DA1112 Lecture March 31 2026

本站添加: 2026-05-05

[00:00:02]recording in progress.

[00:00:05]>> Okay.

[00:00:07]Uh so uh good morning Darwin. So now uh I will take uh first 30 minutes uh to kind of discuss the R activity that we couldn't discuss uh yesterday.

[00:00:20]So what I want to show you is uh or what I want to explain you is how to kind of install R and R studio. I will quickly go through that one. uh and uh let's see why we are considering a sample distribution of a given statistic. For example, a sampling distribution of a sample mean instead of considering a kind of a one sample mean to kind of say something about the population. Okay. So that's what we are going to uh see from this R activity. So yeah, so before going to the S activity, let me go to the uh the end of this uh slide where uh uh uh you are given uh the guidelines to kind of set up uh our uh or to kind of install R and R uh studio.

[00:01:10]Now uh we have R and R studio. Simply R studio is the kind of the uh uh uh it's a kind of the uh uh uh uh interface uh that uh we need to kind of uh uh install uh once we install uh R uh it's a kind of IDE uh ID1 uh I mean uh but to work with our studio uh we need R as well. Okay. So, uh now uh yeah. So, let's first uh what we need to do. So, the R is kind of the installing R is the uh the first thing. Then, uh we use the R studio as the ID as a as an IDE. Uh because the R studio is kind of a more friendly version uh compared to R. You can work with R as well. But uh compared to uh working with R uh working with R studio with R is uh more uh easy more kind of uh it's kind of a friendly kind of the version. So to install R uh so first you need to anyway install ours. So what you need to do uh click this link then uh it will direct you to this uh CRAN uh it's the uh uh reciproatory uh for kind of uh download R the comprehensive R arive network. So you have kind of the version uh s for Linux uh Apple Mac and the Windows. So let's uh I mean uh if depending on your operating system uh you can uh uh for Windows. So then you will have this installer for the first time. So uh this will be the kind of the uh if you click that link if you click this link then it will uh direct you to the uh the uh the current uh updated or most uh recently updated uh our version for Windows.

[00:03:29]So what you need to do just uh download this one just click and then uh uh go through the instructions uh given there and uh finally uh you will be able to kind of install uh R uh to your desktop or the laptops. Okay. So once you install R then you will have this kind of the uh uh uh this kind of uh the uh kind of the uh yeah this is how R looks like uh now uh it's now uh here also I mean we can also now here you we see the R version okay our version and uh couple of other description so we can use this one also to kind of write our program or uh run our program and execute uh our codes and so on. But uh we prefer to you uh use R studio uh I mean instead of using R directly.

[00:04:32]Uh so to install R studio what you need to do uh go to uh uh this uh website the R studio website. Okay. So you can click that one then it will direct you to the uh direct you uh place where to download the R uh studio. So here also you have uh this uh installing uh to I mean link to install R. So this is the R studio uh desktop for Windows. If you go down uh you will see uh the R studio versions for uh Mac uh Linux and so on. So let's click uh our studio. Uh yeah. Okay. So once you click this one then uh you can uh install uh our studio version as well. So this ID version. So yeah. So once uh so once uh you uh install uh R studio uh I mean uh R studio automatically identifies the R uh that has already been installed on your laptop or the desktop. Uh so remember first you need to install R then the R studio because R studio works as a kind of IDE. Uh so so you need have you need to uh you we need to have R installed uh before installing R otherwise I mean we can't work uh with R studio without installing R. Okay. So the R is the base uh base uh tool uh base uh package. So once you download or once you install R studio so uh you have so this is the kind of the uh uh the kind of the layout of R studio.

[00:06:19]So basically uh uh so here also we have that uh that the same description uh the what is the version and so on. Now uh here basically uh I mean uh when you open it uh at the first the first time you will uh we will have kind of three uh layouts. So uh we call this layout let me take so we call this layout as the uh console. So this is where I mean once we run our codes uh we can see the kind of the outputs here. Uh ports we are running and what kind of the not the output but what are the values kind of the uh for example what are the values that we uh we have assigned for a given variable and so on and In this third quadrant or third layout or third quadrant here simply uh kind of the the files uh that are all uh in the uh already in our working directory. So uh so uh so I'm in uh I'm in the kind of the uh yeah uh uh in my file directly uh the uh files I have and uh if you click this tab uh uh and if you plot some uh graph then uh uh those plots uh will I mean we can see those plots here And this one is important packages. So these are uh so this is the kind of the uh uh part that we used to kind of install the packages and there are other ways also to install but this kind of the kind of the uh basic or kind of the famous one uh uh if you want to kind of install uh some packages. Now there are base packages already installed in R when we are installing R. The thing is there are some uh we need some spe special packages uh when we are working with R for example if you're working with time series machine learning multivariate likewise couple of special packages uh so to install such packages here you have a small tab called install and click that one and uh so most of the time uh the packages uh will be installed from uh the sen uh repository. So you need to give the name. For example, uh let's say uh uh let me uh uh let's say uh I graph kind of package. So you need to give the package name and it will install to R.

[00:09:26]Uh then only you can use that package and the functions uh implemented in that package. Remember to check whether the small box is also checked uh we need to kind of sorry uh oh sorry uh okay so it was yeah you see now uh what happened when we are installing this package uh so now uh you see uh I mean uh mistakenly I clicked uh install uh button so anyway so you see now we install this Igraph package this is uh uh it's a kind of a uh package related related to uh networking. Uh anyway uh so so you see now uh this package is now completely kind of uh accurately or correctly kind of installed. Now you see here uh we have this package uh I graph package here. Okay.

[00:10:21]Yeah. So likewise uh if you uh want to install a new package you need to give the name and check whether uh this box is also checked the install dependent see because there are some other packages we need to kind of install to work uh whatever the uh the relevant package so that's why we need to install the other relevant or the dependent dependent uh packages as well then click install okay and uh then we have help I mean you will lose the R again and again uh during your four year uh uh four years. So so here uh help uh gives us the kind of the description of uh all of the functions or the arguments that uh we need to kind of consider when we are uh when we are uh when we are going to use uh the uh kind of implemented function. Now for example let me uh let's use uh kind of uh uh yeah uh t test. Okay. So this is a one of the test that we are going to also uh discuss uh under fundamental of or one sample t test. So you will see uh so this a kind of a statistical test that we used to kind of uh compare I mean used to kind of test uh or used to perform one uh and two sample t test uh on a given data. So anyway so you see uh so t test here uh this the function and it's uh in the package stat is a kind of a base package we don't need to install. So this is the the package uh package name uh and um uh uh not the package uh uh the library.

[00:12:10]So this is the library package of yeah this the uh the name of the library stat. So uh t test is uh in in the library stats and here are the arguments that we need to consider. Uh so uh so this is the function uh this the uh the function that we need to kind of consider with this uh the arguments. So uh so we are using this arguments we can kind of uh give our whatever the uh the data values or whatever whatever the other values that uh we use to kind of uh use uh to kind of do this uh t test.

[00:12:51]Okay. Yeah. So that's basically uh the kind of three layout. Now now let's see where we can kind of type our codes. So to to uh to get uh that uh kind of the R script. So you need to go to uh this file and here you can click this new file R script. Okay, new file R script or you can simply click uh this new file uh button as well. Then it will also gives us the R script. And you see there are other there are different different uh scripts that we can use. You will use a couple of these uh other uh files uh during your uh the other semesters. So you will specially use our markdown notebook whatever the other stuffs right. So we'll simply use this R script. So this is the kind of the R script that we use to kind of write our code. For example, now let's let me write. So this R is I mean we can use R as a calculator. Okay, this is a kind of a statistical uh kind of programming uh language. It's programming language that we can use in uh statistic even in machine learning as well. Now but anyway we can use this one as a calculator like Python. Now uh let let let me simply you uh assign a value to a variable. So where so we use this x= 5 or uh so uh or someone can use this notation as well x this arrow five instead of equal sign someone can use this arrow sign as well. So this means uh we uh assign five to the variable x.

[00:14:40]Okay. Okay. Now to run. Now let's see uh to kind of uh let me make this uh the second code as a command. So I will uh to to make a command uh we use this hashtag. Okay. So now just a comment.

[00:14:55]Okay. So this a command the command. Now here you see uh so we assign x= 5. So now let's see what happen if you run this code. So to run this code you can go to that line take your cursor to that line and then you can click this run button here. So you have a run button here. So click that one. Now you see uh uh so in the environment in this layout we are having the values x5.

[00:15:28]So that means we assign five to x. Okay.

[00:15:31]So now the x so now r knows the value of x as five. And here also we have the uh the output. Okay. So output if you look at the console this one this layout here we see okay x= 5. Then let me uh uh write another uh consider another value five uh y = 3. Okay. And uh I mean you can uh use this run button here or you can uh use control + enter. Okay.

[00:16:04]control + enter press control + enter and then uh we can again run the same uh kind of the code I mean we can run the relevant code so y= 3 now here if you look at the environment now we have y = 3 here okay y = 3 and uh the console also uh we assign 3 to y okay now let's now let's see what is the uh the sum of these two values x + y and let's see uh the answer. Now you see okay now the answer now if you if you look at solve here you see okay x + y is 8 okay now x + y is 8 or even you can uh we can uh divide x by 5 sorry uh control enter okay now it's 1.66 66 something. Okay. So likewise we can use this R as a calculator. But anyway, so this is kind of the very basic. So there are a lot to kind of uh kind of get familiar with. I mean you will learn uh when you are kind of uh working uh uh with your other modules and also you can of course now uh everything's available on I mean online.

[00:17:24]So you can uh go through uh couple of fundamental uh videos or fundamental or a couple of uh uh I mean explanations on how to use R for some task. Okay. So now uh here sorry uh a moment. So here I have given you a couple of resources that you can use to get familiar with R.

[00:17:50]These are kind of very uh basics. So you can uh uh so please uh click those links and uh go to uh these uh PDFs and uh they will direct you to kind of get familiar with uh RB. Okay. Uh you will uh have uh you will have your last uh or the final uh continuous assessment from R. Okay. So yeah, I mean basically you need to kind of use the same code that I have already uh uploaded uh on model.

[00:18:24]Now uh you have this R activity. So you can download this R activity to your desktop. So go to uh so click uh this uh link and save uh this link as so you can kind of save it uh to one of your folders. Now let's go to uh that file.

[00:18:47]Okay. So to open a file from your uh desktop you can go to this open file here. Okay. So open file.

[00:18:57]Now this is my current directory. Now that's why we documents is my current directory. So that's why uh here uh we have uh whatever the file my uh document folder.

[00:19:12]Yes. So that yeah. So let me uh uh go to the uh probability one.

[00:19:31]Okay. So let me open this file now. You see here uh Okay. So let me uh kind of minimize this console. you can click this small kind of the uh icon. Okay.

[00:19:46]Now you see uh so don't worry about this one. So this is a kind of a uh code uh that I use to kind of show you uh the kind of the sampling distribution of a sample mean. Let me quickly go through this one. Uh now uh so we are having couple of quotes. So you can go through one by one. I mean you don't need to remember any of these codes but the thing is you need to be familiar okay that's what you need to do and you will use a kind of a very similar one in your uh in your final continuous assessment as well. So this set seed one uh to kind of uh this uh function or this code we used of reproduce the same results because we are kind of now here in this code we are kind of randomly generating some values uh randomly generating some values uh so therefore to kind of get the same results again and again so that's why uh we are using this set you can give whatever a number here I mean you can give just one number or maybe 1 2 3 likewise whatever a number you can give here okay only the numbers let me give one so now uh so let let me quickly go through this one now what I'm going to do here dar I'm going to kind of uh generate uh now let me go to the r activity sorry let me go to the r activity let me explain what we are going to do here. Yeah. Okay. So, what we are going to do here uh we are going to consider uh so this is the example that I uh took class uh in uh yesterday's class as well. So we consider sample of 20 student in our class and measure their height. You remember this example from the last class. So I'm going to kind of take a sample of 20 students uh and measure their height. So we call this uh it's a simple random samples that means we randomly pick 20 students from the class and we measure measure their height.

[00:21:56]Okay. So uh uh so now uh what we can do so each of us can select different different samples right because uh we uh we have kind of uh in our class we have around 100 students out of those 100 student we can uh collect uh different uh each of each of you can collect different samples of 20 students okay so you you I I I hope you understand now we have kind of different different sample let's say we have 100 samples of 20 students. Okay. Then what we do? We calculate uh the sample mean of each of those uh selected samples and let's see what we can see there. What what what kind of the observation that we can see.

[00:22:43]Okay. Now to kind of uh so this uh so here this code uh so this one actually to kind of randomly generate uh some uh heights. Okay. So randomly generate. So rn norm is I'm kind of generating uh normally distributed uh kind of the values uh kind of the values here. So uh I'm gener so arnom is simply now if you uh so you can go to this help and see what what are the kind what is the definition of the arnov. caromies. So we are kind of simply generating uh some uh values uh from an uh from a normally distri from a normal distribution with a given mean five and the standard deviation one. So I'm assign I'm kind of assuming okay uh kind of the heights are uh kind of uh five uh I mean five uh inches. Okay. So uh not five inches five uh five five ft. Okay. So so uh we are so I'm assuming uh now uh the height uh is kind of uh uh normally distributed with mean five and the standard deviation one. Okay. So so uh so I'm kind of trying to generate uh the whole population. Okay. To kind of uh generate the whole population with kind of the height five. So let me instead of uh 100 let me consider uh 10 uh numbers and uh let me show you what are the values that we have here. So we run each of these code. Now you see if you if you go to this uh console now you see here these are the one moment.

[00:24:37]Yeah. So so these are the values we have. So you see we have couple of values around five I mean we have 4.37 5.18 4.16 anyway these are somewhere around five uh with this standard deviation one. So you see this these values are randomly generated from a normal distribution. So you we can kind of generate whatever the uh randomly we can randomly generate whatever the numbers uh based on a given distribution. For example, if you want to generate uh values from a hyper geometric or if you want to generate uh poise uh values from a poison distribution uh even a kai square there are different different distributions that we have. So we can generate numbers from uh those distribution. Now here I'm kind of generating the numbers from a normal distribution with mean five and standard deviation one. Okay. So these are these are the kind of the uh kind of the population. So I'm generating 100 uh uh uh height because we have around 100 student in our class. So let me kind of now run this one again. Now you see we have we have uh kind of the height of 100 students. Okay 100 students. Now uh now we know so you know the so this is the normal distribution and you know uh normal distribution is kind of a symmetric distribution where uh in the uh on the uh I mean in the center we have uh mean five here because uh we are generating uh uh numbers from a normal distribution where we have mean five and standard deviation five sorry standard deviation one okay so the center will be five Now let me quickly uh discuss this one. Now uh what I'm going to do I'm going to kind of calculate the mean value. Mean value of all of these 100 values. That is the kind of the mean value and let's see what is the kind of the mean here. Now you see okay now the mean is uh let me click this uh set seed as well.

[00:26:47]Okay. Now you see uh the mean is now uh around 5.12.

[00:26:54]Okay. Uh sorry 11 one. So it's it's very kind of similar or it's kind of same as five. It's approximately equal to five.

[00:27:02]Okay. Because we uh simply we uh generate the numbers from a normal distribution uh with mean five. If you calculate the mean of uh these 100 values again it's it's around five.

[00:27:15]Okay. And let's calculate the standard deviation of that population uh value.

[00:27:20]It's uh uh 9. It's around one. Okay. Now what I'm going to do, I'm going to kind of generate different samples uh different different samples of size 20.

[00:27:34]So here we are using this sample function sample and we are uh we need to kind of give the the the population that we uh use to get uh that sample. So here we are given the name of the population and the sample size 20 and replace equal false. So that means uh I am kind of uh get a sample without uh replacement.

[00:28:00]Okay, without uh replacement because now you know uh uh we are kind of uh picking or we are selecting 20 different students uh at at a I mean at a at a at at a time. So uh so uh so we uh get a sample without replacement. Okay. So this is replacement equal false. So you can again check uh uh the arguments here uh uh in the uh the help using the help function.

[00:28:34]Uh so here uh we have uh kind of the argument. So replace means should sampling uh be with replacement. Okay.

[00:28:43]So if you want the replacement then uh this should be true otherwise uh it's false. So I'm using this false because I don't want any uh replacements. Okay.

[00:28:53]Now I'm going to kind of generate one uh uh first sample. So let me uh run that code and let me let's see what what uh what what are the values that we have in uh in the first sample. Okay. So these are the uh this is the uh the uh these are the 20 values that we have in sample one. So this s these values are kind of randomly kind of selecting from this population. Okay. So this sample one.

[00:29:27]Now let let's let's uh see the mean of that sample. Okay. Now mean is again uh 4.9 around five and the sample deviation is kind of 8 something. Now let me select another sample with uh size 20.

[00:29:47]Okay, now you see okay now uh now the mean is not I mean is 4.9 it's again around five that's a sample mean and standard deviation is again one okay so let's uh do uh let's see another one okay now if you look at here we see okay now the sample mean is again around five but the standard deviation for that uh sample is 6 okay now you see we are having a bit kind of different kind of the sample uh mean and the standard deviation compared to the five and the one. Okay. Now what I'm going to do, I'm going to kind of uh get uh or kind of uh select 10 samples.

[00:30:34]Let me run all of these codes. Okay. And let's see the the mean of each of the veh of each of the samples. Now you see so these are the the means of uh those 10 samples. Now you see okay um almost kind of all the values are kind of around five again. Now here 4.95 five5 this is also around five. Now this is this also around five. Now if you look at this this value this is around 5.4 four a bit kind of uh larger than five but anyway around five. Okay. Now uh now if you remember uh from uh last class what we are going to do here now these are the sample mean of each of the sample. What we are going to do we are going to take the mean of this mean of these means mean of this the mean of this sample mean. Okay. So what we can use this mean of this uh vector or mean of these all of these values. Okay. So we can use this mean.

[00:31:45]Okay. Now what happened? Okay. Now we see okay now we are having kind of mean around five. Mean around five. Okay. Uh so the mean so this will be simply this will be uh the mean of the sampling distribution of the sample mean. So this is close to the population mean of population mean of our 5.10 one or population population mean of 1.11 because so here uh we are having population mean. Okay.

[00:32:21]Now uh let's look at uh the standard deviation of this sample mean. So the standard deviation we are taking the standard deviation of this mean values.

[00:32:32]Now it's 0.1. So this will be actually the standard deviation of the sampling distribution of the sample mean. Now from our class you remember. So this is the this is the sampling uh the this is the uh this uh so the mean sampling distribution of the sample mean will be actually it should be kind of close to the population mean and uh the sampling distribution of the sample mean should be close to the sigma over square root n. So now let's see uh uh uh whether we are having uh kind of the approximate value to this sigma over square root 10.

[00:33:14]Okay. So so this is what we have here.

[00:33:18]So this is sigma standard deviation of our population and divided by square root 10. Okay. So this is the population standard deviation divide by uh square root 10. Let's look at the value here.

[00:33:32]Now you see okay it's not actually uh so it's not uh so this value is uh so now you see here so this is the standard deviation of the sampling distribution of the mean it's 0.1 and here we are having around 02 so we see okay so this value is be close to uh this standard deviation of the sampling distribution of the sample mean So standard deviation of the sampling distribution of the sample mean will be uh 0.15.

[00:34:10]So this is 0.2 uh I mean where we use the formula the sigma over square root n. So this is a bit close but not kind of the kind of the same but kind of the close. So that we are kind of working with kind of the random numbers in satay. So we are having kind of a big small deviation here. Okay. Now uh let's look at uh the histogram histogram of the uh the sample means of those 10 samples. Now you see here the plot. So we see okay so this histogram kind of uh right skewed distribution uh because uh why we are having kind of the right skewed distribution here uh we are kind of considering only 10 samples 10 samples uh of size 20. So here you see we are considering 10 samples. Now instead of 10 sample now I'm going to um I'm going to consider thousand samples and let's see what happened to this histogram. Now this is a kind of a skewed histogram.

[00:35:14]Now instead of uh 100 sorry instead of 10 samples I'm going to consider thousand s 10,000 samples. So here uh uh uh I'm kind of writing a kind of a for loop. So you can also uh kind of look at this one for loop uh to kind of generate 10,000 samples with tw the sample size 12. Okay. So let me run this one and uh calculate the sample uh mean of those samples 10,000 samples. Okay. And then let's look at the histogram. Now what happened? Okay. Now we see okay now we are having kind of a very symmetric kind of s kind of a histogram. Now you see here what happened here when we increase the number of samples uh which I mean with the same sample size. So here the sample size is 20 but we increase the number of samples then what happened to our histogram? We are having a very symmet kind of a nicely symmetric distribution. So this is this something very similar to kind of the normal distribution. Now let let me now let me calculate the mean of this 10,000 I mean the mean of the sampling distribution uh or let me calculate the mean of the sampling distribution of uh the sample mean. Now you see okay now uh it's kind of uh around five because uh we uh kind of generate or we have uh a population uh the uh so population mean is uh here.

[00:36:56]So this is the population mean. Okay the population mean uh 5.1. Now you see uh the uh the mean of the sampling distribution of sample means is again 5.1.

[00:37:11]I mean it's the kind of the same value.

[00:37:13]Now let me consider let me calculate the standard deviation of all of these sample mean. Now you see okay now this is very close to 0.1. Let's look at the population standard deviation. So this is the population standard deviation.

[00:37:30]It's8.9 uh or uh I mean uh yeah sorry population uh standard deviation uh this should be so yeah this should be actually uh population standard deviation divided by the square root.

[00:37:47]Okay. So, so we see uh so uh we see yeah so this is the uh yeah so this is the uh this is the kind of the uh sigma divided by square root n it's 2 it's 02 but anyway so we see this is uh.17 it's kind of close to 2 so you see so uh when we increase the number of samples uh now the conclusion or the observations that we get from this exercise. So once we increase the number of samples, we can get a kind of we can uh get a kind of a symmetric distribution where the mean of this sampling distribution of the sample mean is equal to the population mean and the standard deviation of the sampling distribution of the sample mean is equal to the sigma / square root n. Okay. So that's what uh we uh want to kind of see from this uh example.

[00:38:52]Now here you see uh so the uh the sampling distribution of the sample mean.

[00:38:59]So we are kind of considering the sampling distribution of the sample mean. So the mean of that sampling distribution will be population mean.

[00:39:09]Okay. And this uh the standard deviation of the sampling distribution of the sample mean will be sigma over square root 8. So that's what uh we saw from this example. Now you see so the uh sampling uh the mean of the sampling distribution is around 5.1 close to the population mean because the population mean is population mean is uh where's the population mean? Yeah, population mean is 5.1. It's exact kind of same and uh this uh the standard deviation of the sampling distribution is around 02. It's again uh similar to the or the same as the sigma over square root. Okay. So yeah, so that's what uh I just want to kind of show you through this uh R activity.

[00:40:06]So try to kind of uh do this one uh on your own and uh go through uh this whole kind of the explanation then uh you will uh understand uh why we are using this sampling distribution of a statistic instead of considering just one statistic to say something about the population. Now here let me uh explain that one also to you. Now instead of now let's let's consider just one sample here. Let me consider the sample one.

[00:40:41]Now let's say so this is the uh the mean of the sample one. Okay. So this is the mean of the sample one and uh kind of the uh the uh the standard deviation of the sample one. So you see now I mean when we are kind of uh I mean each of us I mean when we are considering different different samples you see we are having different mean values and the different standard deviation. Now in the first sample we are having uh mean around five and the standard deviation around 0.9. In the second sample we are having 4.9 standard deviation a bit lower 4.7 like this. So depending on the sample that we select we are having different means and the standard deviation. Now to ignore that kind of the differences or kind to omit those differences what we do we kind of collect a thousand number of samples and we are generating a kind of we try to kind of generating a sampling distribution of that sample mean and using that sampling distribution of that sample mean uh we can kind of uh say something about the uh population instead of considering just a one kind of the sample mean, we use a sampling distribution of that sample mean and try to say something about the population. Okay. So that's why uh we kind of uh use this sampling distribution of a given statistic. Okay.

[00:42:15]Yeah. So that's all from the uh the R exercise. So try to install RNR studio both uh and I will assign you the continuous assessment maybe by today or by tomorrow I will email you and uh let your uh bachelors as well uh so then uh you will have some time to work on that as a group assignment and yeah let's see how uh you kind of approach or what kind of the uh the approach that you will have for uh to kind of uh do that continuous assessment. Okay. Okay. So, yeah.

[00:42:54]So, Miss Sutra will now uh continue the class. So, she will discuss a couple of questions from the sampling distribution of the sample mean uh that we discussed uh uh like the examples that we discussed uh in the class yesterday.

[00:43:13]Yeah. So please uh join that tutorial class as well and yeah then uh maybe uh yeah see you then next week not next week see you maybe after two weeks uh after your holiday. Okay thank you uh yeah so utra uh that were you there right?

[00:43:34]Yes, madam.

[00:43:36]>> Okay. Uh Utra, uh let's stop recording.

[00:43:39]Uh stop >> recording stopped.

[00:43:43]>> So what I'm going to do, I'll make you as the host.

[00:43:47]>> Okay, madam.

[00:43:49]>> Uh okay. So change the host. So now you are the host. Uh so you can kind of Yeah. uh record on the computer or maybe yeah record will automatically I think uh another