will be that very day because I can see if I move from left and I move also from the light from the right I will end up four as my middle number what happens if I have a huge data set let's say we have 20 values and then it's going to happen even in the exam they might give you 15 values 20 values that you need to calculate the median to do that you need to use a position a position locator or the position number and we use this formula n plus one divided by two find the position of your median now when you use this position allocator or the position number or the position value for the median you will end up with two scenarios orderly actually if your number or the count yeah we have one two three four five so it means it's odd number if your count is an odd number like this then the middle value will be your median it's easy to identify to get that but if your you have for example even numbers because now I have six values which means it's an even value therefore the median we will calculate by taking the average of those two positions so what do I mean by that when we use this position to go find the median let's say for example I'm using the same example that I have here there are six of them so it will be n will be equals to six plus one because they are one two three four five six plus one divide by divide by two so now my data as well is not sorted so I will have to come back and re-sort the data I have a six and I have a seven so that it makes sense because your data needs to be sorted from lowest to highest so now when we calculate the position we get six plus one equals seven divide by two equals 3.5 and 3.5 means I can count from one and go to 3.5 it will be in the middle of two or four and six when it is in the middle of four and six when it's 3.5 I need to take an average of these two values so I will say four plus six divide by two equals so that will give me 10 divide by two equals to five and that will be my median so now going back this is my position and this is my median so you go and use the median position to find the median value let's change again let's say we have eight as a number there still have to use the median position so to go find so there are one two three four five six seven so now there are seven plus one divide by two which is eight divide by two which is equals to four now I need to go find my median value which is one two three four therefore my median value is six and that's where the two points they come in if the values are an odd value then the middle value will be that median if the values are odd oh sorry even then we take the average of the two that is the median and how we find the median any question before we move to the middle no question okay so like I said the median is not affected by extreme outliers now the other measure of central tendency is what we call the mode the mode is the most appearing number the most frequent number the number that appears more than the other number not the biggest number but the number that appears more than the other numbers and that is what we call the mode and it's also not affected by extreme outlier because we only looking for or we interested in the number that appears more than the rest of the other values sometimes the categorical data we can use the mode because we can find the mode of categorical data which will just tell us which category has the most frequency or the count or the highest percentage and that is where you can use the mode when you discuss the categorical data but you don't use it to calculate the mode in any of the categorical data you can use it to describe now when it comes to the mode you must understand the following they can be no mode they can be a mode or they can be several modes what do I mean by that if I have two three four five six these are my data values if I look at this data values I can see that there is no number that appears more than the other number so in this instance there is no mode what if I have two three three four five if I look at this three appears more than the other numbers so yeah we have a mode and the mode is three we can also have two three three four five five if I look at this data set I can see that yeah we have two modes and in this instance where we have two modes because we have three and we also have five as a mode we call this a bimodal data we call this a bimodal data because it has two modes what if I have one oh sorry one three not three three I must make a distinction a clear distinction between the values two two three three four four and five now I have more than two modes so yeah I have three modes and the modes are two three and four this we call it a multi modal data and that concludes the measures of central tendencies any questions if there are no questions you have the exercise to do no questions from my side okay yeah is your exercise remember yeah is your data set and this is a sample data set we can just call it a sample data set of students who are doing 3601 final year exam results these are their results and you can calculate the mean remember the mean if this is a sample we say is the mean is the sum of all observations divided by how many there are remember also that is just to add all the observation and divide them by how many they are so sorry my notification keeps on popping up on the screen that's the mean the median you first needs to find the position remember n plus one divide by two and then locate your value but the first step is to sort your data then find the median to sort your data in an ascending order which is from lowest to highest the mode you are looking for the most frequent value and that's how far I can give you a hint on how to answer this question you have 10 minutes like okay how far are we all done I actually have been thinking on on the answer so I gave you the answers because I was trying to to find the the picture so I can go to the other side anyway that will be the last time I give you answers so the mean since I gave you the answers is the sum of all values so I hope everybody got the same I agree with the answer yes the median you went yes and you found the position by using n plus one divide by two which gives at there are one two three four five six seven eight a line ten so which will be ten plus one divide by two which is eleven divide by two which will be five point five and if you have sorted your data in an ascending order you will find that the median is four two four point five agree yes and the mode is the number that appears more than the others only 51 appears more than the rest of the numbers agree yes any question anybody who didn't know how I'm sorry the the only one will be for me the medium I actually came up with 46 can you please take me through how you get to 44.5 okay so did you sort your values so let's let's try and sort these values around so which one is your so now talk talk to me tell give me your values so my values will be now 25 27 31 slowly slowly slowly I'm I'm writing them 25 27 yes 31 yes 35 yes 41 51 yes 48 okay so you sleep 48 and then you will go to 51 and so far so anybody else correct their answer so if it's five point five one two three four five point five it would have been those two values and that is why they got 44.5 the average 41 plus 48 divide by two which then gives you 41 plus 48 equals 89 divide by two which is 24.5 anyone else okay if there are no questions now when we talk about the mean the median and the mode we can also find the distribution by looking at the two values they describe how the data is distributed and they tell us the shape in terms of whether the data is huge or is symmetric when the mean and the median are equal we call that symmetric it means it's normally distributed it's symmetrical it's symmetric because the mean and the median are the same when the mean is less than the median it means there is a tail to the left therefore it is left skewed or we can call this negatively skewed it's left skewed or we can say it is negative negatively skewed when the median is smaller than the mean then the tail goes to the right and we say this it is right skewed or we can say it is positively skewed and that is the distribution when we look at the mean the median and we can also indeed the mode because the mode for this data set will be the same for the mean and the median there will be equal for this data set the the mode will be yeah will be your mode and on this data set this will be your mode because the mode is the highest peak is the one that has the most very thin so if we include the mode into the picture and for this one the mean and median and the mode will be the same okay and that is the shape and the distribution when we look at the measures of central location we're going to look at the measures of variation we're going to look at the range the variance standard deviation and coefficient of variation we will do the standard deviation and then we will take a five minutes break because I don't want to sit long in front of the computer and also talk long we'll take a five minutes break so that people can go get coffee or tea or water or something and then we will come back and we will use our calculators to calculate standard deviation the variance and the coefficient of variation then the then the next hour then it will be finished then we do exercises and so forth and so forth okay so let's look at the measures of variation what does that mean because measures of variation just gives us the the spread of your data it tells us how far apart your data is from the mean and if you look at this picture that shows you the distribution of the dataset you will see that one calf has a peak shape and the other one has a flat belly shape and those are the results of the variability within the data if this is our mean and we can see that with the one with the peak it's closer to the mean and the one with the flat belly most of the dataset are far away from them so it means that the standard deviation of the one with the peak is closer it's less it's it's smaller and the one with the belly calf that is flatter it means the standard deviation is bigger it might be 10 it might be 5 it might be 8 9 10 20 so forth and it creates that kind of a shape but when the standard deviation is smaller like 0.5 0.1 0.3 0.8 1 2 3 then the calf looks almost like it is tall okay let's understand and understand how do we coupling all these measures the first one is the range the range is the simplest measure of variation because we have that we have been doing the range since yesterday as well so we know that the range is your highest value minus your lowest value or your largest value minus your smallest value when you calculate the range you like the median you have to order your data your data needs to be sorted from lowest to highest and when you do this kind of exercises make sure that you recount your values and double check like we did with the exercise just now where someone missed to only one number it can give you the wrong answer which is correct on the sheet you will pick it you will choose that option because it's there and it will be the wrong one so make sure that you double check your work you recount the values and you recount the values that they gave you and see if you have the total number is the same what I like to do if if I can take you back as well one more time to that exercise what I like to do when I work with this kind of data every time I put the number like 25 I will go and scratch it in that way when I'm done with all the list of the values I will know which value haven't I included and then I can go and correct my data set again so please pay attention when you work with this kind of information okay so we did the range yesterday so in this data set we're going to start with one is our lowest and 18 is our highest so 18 minus one gives us the range so that is the spread of the data say only some differences between the highest and the lowest the variance and the standard deviation which also are measures of central location the variance on the other hand is the average of the square deviation of the values from the mean we also do not even interpret the variance now when we calculate the variance we can calculate it for the population and we can also calculate it for for the sample if you look for the sample variance the statistic is s squared this is where also you need to pay attention when we talk about the measures that comes from a statistic from a sample which are the statistics we always use the simple letters and like the the mean we use x bar you can always remember the x bar because you know that x is x with a bar you don't mean the mean s squared is your variance and s it's anybody can remember s so you just need to know that that is from for the sample statistic so to calculate the sample variance which is s squared you don't have to do anything with the x squared it's just the formula is the sum of your observation minus your mean which is the mean you know the formula we've calculated it previously that means central location measure mean and you square the difference divide by your sample size minus one so your small n is your sample size minus one and that calculates the sample variance the population variance which is denoted by a sigma squared and for the population parameters we use the Greek letters like with the mean we use the mu with variance we use the sigma which is a sigma squared which is also the sum of your observation minus the population mean square the difference divide by the population size now if you look at the two equations they are differences the population we divide by n whereas the sample size we divide by n minus one the answers it means there will be different so you need to pay attention when you look at the question as well in the example in your assignment to check the statement whether they gave you the population data or they gave you the sample data because then the formulas are different to calculate the standard deviation which we can also describe as the most commonly used measure of central tendency of variation sorry you will notice that we will use standard deviation across the chapter now from now on when we do chapter five when we do states when we do chapter seven eight nine and two except when we do chapter 12 so in chapter four and chapter 12 we don't use standard deviation and you will notice that we use that most often when we do the other calculations and it shows us the measures of variation about the mean so it tells us how far apart the data is from the mean like we did with the first slide when I introduced the the section it tells you those differences whether is it closer to the mean or is it far apart from the mean and the standard deviation is just the square or the square root of your variance so if you look at this the equation of a variance when we look at the sample variance you will see that the standard deviation we just put the square root in front of that equation and the standard deviation has the same units as the original data and that is why we can safely use the standard deviation to interpret the values or the data by just looking at that measure and the same you can calculate the population standard deviation which is also the square root of your variance and you can see that the equations are the same so when we do the exercise now or the example I'm going to only calculate the standard deviation but I will also show you how you get the variance and later on when we use our calculator then we can calculate the we can calculate the standard deviation using your calculator okay if I have this data set and I'm told that this is a sample data which has eight variables or eight values the same unit I calculate the mean because the mean is easy to calculate is the sum of all of them divided by how many they are I add all of them divided by eight and I get 16 as my mean then I need to come and calculate the standard deviation remember the standard deviation formula is s is equals to the square root of your sum of your observation minus your mean since we're using the sample so we'll use x bar squared divided by n minus one and if we know that is the formula now when it comes to the summation summation means summing of the values so it means we have to sum everything that is in the bracket every time so we'll have to sum the first observation minus the mean squared the second observation minus the mean squared the third observation minus the mean squared and that is what we are doing with the summation at the top n minus one and we can substitute the mean we know that it's 16 we substitute the value of our mean and our n is eight and we calculate by using the calculator to do 10 minus 16 squared plus 12 minus 16 squared plus 14 minus 16 squared until you get all the values and when you do all that and end them up because it's the summation you'll get 110 divided by seven now everything that is underneath the square root remember is the standard deviation is your variance so 130 divided by seven if we take the square root of that we get 4.3095 and this is the measure that tells us how far apart each value is from the mean from the mean and like I said everything that is underneath the the root sign is your sample variance so if I take 130 divided by seven I will get the variance of 18.514


Hey there YouTube Right, I wanna I've had one of these now for over six months And I made a video on this before but I'm a bit of a neophyte to all this and I Decided that I was gonna make another video because I'm more familiar with it now and I can tell you basically How this works? I mean, don't get me wrong. I'm not gonna go into The electron level or anything like that. I mean just as a just as the end user Who wants to have an idea of what this can what this can do for them? Let me tell you a bit back first It's a blue plastic case see-through as you can see and you can see Or the bits and pieces inside a big blobby bit there, which I'm sure probably there's a really good job. I Don't know what the parts are inside. I'm not even gonna bother trying to tell you and I can see this on the passes or resistors I see there, but that's not really what this is about. This is just about the functionality of it and What you get to see and what it is you buy Now this little gizmo is I'm sure this is a E. So it'd be K E W E I S I Care easy Care easy Okay, don't call me to the pronunciation as I don't know but it's a USB Charge display device I would say it gives you the volts amps milliwatt hour Down here and a time there I'll plug this back in and plug into this top this time As you can see, I've pressed the reset button Which is just on top of here and press it in hold it for a second. I'll do let it go and It resets everything back to zero Now of course the amps will be at zero unless there's something going through And the voltage is just indicating what the output of this is here So it's at 4.95 volts. We can say five volts The time part I was always I was first a little bit confused about how this worked and I used to plug my phone in or I still do on occasion plug my phone into this What would happen was once the phone had carried on I'd finish charging this would carry on counting I always used to think well, that's a bit of a pain really because You don't know then how long it took the phone's charge But thinking about it, which I did earlier on today. I thought to myself well because the phone is still powered on There's still gonna be little bits of charging going on. So of course this won't stop Because it hasn't finished it will only stop when you put something in That when it's finished charging it stops accepting more charge. Let's say a battery Battery on a little helicopter system Because the battery in here doesn't want to be overcharged And so when it is finished charging it will stop And that's what I've done Turn that around There we go Now This battery it seems has been charging for 16 minutes It's drawing 1.4 160 140 milliamps And we've got 43 milliamp hour of charge into the battery Since it started from a reset And I've got that connected into my HTC charger Just put it into a power strip like that And that comes across on this yellow wire Into the side My little helicopter there Which I don't know if any of yous have one of these but they're excellent fun Buy buy them for you know less than 15 pounds and they're great fun just to play around with So if I put that there and the camera doesn't go anywhere from me one you can see over here And you can see this continue now on this one when this finishes now when this has taken enough charge There'll be a little LED that will light up in here And I'm hoping that that's not going to be enough of a draw on this to keep that time going I'm hoping that that will shut the timer off. It may not Because of that LED But it indicates to me that the charge is finished and then I can you know Just give it a little bit of time for the battery to cool and then I can fly it But in the meantime We're back to this I mean this there's not a great deal to say about this You've got Input into USB and then you've got your output into USB On some of these not these particular ones. I've got another one Another one here. It's quite easy as I have done on occasion To get the USB to go in the wrong way round Okay, and it goes partway in and it Ships everything down, but it does come back to life. So that's a good thing Okay On this one, it's not that easy to put the USB in the wrong way round and so you don't fall into that little trap And you don't have those problems, but what I have noticed is But it's not just for this is with this entire thing is if you've got like your phone plugged in here and you knock this But it can happen as well if your phone is just plugged directly into here and you knock it And you lose pretty much all of the power going through These things don't like that. This doesn't like that. The only reason I know it happens on here is because if you just Come this way with me and look there That tells me the amperage being drawn for my batteries and so because This is connected to my batteries. I get to see them over there the power draw and Then I can see if I've knocked it. I can see it's gone down to nothing over there Otherwise it normally when my phones plugged in be around about two to three hundred milliamp power draw so You know, I've had these now over six months. It was 15th of December that I bought them and I played around played around I made a little video, but I made a big I made a mistake with how this works Because when it gets to 99 hours and 99 minutes It says full. I don't know why it would say full. I don't know why it wouldn't just start again, but it says full And so I thought you know, it's it's months down the line now It's there's been various things that I've bought from eBay that don't work after a while This still works and that one over there still works Which is great, and it would be lovely just to see if that actually stops the timer on that particular one When this is finished charging when the helicopters finish charging and that's something that I'm not gonna carry on rabbitting So you about now I'm gonna Pause the video and basically come back when that's finished and we'll see if it actually That's nice and easy to see The blinds again just so you can see the display It now says it's only drawing 10 million zero million up now So what I want to see now is that the timer stops so we don't want to see it go from You can't see that Let me see if I can just change that slightly for that to be different. This is one oh six And then what we don't want to do He's have it go to one oh seven That'll be quite disappointing because it's not taking any amps now My fingers are crossed because I really want this to show That this thing actually stops the timer When there's no current passing when there's no charge happening because then it actually makes Reasonable sense to have it there. Otherwise if it's just a Tick-tock timer for no reason. Well, there's the point. There's something else But you could put a power thing there for you know for those who would like to see a Quick calculation of the power rather than you know whip out a calculator or doing your head It's looking promising because this minute seems to be dragging on a little bit longer than I'm actually looking at the clock on my computer now just to Because if that goes past another minute then we can pretty much say that that does do that then but you have to have it where You know like on a like on a helicopter where it will age or as much power as what it needs And then it will shut off because it doesn't want the battery to overcharge except which has damaged the battery Bloody splendid if that is the case Okay, so my clock on my computer is now gone to 18 and we're still on six on there So before most definitely when it goes to 19, let's just have another look at what's going on here Still see that It's not the greatest. I mean if I put the light back on over the head overhead you can see better Because then we're going to see the then they do You still see the LED there. Yeah, I mean we can make out the LED now You know where you're looking is easy to see that the LED is there and we're still on 18 on my computer And even though as you can see I'm drawing off power. We still got 13 volts Going into these He says that and one goes up But you know what see what out now And I'm quite happy to say my computer is now gone to 19 and we are still on 106 so yes the timeout does stop when it stops The flow of power Because the power is no longer required and that gives a good indication of when something Is taking time to charge so now I know that it takes approximately one hour six minutes And my battery to charge in the helicopter And that's good because I know that before For some reason I thought it was like 25 30 minutes Maybe it was when I first got it, but I've thrown it around quite a lot since I first got it. So That's that's good. That's good. I'm happy with that That actually makes that particular device More helpful Then what I've anticipated it to be for the period of time I've had it which is you know over six months Since this 15th December 2015 brilliant Six months brilliant. Okay. Oh, I don't think we need to carry on with this. I think you can see now The device does work The timing system does work and it works quite well You can buy these on eBay UK sellers if you're in the UK for like eight pounds I bought both of mine for £4.48 From China. They didn't take that long to get here either But there is this issue that if you do wobble it around It does upset the electronics and it all or prefer to be Reconnected reset again. It's one of those things you'll have to Take into consideration If you are going to be charging your phone with it in search, don't expect the timer to give you a The indication of when it's finished charging because if your phone's on It won't finish charging of course because you're continuously using the power that I have in the phone on I Expect it will work now if the phone is actually powered off and charging But we can certainly see that when you're charging a device like this It does stop because we're still on 106 over there and I've been jibbering on again for minutes Hmm So yeah, that's a that's a thumbs up from me on these little devices because like I say they still work for starters And that timing device now does come into a practical usage Now even though I've just moved this and probably upset the balance a little time a bit with connections It's now gone to 20 milliamps and time has started again But I will imagine that that's just going to be until the battery goes There's a little checks and it goes hold on a minute. I am charged and shuts it back down again As you can see over there, it's flicking between 2010 I'm pretty sure that's gonna drop down again to zero, but we know that once it does drop down to zero That it does stop the timing Which is great, and of course, you know the next time I go to plug it in to try it again I can press the reset and as long as I don't move the wires around or upset anything while it's happening So the sensors don't try and pick up, you know, whether it's Charge not again. It will It will stop the timer when it's finished Okay, well, I hope that self-help so anybody out there was considering buying one of these whether they're actually a practical or not and I


That's right. Yeah, okay. It's just regular. Just pronounce it like it's American. Good morning, good afternoon, good evening. Welcome to another episode of Cloud Native TV here at KubeCon, day zero wrap-up show. I am Chris Short, CNCF Ambassador, Kubernetes contributor, and currently employed at Red Hat as a technical marketing manager running the Red Hat live streaming channels. I am here today with Laura Santamaria, one of our many guests that we'll have today on the air. Laura, how are you? I'm good. How are you? I'm excellent. How are things at Pulumi going? They are amazing. You're having a wonderfully lovely time. Yeah, I'll say, how has KubeCon treating you? KubeCon has been fun. I've only been here for about a day and a half now so far. My flight got late. I think everybody else's flight was late. Yeah, exactly. You know, something along that line. But no, I've gotten to see a lot of people. It's nice to be back in person. Right. Just seeing people in real life. I kind of don't recognize anybody because they all have masks on. Yeah, like it's really hard. I will freely admit that, like I saw people I definitely knew walk right past them. Yep. And then like, they stopped and turn around like, Chris? Yes, exactly. That's exactly been happening. So also, everyone's told me I'm much taller than I am. You are much taller than you appear on camera. So yeah, just so that you know, he's much taller than me. Just in case you're not sure about that one. So what did you do today? Day zero? I mean, well, first things first, I missed my run this morning because, you know, getting in late. So that was kind of a bit of a bummer. But then we went and got our badges. And it was really easy. So for anybody coming in late, it's really simple. Just take your clear app, you do the little health survey thing, show them the thing, and they go, great, go in that way. And they check your temperature and they say, keep going.  And I'm like, Oh, this is simple. This is nice. Yes, I was thinking it was going to be more. I was expecting like a 40 minute line. Yeah, like it was not as I was done in five minutes. Yeah. But you have to check in every day, right? And like, yeah, that's not bad. No, that's not too bad. I'll take the Monday, Tuesday, Wednesday, Thursday, Friday stickers and just live on with life little sticker. I'm done. So cool. And then wandered around the convention center a little bit till I made it to OpenShift Commons. So I stopped through there, watched some talks. That was really good. Yeah, we had a good time. And then got to see more people, went to lunch, went and helped out with our sponsor booth. Right. And then went to see more people. And it's been great. Like seeing people. Yeah, like real life people. I thought I would be more anxious about it at first. Right. I definitely didn't know what to talk about at first because like, like, what have you been doing during the pandemic? I don't know. Something along that line. Right? Like, it's just been entertaining. Which new hobby did you pick up? Exactly. Look, or, you know, how many windows did you watch? Everything happened out of? Yeah, right. Exactly. My son knows a lot more about birds. Well, that's good. It's a good thing to learn. I mean, maybe. Hobby. Hobbies are good. Yeah, there's some weird sounding bird names in like the state of Michigan. Let me tell you. Anyways, that's okay. So Commons and then you did some booths set up. Yeah, setting up for the booth. We've got a booth up here. Helped out with some other booths set up because, you know, why not? Just saw some people there like trying to figure out how to open up their sticker packs and, you know, I'd like I have a pen. We can use a pen to open this up. So I sat there helping them open their sticker packs. Yeah, they're not having a pair of scissors around is really, it's really hard. Yeah. Yeah. Yeah. So is the booth all set up ready for tomorrow? We are ready. I'm so excited to start seeing people again. Yeah, like come by. It'll be right. Stop by the plume booth. Wander by every booth because it's gonna be fun. Like there's companies I have not heard of here.  Yeah, where'd you come from? Where'd you come from? Exactly. Yeah. The interesting thing is going to be how many people are like, I don't have any more tech shirts because they've all gotten worn over the past year and a half. How many people are going to be going by like shopping all the booths for more tech shirts? Well, I will be. Yeah, I think a lot of people will. Like only the good ones though. That's true. I will assess the field and then go pick what I think is best. Something like that. If you see me in front of your booth writing something down on my phone, that's that's a good sign. Yeah, maybe he's tweeting about it though. Yeah.  So tomorrow, are you speaking? No, tomorrow, I am wandering around. I'm going to be at the booth. Okay. Going to be wandering around seeing all the keynotes. Nice. Finding more people to hang out with and say hi to. Right. And people can heckle me on Twitter. I will heckle back. It'll be fun. What's your Twitter handle? No one's actually going to be able to actually write this down. I know. Yeah, it's okay. So Nimbunatus, it's, it's Latin. Don't ask me. You can start laughing. It's okay. N-I-M-B-I-N-T-U-S. A-T-U-S. A-T-U-S. Good Lord. All right. Wow. It's all good. Yeah. All right. Look for Laura Santamaria. But, well, awesome. Thanks for having me. No problem. Thank you. All right. So who's next? I think who wants to go next? All right. We're having fun looking over there. Like, who wants to go first? Who's my next victim? Bring it on. Come on. Thank you, Laura. You're welcome. I'm gonna hand you the mic. Have fun. Andrew? I'll try. I'll do fun. I'm all right. Glad to be here, Chris. Happy to have you here. How was your day zero? Did you go for a run this morning? I did not. I had the intention to do so. Did you really? I did not. No. I didn't either. I failed today. Got tomorrow. Yeah, I didn't bring the shoes. Anyways, so here we are. Los Angeles. Security count. Okay. So you were down the hall. Yeah. Down the hall. Ran from nine to five. We had a great selection of created tracks. For the first year, we had a breakout room. Wow. Two separate tracks. Nice. We also had a capture the flag event, which has been an absolute success as scenarios of Kubernetes clusters made available for people to attack at. Nice. So what was the most interesting like thing you might have learned today about? Might have learned. Yeah. You know, cloud native security can be very intimidating. Agreed. Yeah. And we have the tendency to deep dive and assume and leave a lot of folks behind because context matters. It's been quite the evolution of lowering that barrier of entry, making information more accessible. We've produced a number of artifacts this year around security maps, reference architectures, security audits for projects. So Andy actually wrapped up the event talking about his Hack and Kubernetes book that's about to get published as someone leaving the event super excited. You get to the workplace and you have a hard time explaining folks what is that got you so excited. Yeah. And yeah, you can check out the code and GitHub, but getting it from there to production, there's a lot of people to convince. Yes. So we got this resources that once you get to talk to your security pals, they can understand the attributes, the failure modes, the compensating controls, and make the journey from zero to production easier. And knowing that you've paid attention to security, you've incorporated it as part of the design and that there's enough thought of how to integrate those interfaces with other sharp tools. Wow. So yeah, y'all had an awesome day, it sounds like. It was good. Yeah, it was good. Yeah, we got we got plenty of work ahead of us still. But yeah, that's for sure. Yeah, we got all attending to do. Yeah. So the the rest of the conference, what are your plans? I am co-presenting with Brandon Lum tomorrow. Okay. We got somewhat of a contentious title, creating cloud native security. Credits go to Emily Fox, who is also part of the percent and crew who's unfortunately not not with us this week. Right. Yeah. We're looking forward to that. Looking forward to the hallway track. Oh, yeah, yeah. Very good conversations, seeing what other people are about. Like, what have they been working on? What have they been taking away? Awesome. Yeah, man. So any booth duty for on your schedule? VMware actually made the decision not to have a booth. Interesting. To give folks the option not not have them feel they were committed to attend. That it was an option for them to to come or not, but it was not mandated by work to be here. Okay. Yeah, that's cool. We were intending to have a booth in office hours for our security technical advisory group. Okay. We decided not to do that because also running with a skeleton crew and most of our folks are remote. Right. Yeah. I mean, the Kubernetes contributors are kind of the same thing, right? Like, not a lot of people were here. We just decided, hey, let's turn it into a new contributor workshop, Doc Sprint, and off we go, right? So still make use of the space, but not waste the time. Rolling with the punches. Exactly. So being very flexible is kind of key this year, I feel like.  We have great reach and engagement through cloud native TV. I feel like it. Yeah, omnipresent. Yeah, but you know what though? I just realized, Bill, I did not grab that laptop or iPad over there, which is fine. But anyways, I can't see your comments. That's the only problem. Sorry, folks. If you yell loud enough, maybe I'll hear you. Yeah. So who's up next? Feel good? Andres, thank you for joining. Thank you. Thank you. Thank you, sir. Appreciate it. All right. Let's see. Let's go back here. I do enjoy a good pineapple pizza. Sacrifice in some places. Yes. Yes. Yes. So how are you doing today? Very well indeed. Yeah. So you've got a book coming out of here. Yes, I do. I mean, primarily we were running the CTF alongside supply chain con, right? Well, sorry, that was yesterday alongside security con, both excellent, excellent days. And yeah, we stood up at 600 nodes. We had about 150 players. Wow. Everyone gets their own dedicated cluster for each scenario. Nice. And yeah, the learning outcomes on this occasion were based upon exercises and sort of tests that our red team have done on production systems for customers. Okay. So we had a higher bar than we would do normally. Right. And people got them really well. Wow. So what was like the coolest hack, maybe? Well, actually. Or compromise incident, whatever you want to call it, right? Yeah. We did two live streams through the day. Okay. And on the second one, we had Duffy and some other folks on there. And Duffy said he was tempted to drop the Run C exploit that he's going to talk about tomorrow as part of the CTF, which is kind of a bit of a gangster move. That would be pretty funny. Yeah. We, yeah, we believe we're on the latest patch versions, but always checking. So yeah, the often with cloud native systems, because everything's declarative, there's a well known attack surface. Brad Geesman likes to say that the application is the soft underbelly, right? Pubernetic security. Yeah. It's a great turn of phrase. It is. And so, yeah, we started off with an injection attack. Had to pop a reverse shell to get assistance inside the container, then exploit our back, jump around to other places. Another scenario we had some mission controllers, Coverno in that case, that were using regexes to validate entry. And then you can get around these things. Yeah. Regex, I mean, like regex is, like, if you think YAML is bad, right? Like, or for the, not to say YAML is bad or anything, but if you think you've got a perfect regex, I feel like you're just waiting for something to happen, right? Like, if it's really vet your regex and maybe ask your vendor if you're using one, you know, hey, is this a good way to implement this? Because yeah, there's a lot of ways to get around regex, like validation nowadays. Yeah. And it comes with the territory because we've been given the skills to write our own policy, which is amazing. And now we've got so much flexibility. But then it's always who will watch the watches kind of thing. Had a great conversation with somebody on maybe stackable admission controllers. If you're looking to sign the configuration that you're pushing into the admission controller, then you do that with an operator, or you do what kind of Aparma stackable LSMs do and just run two of the same thing, one to make sure that the other one's not being compromised. Interesting. Yeah, it's a bit philosophical at this stage, but... That's metaphysical almost. So what are you most looking forward to this week, the week ahead, I guess? I'm speaking tomorrow as well. I've got a Kubernetes software factory talk, kind of all the supply chain. There's so much conversation about this. So I'm trying to go high level and just summarize what everyone else is talking about because there's so many great people who've written tools. Right, and gone down the rabbit hole kind of thing. Yeah, exactly. So I'll do that tomorrow. I will shamefully cross promote the book in any and every occasion. Who's hacking Kubernetes? Who's the publisher? Oh, Riley. Beautiful. Awesome. Did you have a co-writer? Yeah, Michael Hasenblas. Oh, perfect. Awesome. I know Michael. Yeah, yeah, yeah. So I mean, he guided me through the process and obviously he's a pre-eminent and well-published author already. Yeah, yeah. And yeah, really excited. It's due to be published, I think, at the end of this month, something like that. Cool. So yeah, definitely check that out. And then I always love the... I mean, I love going on the bootcrawls. I love the all attendee parties. And just, I mean, it's, as everyone says, it's just a delight to see people in the... It really is, right? Like, if you're watching, you're not here. I'm sorry, you can't experience it for yourself, but it's very reassuring to be seeing people again, right? Like, when you look at how things have gone the past 18 months, it's wild that we're even sitting here right now, right? Absolutely. Yeah, it's crazy. And, you know, we are six feet apart here, but yeah, everyone's wearing their masks, using the hand sanitizer. I haven't heard of any problems.  Yeah, absolutely. Yeah. There's hope and we can learn to live. I love the interaction bracelets. Yes. The degree of sort of interaction you're happy to have with somebody. Right. So yeah, though, an interesting, like, if you've been to cube cons before, there are now red, yellow and green wristbands, which I put on my lanyard just so people can see them more easily, but you can wear them on your wrist, no problem. To indicate, like, what level of physical contact or, you know, interaction you're willing to take and, like, literal, like, guide to it next to the bowls of them. It's pretty nice. And, yeah, like, Laura, for example, has a red and a green one, just in case. You never know, right? Like, you might change your mind walking back to the hotel, right? Like, you already go to bed, just put on a red one and you're good. Yeah. Yeah. So, yeah, that's awesome. So, book. Any other talks you're looking forward to, maybe? Oh, man. I would admit to barely having even glanced at the schedules. To be honest, I'm in the same boat, right? Like, I usually, this is the night that I look through the schedule and, like, line up all the places I want to be. Yeah. Yeah. I have, I love the calendar export feature, so I did go through it a few months ago. I do have a calendar that will tell me where I need to be. So, you did the legwork already. Yeah. See, you're ahead of the game. I'm not. Yeah. Good for you. Yeah. I haven't had a moment to reflect. So pleased that we've now shipped the CTF, shipped the last two days. Like, the last two day zero conferences were just brilliant. Really. Yeah. So much. Awesome. And, yeah, so I'll kind of settle back into it and get my conference groove on again. There you go. There you go. Yeah, yeah, yeah. No, this is great to be just back here with everyone.  I mean, it's not, it's not quite, you know, to that level, but close. So, yeah, you know, it's, it's an experience when you walk through a hotel and you're like, hey, there's somebody I know from, you know, another part of the world, maybe potentially. Absolutely. I mean, I haven't seen so many U.S. friends for two years. Right. Universally. Yeah. I mean, people didn't realize how tall I was, for example. You know, right? Like, it's just one of those things. Yeah. So anything else you want to plug or talk about? Or, you know, I don't know if I've got any plugs left in me. Yeah. There you have it. All right. So who is next up here? Go next. All right. Awesome. Thank you. Thank you for joining me. Thank you. So you wearing a Spire shirt. How's it going? Yeah, good. Awesome. There's so much stuff going on these few days. Please introduce yourself for the audience. Let people know, you know, what you've been up to today. Yeah. Yeah. I'm Brandon Lam. I'm the co-chair of the CNCF Tech Security. Awesome. So naturally, you know, we've been hanging out at the security con today. There's just so many security-centric events over these past few days. It's been a real focus, like a real focus. The number of times I heard supply chain already. Like even in, like, non-security-related ones, because I bounced between OpenShift Commons and GitOps.com today. And it was mentioned in both, right? Like, so it's like a universal need. No, I mean, security is one of those things where you can't really escape it, right?   It's really about making sure that people come together and that it's a shared responsibility going forward. Right. I think, I mean, yeah, there's not going to be one team that's going to manage your Kubernetes security, I don't think. No, it's just, it is a collaborative effort and multi-layered thing. So what are you working on in, I mean, in tech security right now? I know there's some papers that have been out lately, some stuff's been going on. You want to talk about that a little bit? Yeah, so there's some awesome stuff we've done in the past, right? We've done the CNCF-Cognitive Security White Paper. Yep. Recently, we just launched the Cognitive Security Map, which is like landscape, geographical website where you can go in, navigate through it, look at the different, you know, more from practical approach. Right. What are the projects that, if I'm looking at, you know, image signing, artifact signing, what are the projects I have to look at, and things like that. One of the things that I think is really cool that was just released today was the Cognitive Security Microsurvey. Ooh. Yeah. Okay. So this was something. Please tell me you have a short URL you could say on there or something. Unfortunately, I don't. Okay, fair enough. But if you look hard enough on Twitter, you'll find it. Yeah, exactly. All right guys, what is this Microsurvey going to cover? Yeah, so this Microsurvey kind of, it's a follow-up from one of our previous surveys that kind of looked at, you know, generally what people are looking for. We are checking back up on what are the concerns and challenges of the industry in general.  Right. But I think one thing that, you know, we also saw a lot of things on vulnerability management, secret management as well being like big things. One of the key things I will mention in this was that organizations actually found more than 50% of the respondents said that they felt like they couldn't, they didn't have the right expertise or the talent to be able to implement security. Well, to be fair, the market is super hot right now. And there was a reason for that is because the talent is very much wanted, right. So learning Kubernetes, thinking about Kubernetes securely and operating in that secure model is going to be the wave of the future, right. Like, it's, it's not something we can say, hey, you know, let's worry about security after the cluster is deployed. No, it's part of the life cycle of that cluster. Yeah. Right. That's awesome. Yeah.  And like, we, we have a lot of other projects such as like the security defaults, right. Eight, which is kind of like, you know, how we in Python, you have the PEP 8, which is like the style guideline. This is like the security defaults guideline of if I'm starting a project as a maintainer, what should be my mindset and approach that I think about with going forward with how I design the skill configuration of my, my project. Interesting. Yeah. That's very nice. I might have to go read that. Like, I know it's going to be long and it might take me a few hours, but that's fine. I might have to take a break. But anyways, so what are you looking forward to in the conference this week? So I'm going to be talking, I think Andrews mentioned that we're doing a talk tomorrow on cognitive security. I have another talk on Thursday that we're talking about workload identity, you know, spiffy spires, you should say. Yeah. So we're going to be talking about, you know, how do you deal with the access control problem when you go from a single cloud to multi-cloud. Now you have all this tens of thousands of things you have to manage together and it becomes, it becomes a difficult management problem. So that's, we're going to be talking about how you can use spiffy spire to address that. But in general, there's so much excitement for spiffy inspire. There's like, I think there were like three talks, three or four talks throughout the entire con. So for folks that don't know, right, like what do spiffy inspire do for you? Yeah. So spiffy is the secure product, production identity framework for everyone. The main idea of spiffy is how it defines what workload identity is and how workloads can securely obtain identities and to be able to perform, you know, authentication between the different workloads to, you know, this idea, I know the, the work gets thrown a lot around zero trust. Sure.  Yeah, exactly. Isn't that nice? Yeah. You know, I am the pod you're looking for. Yeah. So with everything going on, you know, security wise out there in the world right now, it's, you know, every day I wake up, there's another breach. So people moving to Kubernetes have to consider the security aspect as they're moving, right? Like it's, it's not like you can bolt it on after the fact. I mean, you can, but like technically, yes, you can, but it's a bad idea to wait, start secure and follow that kept bait kind of thing. Yeah. And, and you know, a lot of times people say like security is costly, but if you do it earlier, you're actually saving costs. Right. Yeah. Yes. Building that into your pipelines and automating that security process as much as possible and then verifying it. Like you said, you know, like that's very important. Checking everything zero trust. Yeah. Like not, not saying, oh, yeah, you know, that pot is definitely good, even though it's not signed or, you know, anything. Yeah. So the, the talk you're doing, it's, you know, broad is what Andrew said. So the, the main topics, what are you going to talk on? I guess. So, so the main idea right is, is going to be talking about with workload identity, I think as a community with kind of looked at it in terms of what the cloud providers have provided us with. IM systems and stuff like that. Right. And then we take a look at the autogonal problem, which is user identity, right, which we've been, you know, for decades, this has been kind of the status quo already. And how we manage it is, you know, we don't think about user like Chris AWS or Chris GCP or something like that. Right. We think about Chris as a person. Right. And I think the, the main concept is we want to have the same idea with workload identity. Right. We don't want to say this is platform specific thing. It belongs to the organization. Sorry. Yeah. If you can hear that, there was an announcement going on. Sorry. They want to hear more about it. Yes. I mean, so what are you most excited about in the talk, I guess, I'm most excited to see like, kind of hearing other folks, what they think about this, hearing, you know, while it's on the pain points, while there's some, some things we can better integrate with, you know, I think in the end, we all want the same thing. And we just have to really get together and discuss and figure out the common goal and the common way to achieve that instead of like, trying all the different things. 400 different ways to do it. Yeah. Mucher rejects. That's not good. Yeah. So any parting thoughts before I bring up the next person? Is there some piece of knowledge you want to share with the cloud native world? Because now's your chance. Yeah, I think, I think, I think I've said this before as well, you know, going into security, everyone doesn't have full knowledge of what I'm going to do, right? And so it's like, every incremental thing you can do, you know, look for the things that easy win, start with that, start building out your security posture from day one. Day one. Exactly. Exactly.  All right. So who's next up here? Thanks. Thank you. Appreciate you coming on. No. So, Edith, you're next. Oh boy. No. Be fine. Thank you. So, so folks, if you have questions, feel free to ask. We have a bunch of experts here. I promise I'll make this easy. Yes, because I'm learning English. Okay. Sorry. Congratulations for your next step in your career. So thank you very much. Awesome. So this is your first Q crown? It's a, I can say it's my first coupon. The first one was in 2019. Okay. But I wasn't able to speak English. So I can consider this the first one. Awesome. That's great. So what do you do? I'm a software engineer. I was working for a company Places Equal two years ago. Then I decided to prepare for my master in computer science. And right now I am just studying English and doing some, improving some skills. Awesome. So what's your first coupon? Were you speaking English? My first coupon, speaking English, this one. Yeah. Yeah. And is there anything you're looking forward to in particular? Needwork is awesome here. Yeah. And also some technical sessions about beginners in Kubernetes. I'm looking for that kind of session. Yeah. So how intimate, like this might be a very forward question, but how intimidating is Kubernetes for you, right? Like, it's not too much, I think. Yeah. For me, it's a little bit because of the language. Right. But I think even, even about that, I can, I can make in Greece my needwork and also I can learn many things. Right. Yes. Yeah. It's a font of knowledge literally just every day. Yeah. Awesome. So you're networking. This is, there's 3000 plus people here, I think. The network opportunities are awesome so far that I've seen.  Are you looking forward to meeting anyone in particular? Or is there someone you've met right now or recently? I meet some people. So I meet a guy I used from one university. He's studying PhD. Nice. So we are in changing technologies about Kubernetes operator, for example. Nice. Okay. Awesome. What, what, I mean, what challenges do you see with Kubernetes and your day to day work? I'm looking more deeper knowledge because I am preparing for my certification in Kubernetes for developers. Nice. So the last time I tried, I failed. Well, it's not a trivial one. It's, it's, it's hard to, I will try again. And I will reach the, the score. So now I am trying to, to get the knowledge here. Awesome. That's fantastic. What kind of like study things have you done to, you know, bolster those skills?  What is it that you think were some of the hardest parts of that test? I mean, is there, is, is there one thing that you could be like, Hey, I would study this piece of material more than the other, not giving away any of the test answers or anything like that. Yeah. Bill's over here, like about to rip my neck off. What, what would you suggest people focus on? First of all, I, I can say that if the, if the people want to start in Kubernetes, they should dominate containers. Yes. Dockers is a very good way to go. Then start to practice for some certification because that forces you to learn some basics. Right. Fundamentals about Kubernetes and the certifications for developers is a good point to start. Right. Yeah. So where is everything fundamental and more easy than Kubernetes for administrator and security?  So the, the, what are your next steps here during kube.com? What are you going to, like, what are you looking forward to? In, in the kube.com? Like during this week? Yeah. I would like to, I would like to attend more, even more sessions for beginners. Yeah. Then in my future steps is also to propose an, an session to, to, to talk. Yeah. That'd be great. So I, I, I'm preparing for that. So I'm doing my English and also trying to know more about my technicals, technicals about Kubernetes. Right. So that is my next step. Awesome. Awesome. Yeah. So you're not a beginner per se, but you're, you're closer to the beginning of your journey. What made you get involved in Kubernetes? What was it about the project? Was it something at work or was it just a curiosity of your own? Yes. It was curiosity. Okay. I always feel attracted for technology, but new technology. And Kubernetes was in the time attendance threat. So I started to do some demos for myself. Then the company where I was working, I just mentioned that I made some demos and they say, okay, let's try to make a project and we start to work in a project. That's awesome. So how's that process going, right? Like, it was a challenge. I can say it was a challenge. Difficult because of the frustration to try to work, to try to work something because in that time we was working with GPUs. And this is our challenge that the normal Kubernetes, we should know about NVIDIA over Kubernetes. Over Kubernetes. Yeah. And I can say that it was a challenge, but we finally we did it. That's awesome. No, the GPU use case in Kubernetes has just grown over the years. And I see that becoming like almost a better option for compute, maybe, right? Like, you can definitely throw a few GPUs at a problem and it'll potentially outperform multiple cores, right? So yeah, it's very, very powerful way to manage your compute. That's pretty cool, right? Like, how long how long did that project take out of curiosity? Maybe one year. Okay. Yeah, from like, from idea to actually in use. Yes. Awesome. Great. So what's next for you, as far as the next Kubernetes project that you're going to tackle potentially other than the CKAD? You said you were going to do a talk or propose a talk. Let me know if you need any help with that. And then the CKAD, after that, what do you think your next step will be? I will definitely would like to start a project from scratch for a big company, bigger. Right. Move up on the road. Lead the project. Awesome. Yeah, that'd be great. Are you volunteering in the project right now? Are you working on the aspect of it or with anybody, any SIG or anything? In Kubernetes, I am doing some contributions. I'm trying to translate from English to Spanish. That's awesome. Make more accessible. Yeah, that's awesome. Thank you. Also, I am trying to get enter into the query to contribute to the query of Kubernetes. Awesome. That's awesome. So I have no idea how much time we have left whenever we want. Okay, cool. Any parting thoughts before we potentially sign off for the day? Party? What are any other thoughts that you have about? I don't know. I just want to say thank you so much for the invitation and enjoy in Cubicon 2021. Yeah, it's awesome. We're back, folks. And yeah, thank you so much. I really appreciate you coming on. So, folks, that's all the guests I have for today lined up. Thank you very much to our live studio audience here, all five of you. So yes, it being day zero, it's kind of quiet out here right now. But to my left is the vendor hall, which will be buzzing tomorrow. So feel free to check that out. And yes, awesome. Yes, thank you for sharing your experience. Yes, thank you everybody that came on today for sharing your experiences. And I am Chris Short. And you can follow me on Twitter at Chris Short. And if you have any questions, feel free to send them to me on the Kate Slack. And I'm happy to help you out with anything that I can help with. And for now, I'm signing off. We'll see you around.


Gregorian chant, it turns your mind to God. It purifies your passions, your intentions. It's music that is purely sacred. It has no other function. And there's a kind of spiritual, like a subdued spiritual joy and elation in those chant melodies. It just carries your soul to God. You could almost say it's like musical holy water. You get sprinkled with it sonically.


Hello and welcome to this session. This is Professor Farhat and this session we're going to look at a comprehensive example dealing with Pension so at this point if you don't understand how pension work the various component of pension expense how the PBO work by all means Go to farhatlectures.com view the prior recording So this basically this example would illustrate all the concepts that you have learned in this chapter So if you if you get follow with this example, then you are good to go I would say when it comes to accounting for pension So let's take a look at the data that we are giving So we are giving three-year data for Jackson company Jackson company adopted an acceptable accounting for its defined benefit pension on January 1st 2016 with the following beginning balances They immediately put 200,000 away Under plan asset and they believe they will have a PBO as of today $250,000 So this is what they start with simply simply put they started with a underfunded plan of 50,000 Okay, in 2016 they have they have we have three years 2016 2017 and 2018 in 2016 the annual service cost was 16,000 the settlement rate and the expected return is 10% actual return on the plan asset is only 18,000 annual Annual funding contribution we wrote a check to the plan Administrator for 16,000 and the company paid benefit of 14,000. This is 2016. So let's work with 2016 So notice we're going to start with 2016 and if you know anything about this example That looks like straightforward this example 2016 Then we're going to be adding a little bit more in 2017 more complication a little bit more in 2018 So let's start with this simple year 2016 2016 we started with again where they have a pension liability of 50,000 at the beginning of the year Why because when we started the plan we immediately put away 200,000 but our obligation was 250 Then the employee worked the year Work one one additional year for us. Therefore, we have an increase in pension expense an increase in PBO of 16,000 Interests on the liability, which is how do you come up with 25,000? We said the settlement rate is 10 percent 250 times 10 So this is going to also increase the PBO Our return on asset Return the expected return is 20,000. Why 20,000? It's it's the plan asset times the expected return, but we only returned 18,000 Well, we thought we're gonna get we thought we're gonna get 20,000. We expect it to get at least 20,000 Let's make put it this way that that was the expected the actual was only 18 It means we're gonna have to put an additional 2000 because we always use the expected But we're gonna do what we're gonna do with that remaining 2000. That's gonna be a loss an OCI simply put Now we have a loss an OCI an expected loss of 2000 And again a loss in OCI is a debit loss and OCI is a debit So we debit OCI 2000 simply put for that Contribution this is the check that we wrote so our cash goes down And our plan asset goes up because we submitted a payment to the to the plan asset And we paid 14,000. Therefore, we have less PBO and less plan assets So we pay our retirees for we did we did not really pay them the administrator paid them So let's first take a look at the balance and PBO The balance the first thing you want to do is compute the balance and PBO the balance is 277,000 Which is starting with 250 plus 16 plus 25 minus 14 and the plan asset is 220,000 so we are still underfunded by 55,000 So our liability will be under our liability will be 57,000 Therefore if we started with 50 and we need to get to 57,000 that means we have to credit Because this is the journal and we have to credit pension liability 7,000 We add up all our expense. Our pension expense is 21,000 Cash is credit 16 and here we debit because it's a loss because we incurred the loss We debit OCI 2000 and obviously we credit the liability 2000. So let's take a look at the journal entry The pension expense is the total of the pension expense OCI is a debit of 2000 because we have an unexpected loss And the pension liability is 7,000. How do we come up with the 7,000? Once again I started with the pension liability of 50,000. I needed 57. What I'm missing is 7 And I paid 16,000 and cash always make sure Your debits And your credits are equal which is they are equal to each other. So this was 2016 now bear in mind So when you start 2017 when you start 2017, this is what you are When you start 2017, this is what you are starting with you are starting with a pension Liability of 57,000 pbo of 227 and plan asset of 220 So now we are going we're going to be working with 2017 data Okay 2017 data, let's review the 2017 data annual service cost of 19 Again the settlement rate and the expected return is 10% the actual return was 22 The annual funding we're going to write a check for 40,000 We're going to pay our employees 16,400 but in 2017 we amended the plan We amended the plan and as a result now we have a prior service cost of 160,000 Why we just we amended the plan? What does amending the plan means? It means we decided to Give additional benefit to our employees and as a result our obligation went up by 160 And amortization of prior service cost of 54,000 So we're going to amortize out of this 160 54,400 just they just gave us this amount We don't worry about how we came up with it. So let's start 2017 worksheet Again 2017. We are starting with 57 of pension liability 277 of pbo and 220 of plan asset now Also, we are starting with a debit balance of 2000 and in the oci because oci This oci just want to make sure I you know this it's a balance sheet account So oci is a balance sheet account and we're starting with a 2000 debit balance The first thing we do we're gonna we're gonna add our pbo I'm not add to the pbo the prior service cost. Remember we amended the plan. It's going to hit our pbo It's not going to hit our expense. Okay, 160,000 and we're going to put 160,000 in pc and prior service cost Now that's going to give us the balance adjusted balance as of the beginning of the year And we're going to add the service cost the service cost for this year was 19,000 Interest on the obligation is 437,000. How 43,700 how do you come up with this? It's your pbo including prior service cost times 10 Gives me 43,700 return on asset That's 22,000 and guess what happened here. Guess what happened here this year return on asset was 22,000 But if I take how do I come up with the expected return? I'll take my plan asset To let's take this one 220 and I multiply it by 10% if I do so I'll get 22,000 in my actual return was 22,000 It's very unusual that this happens, but this is to simplify things So the return on asset was 22,000, which is equal to the actual return. That's fine Then we amortize out of this 160. They're telling us you are going to expense 54,400 again. How did he come up with this just told us this number So we're going to amortize this amount. So we're going to add it to our pension expense and reduce it From p prior service cost notice. We're going to be reducing it from the 160 Okay, then the contribution we contributed for 40,000. So we wrote a check for 40,000 reduce cash and increase The plan asset by 40,000 Okay, then we paid 16,400. That's not on our record. That's on the on the Administrator. So the first thing you do once again, the first thing you should do is Add up what happened to what's your pbo Your pbo is 483 483 300 your plan asset is 265 600 So you should have a liability of 277 100 You should have a liability of 277 100. Well, you started your liability with 57,000 you started with liability with 57,000 and you need to have 277 700 now because your pbo is greater than your plan asset by 277 100 Therefore the first entry is you need a credit of 160 700 for the pension liability Then what we do is you just go through the Go through the figures and you have to find out what else do you need to do You have to debit pension expense. This is a debit to pension expense 95,100 you credit You credit cash cash is credit cash is credit cash is credit For the amount that you paid which is 40,000 and the prior service cost You started with 160 you reduced it by 54,400 and this is your first year. Therefore, you're going to have a prior service cost 105 600 and since you did not have any Basically a beginning balance. There is no beginning balance and prior service costs Just hit the just the entry will be debit to prior service cost of 105 600 to do what to start the balance 105 600 And for the gain and losses nothing really happened. So there is we don't There is no entry for the gain and loss. We still have 2000. So your entry will consist of the pension expense Other comprehensive income again.  Well, we started with 160 Then we amortized 54 400 So basically our balance should be 105 600 Why the balance should be that much because we did not have a beginning balance You just just hit oci debit oci 105 600 to establish the balance Now this balance will stay with us for next year because now it's established Again the pension liability how they come up with this 160 700 Again, your goal was to have 217 700 as a PBO and you started with 57 000 therefore what you were missing is a credit of 160 700 and it's 160 700 And you paid cash 40 000. So this is the entry for 2017 Now now remember what's going to be with you for next year what you're going to be starting with which is So you make sure you know this when you start next year, which is 20 2019 You're gonna you're gonna have you're gonna have A PBO of 483 300 the PA of 265 600. This is the PA. You're gonna have Pension liability of 217 700 again A loss actually not again a loss of 2000 and a prior service cost the debit balance of 10 105 600 why because that's a loss. Okay Now, let's take a look at 2019 data and 2019 I'm sorry 2018 at 2019 your annual service cost is 26 Your settlement rate is still 10 percent your actual return was 24 000 your annual funding is 48 You paid benefit of 21 000 And amortization of prior service cost is 41 600. So you're gonna be amortizing 41 600 And there is a change change an actuarial assumption december 31st PBO so at the end of the year by the time we got to the end of the year The actuary came and I said look your PBO now is it doesn't matter what you are thinking Your PBO should be 520 000 you guys remember The actuary can do this the actuary can tell us your plan now has changed So simply put your PBO at the end of the year is 520 So, you know your how much you're ending PBO is if it doesn't come come up to that you have to plug that figure You have to plug that figure and we're gonna see how so this is something new. We're gonna be working with this example Okay, this is something. So let's go ahead and So those are beginning balances Prior service cost a loss of 2000. Those are both debit We have a credit in the liability and this is the PBO and this is the pa service cost was 26 000 debit. I'm sorry, uh, put it under pension expense and put it under PBO interest cost is for 48,000 330 which is the PBO times 10 percent your return on asset is 24 000 and What's your return on asset that should be how much it should it should have been let me see It should have been Let me just 265 600 times 10 percent. It should have been 26,560 So notice you only earned 24 000 your actual return was 24, but you should have earned 265 60 again You have an unexpected loss another debit to OCI of 2,560. So I want you to kind of follow this OCI Let's see. This is your OCI you started with 2000 and you're going to hit OCI by 25 60. Okay Amortization of prior service cost Of 41 600. So again, we're going to reduce this amount by 41 600 as we reduced it It's going to hit the pension expense because now we're amortizing that prior service cost Contribution me the contribution of 48 000 We're going to reduce our cash and increase our planned asset and the administrator paid 21 000 to our retiree that's going to reduce our pvo and reduce our planned asset now Here's what's going to happen. We're pretty much done with everything Here's what we do at this point We need to have a balance of 500 and 20 000. Hold on a second How do you know it's going to be 520 000? The actually told us your balance should be 520 So when I add up all these when I add up my pbo column now, I'm working with the pbo column I'm working with this column here I'm working with this column here And I'm going to go ahead and add these figures to see how much they So let me go ahead use my calculator and add up these figures. So I'm going to take 483 483 000 300 plus 26 000 plus 48 330 minus 21 000 That's equal to all these numbers together All these numbers together Okay, let me just hide this all these numbers together are equal to All these numbers together 500 36 000 630 hold on a second And you're telling me my based on my computation my liability should be 536 630 and actually said no that's way too high your your your Your balance should be 520. What does that mean? That means I made a gain immediately I have less liability of how much the difference the difference is 16 630. So I have to reduce my pbo basically have a plus to the pbo of 16 630. Where does that go? It's a gain in my pbo. Remember any changes in actuarial Assumption hits the gain and the losses now. What's going to happen is now I have a gain of 16 630 Which is a credit to oci simply put Simply put this was the beginning balance. This was the beginning balance This was uh, yeah, this was the beginning balance. So we credit the balance 16 630. We credited 16 630 We debit it 25 60 therefore the entry and under for the gain is 14 000 o 17 Okay, let's keep on going. Let's keep on going here Okay, so what I did is I added all my pbo. It doesn't matter what I added up to I have to make it 5 20 The plan asset is 3 16 600. Therefore, I have to have a liability of 203 400 I have to have a liability of 203 400 my beginning liability is 217 Well, excellent news. My liability went down My liability used to be uh, I used to be 217 700 my liability went down to 203 to 203 What does that mean? It means this year? It's it's a good it's a good year for the company because I'm going to be debiting my liability I'm going to be debiting my liability for 14 300 Notice this is a debit. Why because my liability went down? So this is my debit and my credit to oci is 14 470 This is my Debit, uh, this is credit because I'm amortizing a loss. It's credit. I'm removing the loss. I'm reducing the loss This is credit to cash for 48 000 and my annual pension expense is 89 3 70 So let's take a look at this my journal entry my journal entry will consist of my pension expense My pension asset now my pension asset is 14 300. Why is it an asset and in the in the prior two years? What's the liability because in the prior two years my liability was going up Therefore, I needed to credit my liability this year it went down and it went down again from 270 700 Let me just do it one more time. I know i'm repeating myself. It went from 270 700 to 203 400, which is good That's why I debited my it's called now pension asset My other comprehensive income. I amortized 14 700 from other comprehensive income um, this is for the What was that for that was for the? actuarial gain and Expected losses. So remember I have lost I have losses of 2560 because my my asset did not perform well So I had expected losses then here comes my gain of 16 000 my gain was 16 000 from the from the actuarial From the actuarial gain 16 6 30 you net these out and equal to 14 070 which is a credit. It means my it's a gain it's a gain Then other comprehensive income. I amortized 41 600 from my prior service cost 41 600 and obviously my cash always Add up your debit add up your credit. So this is your journal entry now. What's going to happen when you start 2019? When you start 2019 Remember when you start 2019, I want to make sure you know what your balances are When you start 2019, you're going to have 64 000 in your prior service cost. Why 64 000 you started with 105 600 then you reduced it by 41 600 So your balance is 60 000 60 64 000 64 000 Your oci it's going to be a gain of 12 000 Let me just change the color here Your oci will be a gain of 12 000 and 70 12 000 and 70 why again just follow my t account analysis And you will see that you started with 2000 then you increased it by You increased it by 25 60 you increased the debit then you reduced it by 16 000 6 30.  It may go down Then you'll have to figure out what's going to happen to your pbo and plan asset, but this is a good Comprehensive example If you can follow this example because this example involved both prior service cost It involved actuarial gains and losses, which is the losses were for the Unexpected losses on our plan asset and the gains were the unexpected gain on the pbo suddenly Our our actuary told us well, guess what you owe less. You're gonna owe less money So if you could follow this example, I think you're gonna be good for this chapter Which is the pension a very important topic that's covered on the CPA exam If you have any questions any comments by all means email me or see me in class