I need to have my calculator up. Please make sure that you complete that register and you have your calculators ready. Because today's session, I'm going to show you how to use your calculator. What type of calculator do you have? I have a Casio, but it must be an old one. It's not as fancy as yours. It can't do the fractions. Okay. But I don't think you will be required to do a lot of calculation in your studies. But I'm just going to show you in case they ask you to do some calculation so that then you know how to do certain calculations. Today's session, we're going to be looking at how we do hypothesis testing for the correlations. In a correlation, it's where we look at if two numerical variables are related or are correlated to one another. This is our second last session. Our final session will be on the 27th of September, and then we would have covered everything you need in terms of statistical skills that you require in order to go through your module with ease. Then we will see if we can set up an exam preparation session for us to go through at least one exam paper and see how to answer certain questions from there based on everything that we would have learned in preparation for you to go right there. Then also, I will be sharing with people who have been attending the sessions, we will share the link to a one-on-one session if you want to book such. There are times, so because I've got limited time, I can only offer support to those who have been participating week in, week out within the session, so we will prioritize them first. So look out for that email so they will look at the register and then send out the link to those people to say, if you still want some support or some engagement with me one-on-one, this is the process that you need to follow. So it's not going to be sent out to everyone, possibly it will be sent to those who have been attending the sessions so that we can support you even further. Okay, so are there any questions before we start with today's session? Have I started the recording? Yes, it says recording has started. Do you have any questions? Nope. The other person just left, so it's only me and you. Yeah, so it's fine. So like I said, we're going to be looking at correlation and like I said, also in your module, you're not expected as well to do a lot of calculation, but I'm just going to show you how to do some calculation for other measures, but you will be expected to know how to interpret your correlation values as well. So in order for us to go through this, you need to know there are formulas that you're going to go through and also you require a calculator because sometimes you will need to do some calculations. So when we talk about a correlation, because we're talking about the relationship between two numerical values and to visualize them, we always put them on a scatter plot. And a scatter plot shows you a relationship between your independent variable and your dependent variable and remember always everything that sits on your x-axis will be your independent variable because it's your input variable and anything that sits on there y-axis will be your dependent variable, which will be your output variable, which is something that we want to predict or so. In terms of correlation, it is used to measure the strength and the direction, not only the strength, but also the direction of the relationship between the two variables. And sometimes we call this relationship, we want to check if it is a linear relationship. So when we talk about the strength, we talk about whether is it a strong, a weak, a moderate or no relationships type of the strength. And when we talk about direction, we're talking about whether the value that you will be calculating will be negative or positive or when you look at the scatter plot does it show that it is declining or is it going up? What we mean by declining, it means when something goes up, the other thing goes down. That it means it's declining when it's ascending. We say when something goes up, the other thing also goes up. So that is what we're going to monitor and look at in terms of correlation. So if I have my independent variable x, which has the score of 1, 3, 5, 5, and 1, and I have my dependent variable, which has the score of 4, 10, 12, and 13, I can take those two variables and plot them onto my scatter plot. Remember, on the x-axis, we plot the independent variable. And on the y-axis, we plot the dependent variable. So in terms of a scatter plot, you can see that 1 corresponds with 4. So this will create a dot. So where I have my independent variable of 1, I will have my dependent variable of 4. And that will be the dot that you place. And 3, 3, and 6, you go and look at 3 and 6, that will be the variable. And 5 and 10, go to 5, and you go to 10 where they meet. That is the variable.  And that will be the variable. And 1 and 13, where it's 1, and there is the variable. Now, after plotting this dot on this scatter plot, we can clearly see that this is a value that is away from the rest of the values. And that is what we call an outlier. You can look at what an outlier is. It's an extreme value. It's a value that is far apart from the rest of the values. But when you look at the other values, if we ignore the one that is far apart, we look at the other values, you can see that when my x values are increasing, my y values are also increasing. And that is what we're going to be talking about in terms of the correlation. And looking at this, we will say this is a positive correlation because my x value and my y value are both increasing at the same time. Okay, so there are measures that you can calculate to measure this relationship. Sorry, to measure this relationship, we can calculate it by using what we call a coefficient of correlation, which is also called R, which is a measure that will tell you how well your linear equation describes the relationship between the two variables that you are measuring. So when we talk about the linear equation, when we do the regression, you will understand where this linear regression comes in. But R will tell us how well the two variables relate to one another. So for example, how do we calculate the coefficient of correlation? So you will be given the x and y value, you're independent and dependent. There is a formula that you need to calculate. And on that formula, we always calculate, so this has the bar, this are some of the values that you need to be able to know how to calculate them. So for example, the first value, which these are the values that you are given x and y are the values that you will be given. And if you add all these values for x, you will get 15. When you add all the values of y, you will get 45. And these values, when you add them, the total, we call them the summation. So for example, the summation of x will be equals to 15. The summation of y will be equals to 45. That is what the total is. So total means summation, adding up. Summation means adding up. So now when you have the summation of all the values, you are able to calculate what we call the mean. So if I need to calculate the mean of x, which is x bar, we know that it is the summation of x divided by how many they are. And here we see that the summation of x is 15. And how many they are? They are one, two, three, four, five. They are five. And when we take 15 divided by five, you will get three. Hence, I have the mean of three. The same thing, 45 divided by five will give you the mean of y, which is 45 divided by five. It should be equals to nine, because it's the sum of y values divided by how many they are. So if you have your mean and you have your summation, you should be able to also calculate other measures. So for example, like the second block or the third block, the column, it says x observation minus the mean. So what it means, it says for every value of y, we go into subtract the mean. So one minus three equals to minus two. Three minus three is zero. Five minus three is two. Five minus three is two. One minus three, it will be minus two. And if I add all of them, I will get zero, because the sum of all this, they will cancel out. Minus two plus zero plus two will be zero. Plus two will be two. Minus two will be zero. The second, the fourth one, or the fourth column, it says y minus the mean of y. Hence, I put there the x bar and the y bar. I forgot to put the bar on there, or the line is very small, but there is a bar there. So we do the same. We go to the mean, your y observation. So four minus nine is minus four. Six minus nine is minus three. Ten minus nine is one. Twelve minus nine is three. Thirteen minus four is nine. And if you add all of them, you will see that you will get zero. The fifth column, it says x multiplied by y. So x multiplied by one, it means we multiply our x value with our y value. One multiplied by four is four. Three multiplied by six is 18. Five multiplied by 10 is 50. Five multiplied by 12 is 60. One multiplied by 13 is 18. And all of them, you get 145. And we do the same. The next one, it's x squared. So therefore, it means this value of x multiplied by itself twice. One times one is one. Three times three is nine. Five times five is 25. One times one, we already made that is one. The same will be the same for y squared. Four squared, which is four times four is 16. Six times six is 36. 10 times 10 is 100. 12 times 12 is 144. And 13 times 18 is 169. And in all of them, you will get 465. So once you have built all the columns that you need, you can then go and calculate what we call the coefficient of correlation, which is your R, which is your Pearson, R coefficient of correlation. Remember our table that we have calculated some of the values. So now we can take the summation because you can see there is the summation of x and y. Remember the total is your summation. So n is how many there are. So we have established that there are only five of them. So we can just substitute five onto n. The summation of x and y, we take the sum, the total of x and y is 145. We're going to substitute it into that formula. The summation of x times the summation of y, it will be, the summation of x will be 15. The summation of y is 45. The summation of x squared. Now, this summation of x and the summation of x squared are different. The summation of x squared, it is 61. The summation of x, remember it's 15. The same, the summation of y squared will be 465. So we just substitute into the formula and calculate your R. So in your exam sometimes, they will just give you the value of R is equals to 0,32 and ask you to interpret that value. This is how you calculate the value, but you might be asked to interpret what does this value of 0,32 mean? And in terms of that, based on the scatterplot, you should be able to see or determine whether your correlation coefficient will be a positive or will be negative. For example, if you look at this scatterplot, all the dots are lined up. It clearly shows from the scatterplot that when the values of x are increasing, the values of y are increasing. And because the dots are in a straight line, usually the R, for this type of a visualization or for this kind of a scatterplot, R will be equals to 1. And when R is equals to 1, we say in terms of the direction because R is equals to 1 is positive. We say it is a positive relationship. And when it is equals to 1, we say it is a perfect relationship because everything lines up. But when the dots are scattered everywhere, where you are unable to format or see a pattern arise, because clearly on these dots, you cannot say when x is increasing, y is also increasing. It's not the same case. However, in terms of the coefficient of correlation, when you calculate it and you get the coefficient of correlation of 0.18, we say this is a weak positive relationship because in terms of the direction, it is positive. So let's look at another example. If the dots look like this and R when you calculate it and get R of 0.85, we say this is a strong positive relationship. And when it looks like this, where R, the dots are, you can see that when x is increasing, but y values are decreasing. We end when you calculate R, you get minus 0.92. You can also state that there is a strong negative relationship. But this are not how you only interpret the data. So let's assume that they didn't give you the graph, they just give you the R. So you should be able, oh, sorry, you should be able to use the R value to say whether it is a perfect positive or negative relationship or it is a weak relationship or it is a positive or negative strong relationship or there is no relationship. So when R comes closer to 1, we always also state that the two variables are closely related or are more related to one another. At some point, you should be able to also complete what we call R squared, which is an important statistic that indicates the variation, the total variation in your y value or your dependent value that is explained by the variation in your x value, which is your independent variable. So how do we interpret the values? If your R is equals to 1, we say it is a perfect, or if it's exactly equals to negative 1, we say it's perfect negative relationship. If it's minus 0.7, we say it is a strong negative relationship. If it's minus 0.5, we say it is a moderate negative relationship. If it is minus 0.30, we say it is a weak relationship and if it is 0, we say there is no relationship. The same will happen when it is positive because the only thing that will change will just be the direction, which will state positive. And this is how you will interpret your R. So you just need to know how to do that. Then if we go into test the hypothesis, then the way we state our null hypothesis and our negative hypothesis, we use our coefficient of correlation but for the population, which is pi, which is your phi, not pi, but phi, phi, phi h i phi. So to state your null hypothesis, you will always say your phi is equals to 0, which is your population coefficient of correlation is equals to 0. Your alternative will state that it's not equals to 0. So we always state with not equals to 0. Our null hypothesis will state that there is no correlation between x and y because your correlation is equals to 0. Your alternative will state that there is a correlation between your x and y. You will need to be able to calculate your test statistic because your P will always be 0. So your R is something that you would have been calculated or it will be given to you. For example, like the first one that we did, we know that our R was equals to 0, 32.  I will just take 0.32 squared and your N, it will be how many there are. There were five values, so your N was equals to five and you can calculate your test statistic. You will need your degrees of freedom to go find your critical veil. So if your R squared is positive, your coefficient of correlation is positive and V1 is greater than 0. And also you will need to find it where your R is negative the square root of R squared if V1 is less than 0. So it will be negative if V1 is negative. It takes any value that is negative. It will be positive if V1 is greater than 0. Okay, so how do we then test the correlation? So for example, is there an evidence of linear relationship between X and Y at 5% level of significance? We know how to state the null hypothesis. V is equals to 0, no correlation. Alternative will state that there is a correlation. Statement number two, state whatever you are given. Our alpha value is 0,05. Our degrees of freedom, let's assume that they have given us previously, which was five minus, we said it is five, right? Yes, it was five. So our degrees of freedom is N minus one, which is five, oh sorry, N minus two, which is five minus two, which is equals to three. And we go and calculate our test statistic. R is 0,32. P is zero because we take it from the null hypothesis, it's equals to zero. It's always hypothesized to zero. And the square root of one minus 0.32 squared and N is five minus two. And when we calculate our test statistic, we find it is 0,585. And we can then go and make a decision. So we go to the chi-square test. So they will give you the critical values because you don't have a table. Otherwise, they will give you a p-value and we can always use a p-value. So using the degrees of freedom and your alpha value, you go and find the critical value. But in your case, you don't have to go and find the critical value because you don't have a table. They will give it to you and you create your region of rejection because it's too silent. It's a non-directional test because we can notice from the alternative hypothesis, it is a non-directional test. So therefore, there are two regions of rejection. And we look at our critical value, it's 3,1824. So if you go to the t-table, you will find with the degrees of freedom of three and your critical value, your alpha value of 0,025 because it's two sides, we divide alpha by two. And our critical value is minus 3,18. And then we take our test statistic, we check if it falls in a rejection area or it will fall in the do not reject area. And we can see that 0,585, it falls in a rejection area. Therefore, we do reject the null hypothesis. We reject the null hypothesis and we claim that there is no sufficient evidence of linear association at the 5% level of significance. Oh, we do not reject because my error is pointing on the wrong side. It should point here. We do not reject the null hypothesis because 0,58 is less than 3. It doesn't fall on this error. Yeah, it's pointing at the wrong point. It falls in the do not reject area. So we do not reject the null hypothesis. And we can claim that there is no sufficient evidence of linear because we said there is no relationship if we're not rejecting the null hypothesis. And that's how you will answer some of the questions. They just, they will just need to know if you're rejecting the null hypothesis, how do you conclude? So let's look at exercises in terms of your questions from your past exam paper to see how the questions are framed. You just need to know how to state the null hypothesis. Let's go back to those statements. You just need to know how to state the null hypothesis. What kind of a statement do you make when you state the null hypothesis? You need to know how to interpret your coefficient of correlation and so on. You will see when we look at exercises and activities that sometimes they will not even ask you to do any calculation. So let's go there. So the first exercise. A group of hospitalized patients who have been diagnosed as suffering from dementia are treated with certain drug over a period of time. These drugs were prescribed to improve their mental alertness. A researcher studies a random sample of deity, this, a sample of deity, this patient who have been on this drug, who have been on these drugs for varying amount of time, hoping to establish a relationship between the number of days of the drug treatment and the patient's go on a mental alertness test. Which is the correct formal way to express the appropriate null hypothesis? Is it number one, number two, or number three? How do we state the null hypothesis? Number one. It will be number one. Yes. Same question. Right? Same, same question. Nothing has changed. Which is an appropriate test to determine the significance of the relationship between the number of days that the drug was administered and the score on the mental alertness test obtained by the sample patients? What will be the appropriate test? Is it going to be a car square test? Is it going to be a Pearson product moment correlation? Or is it going to be a tea test for one sample? Number two. It will be number two. Now, I want to go back to the statement. The previous weeks, we were talking about a test to test the difference. Now, let's assume in the exam, you have question one, question two, question three. They all give you almost similar statement. How will you identify that this one is talking to the relationship or you need to be looking at correlation? So the only thing that is different between what we do in this week and what we did the previous week. Previous week, it was more about are there any differences? You can see, yeah, they don't talk about differences, but they talk about relationship. So the previous one would have said, establish if there are differences between X and Y. With coefficient of correlations or correlation or hypothesis test for correlations, they always talk about relationship. Next week, we're also going to look at the other one, where it talks about the relationship. The difference also between what we're doing this week and what we will be doing next week is that this week, we're talking about numerical values. Number of days, test, all of them are numerical values. Next week, we will be talking about categorical values. So it's not going to be the actual numerical value or values that you are able to count or measure. It will be values that you can put into categories. So you should be able to know the differences when you're looking at the questions. So we're moving on to the next exercise. Oh, this is for next week. This is for next week. Don't worry about this. I don't know how it came into this, because it talks about the contingency table. Remember, the two days this week, if it talks about contingency table, it's not what numerical correlation about. It still talks to the relationship, but it's not the one that we're looking for. Okay. The other thing that I forgot to mention when we were explaining the correlation coefficient is that your R value takes any value between the value of minus one and one. It takes any value between that. So it can be negative one and one. It cannot be negative two or negative three. It cannot be, but it can be negative zero comma eight or it can be zero comma eight or negative zero comma three five and so on and so on. As long as the value is between negative one and one. So this question, it looks tricky, right? It says which of the following can take on a value of a negative zero point five? Is it one, a probability, two, level of significance? Remember, level of significance is alpha. Three is coefficient of correlation, which is R. And four, which is the variance. I'm going to use the probability as px. So which one can take a value of negative? Always remember that. Your level of significance is always positive. Your variance will always be positive because you're taking the square root and you're taking the square of the values. So it will always remain positive and this will always be positive and this will always be positive because the probability, remember probabilities are always between the values of zero and one. So they cannot be less than that or more than that. They are always between zero and one. So looking at what I've just explained right now, it leaves us with only one, right? Which is option number three? Three. So that will be the value. If there is no relationship at all between two variable x and y, what would be the most likely value of your Pearson correlation between coefficient of correlation R out of the following? No relationship. Do you still remember what will be the value of R? Number three. R will be equals to zero, which is number three. So the questions in the exam will be like you will be sailing through because it seems as if you just need to know the basic concepts of correlation and that's it because I haven't seen any way we calculate something as well. So I don't think they will ask you to calculate. Okay, so this one looks a little bit different. A researcher suspects that children's level of anxiety during a test will interfere with their memories. He gives a list of items to be memorized to a sample of children and gives them a test to see how many items they can remember. Directly after, he also tests the level of anxiety of each child with an anxiety scale, where a higher score shows a greater level of anxiety. The researcher draws a scatter plot of the relationship between the level of anxiety and the number of items recalled. The results are presented on the scatter plot below, where our X variables, which is our independent variables, are your level of anxiety, and your Y variable, which is your independent variable, is the number of items they can record. Now looking at this graph alone, you can already make up your mind because what you're looking at, when the level of anxiety increases, as it goes up, what happens to the number of items they are going down because when the level of anxiety is low, the higher the number of items they can recall. When the level of anxiety is high, the lower the number of items they can recall. You need to be able to make interpretation based on the thing that you are visualizing before you even go to the question to answer the question. So let's see what the question is asking us. What can the researcher infer about the relationship between the level of anxiety and the number of items remembered from the graph presented above? Now how do we interpret the graph that is above? Number one, it says there is a negative relationship as the anxiety rises, less items are remembered. Number two, it's a positive relationship as anxiety falls less, as anxiety falls less, items are remembered. Number three, there is a negative relationship less item remembered as anxiety falls. Number four, no actual relationship the graph shows a negative trend over time. Is it one, two, three, or four? Number one, it will be number one, it is a negative relationship when the level of anxiety increases the fewer items or lesser items they are remembering. So the answer will be number one. Like today we'll finish early, we'll be done by an hour by the rate that we're going. Exercise seven is the same information I'm not going to read again the data, but now looking at the same information, suppose the researcher uses the data collected or calculated uses the data to calculate the Pearson product moment correlation are coefficient to determine the size of the relationship between the number of items remembered and the level of anxiety. Which of the following would be most probable as a description of R? Which of the following description of the expected value of R seems to be the most appropriate? So looking at this are we able to say R is equals to zero therefore it means there's no relationship? Are we saying R is greater than zero therefore it means there is a positive relationship? Or are we saying R is less than zero therefore it means there is a negative relationship? Or are we saying R is not equals to zero, we say R is not equals to zero there is nothing like that, that we calculate, it's either it's zero or any other number between minus 1 and 1. Looking at this graph, which statement? Number 3. It will be number 3 because number 3 it says less. So when you see the sign that it looks like this, always think of the values that goes to the left. Remember if I'm starting here at 0, any value that goes to the left will be negative. Any value that goes to the right will be positive, right? So when it's greater than it means it's positive. When it's less than it means it's negative. So this will represent negative value. Moving on to exercise 8. Now here is I said to you that you don't, you are expected not to, what did I say? Not to do any calculations, right? And they give you a table and they ask you to find which one will be the closest. So now let's see if you really need to do any calculations. Which of the following values given below is the closest to the probable value of Pearson product moment correlation coefficient for the value of x and y? Now all what you need to do is let's look at these values. It says if the value of x is 1, the value of y is 8. So I'm gonna put it there because I don't want to go and do some calculation because this says it's a perfect because this is minus 1.0 and this is 1.0 and this is 0, right? So I can just create a scatterplot of this. It says when it's 2, it's 7. When it is 3, is 6. So it goes down if I can write 1, 2, 3, 4, 5, 6, 7, 8. I can just do it like that. 1, 2, 3, 4, 5, 6, 7, 8. Just like that. Just making an illustration x and y. So we say when it is, let's start again because I created new values. When it's 1, it's 8. So it will be there. When it's 2, 7, then it will be there. When it's 3, 6, then it will be there. I scale is not two points so that is why I am struggling to write the right thing in my book. When it's 4, it's 5. When it's 4, it's 5. So it will be somewhere here. When it's 5, it's 4. It will be somewhere there. When it's 6, it's 3. It will be somewhere there. When it's 7, it's 2. It will be somewhere there. When it's 8, it's 1. It will be somewhere there. Looking at this now would you say this is positive or negative relationship? Negative. It's a negative relationship and looking at the dots are they perfect? Yes. So therefore if they are perfect remember when it's perfect therefore it means your R will be either positive or your R will be negative one. So which one will that be? Number one. It will be number one. So you don't even have to go and calculate anything or try and do anything. You just need to know how to visualize the values. The other thing you can do as well is look at your values. You can see there it says one two three four five six seven eight and in reverse eight seven six so it means when one is going up the other one is going down. Right? When the values of X are going up the values of Y are just going down. And you can just determine that when something is going up and the other one is going down your R will be equals to a negative something and if they line in a perfect straight line then it will be equals to one. If it's not then you can find if they were scattered all over then would have selected zero. But at the moment they are all aligned or lined in a perfect straight line. So that's how you will answer some of these questions. Yeah so let's go to question number nine. A researcher hypothesized that the drug treatment for the hospital schizophrenic patients improves their mental alertness. The study or he studies a random sample of 27 patients to see whether there is a relationship between the number of days of drug treatment and patient scores on their mental alertness which is an appropriate null hypothesis for this research. There's always one way of stating the null hypothesis regardless of which questions or what statement especially for the correlation is it one, two, three or four. Number four it will be number four. We always use phi, phi, number ten. Which one or which of the following is suitable for representing the ages versus the height of a group of children? Age is numeric height is numeric. So which one do we use a scatterplot? Do we use a contingency table or do we use a histogram? Number one. It will be number one a scatterplot. So when you have two numerical values always remember two numerical values then you use scatterplot. One numerical value then you use a histogram. Two Partegorical values. Then we use a contingency table and that is what we're going to learn next week. Okay, exercise 11. Which of the graphs below is most likely to represent a Pearson correlation of R equals to positive 0.85 between variable X and Y. If measure, if measurements are plotted on a scatterplot. Which graph A, B, C for a positive 0.85 from A? It will be graph A. So because graph A is positive there is a purely A positive strong relationship and this is a negative strong relationship and this is a weak. I don't even know whether to say it's a weak relationship or whether there is no relationship because it looks like most of the values for Y are constant. So I can't even say it's a weak negative or positive with this one. No. Why don't we say. I would say there is no relationship. Yes. On this one. Yes. Hi. So we almost at the end. A negative correlation between X and Y implies that a person scoring low on X will generally score on Y. What is a negative relationship? It will be Y on Y. Sorry. Wait. It says a negative correlation between X and Y implies that scoring low on X will generally. Here is your X. Scoring low on X will generally mean for a negative relationship, it will be you will score high because your point will be there and if you score low, your point will be somewhere there. And then it will be just like that. That's the positive correlation. Sorry. Yes. Yes. So this will be low and high. And if it was positive, it would have been low and low. Always visualize. If you are not sure about certain things, sometimes just draw for yourself how this catapult would look. You can just do something like this and then something like this to say this is negative, this is positive. So if I'm here at low and here at low, then it's a positive relationship. If I'm here at low and here at high. So find alternative ways to remember how to interpret certain things. Okay. Question 30. I did explain this sometime and I wrote somewhere on one of the slides at the top right here and I said R lies between certain values. So I hope you still remember that. Pearson correlation coefficient can take values ranging between. Number three. It will be number three, which is it can take ranges between minus one and one. Probability. This is for the probability. This is for R. I'm just going to write the R. And this is, I don't know what that is. I don't know any value that can be between that and that. Maybe probably your alpha. Yes, alpha can be like values, but for alpha value we don't actually even restrict it to only 10. It can even go to 20 or something like that. Because for 80% it will be 20 and so on. I thought we almost done it. So I have a lot of questions from your past exam papers. Okay. Let's look at another one. Pearson R represents. So now you need to be able to know how to interpret your Pearson R. How to interpret your coefficient of correlation. Number one. Does it represent a comparison between observed frequencies and expected frequencies if the null hypothesis is true for the distribution of the data across the variable? Number two. Does it represent the relationship between two variables when the way in which they vary together is compared to their individual variances? Number three. The difference score between two variables relative to their pooled standard deviation. Your keywords, difference, relationship and in terms of the first one I'm going to highlight the two values. Is it one, two or three? Number six. It will be number two. Number three is something that we dealt with last week. Remember for the pooled variance where we looked at testing for two independent groups. And number one, we're going to deal with it next week, which is one part of the Chi-square test for contingency tables to test the relationship between categorical variables. So relationship, question, exercise 15. What is the coefficient correlation between the following x and y values? Also, you can do the same. You can just say because on this one it has a negative value. I'm just going to write here in the middle zero and here minus one. Oh no, really? Let's assume that this is a Cartesian plane that looks like this. At the middle here, there is what we call a zero on the x-axis. On the y-axis, any value this side is negative. Any value this side is positive. Now, based on the information that you see in front of you, it says what will be the coefficient of correlation for this. If your x is zero, so it means x is right on top of the line, y is minus one. So the dot will be here. The next one it says where x is zero, which is right on top of the line. Your y is also zero, which will be right on top of the line. So you've got already two points. The next one it says when x is zero, so I know that on this line x is zero and y is positive one. So let's assume that positive one is there so that will be the dot. If I'm looking at this relationship like this, what is my r? Zero r will be equals to zero, which is option number two. So you don't even need to go and do some calculations that I showed you to say go and calculate your sum of x and y, calculate your sum of x, your sum of x squared, so that you can substitute into the formula and calculate. You don't even have to worry about that. All you need to do is use the scatterplot to find your answers. Okay, Miss Elizabeth, can you just go back to exercise that one? Can you please explain to me why I was saying it's zero? I'm trying to understand exactly why I was saying it's zero. What makes it to be zero? It's the dot where the middle one that makes it to be zero on the straight line. Okay, sorry. I see now we've got company. Hey, because we're trying to late. We already had explained what does zero mean and all that. Let me go back so that then we can be on the same page with everyone. Thank you. I don't think I ever even, no, I don't have. So looking at a typical scatterplot, this is what we call scatterplot, visualizing two numerical values. If the dot are lined perfectly on a straight line, like the way you see them, your R can be equals to, it doesn't have to be equals to one, but in terms of the purpose of explaining the value of your R, I'm going to say if the dots are lined, they're like in a straight line going up. It means it's positive. Therefore, your R will be equals to one for a perfect relationship, perfect positive relationship. If your dots are scattered all over the place, they can be what we call a weak relationship, or they can be what we call a no relationship when R is zero comma one eight. We say it's a weak relationship. If R was zero, we say there is no relationship. The same similar to when you have a scatterplot where you have all the values scattered all over. Your R is equals to zero comma eight five. We say it is a strong positive relationship because the dots are going up. When X is going up, Y is going up. If you look at the bottom one, the dots are also scattered almost everywhere, but at least they form a straight line. But you can clearly also see that when X is going up, R is going down. Oh, sorry, Y is going down. So these values are going down when X is going up. And when you calculate R, you might find that it is minus zero comma nine two. And that we can say it is a negative relationship. And R takes a value between zero one and negative one or negative one and one, which we call the perfect relationship. As the value comes closer to one, we say they've got a relationship. Now, the other way also of interpreting your correlation is based on the scale. Not only exactly like this, because if it's zero point six nine, you will say it's a moderate relationship, whether it's negative or positive because it can take a value of negative or a positive. If it's zero, it means there is no relationship. Therefore, it means the dots are scattered all over. You cannot tell whether when X is increasing, Y is increasing or it's decreasing. You cannot tell because the dots are just lying all over the place, right? Or alternatively, you can have a scenario where it might look like this. Even though your values of X, you might have your dots looking like this. It's not a perfect relationship because when the value of X are increasing, the value of Y are staying constant, right? So there is nothing there. So your R here will be equals to zero. Or alternatively, you can have some way where it looks like this. It is still perfect, but when your X is constant, let's say this is two and your value of your Y, so this is your X, your Y, as it increase or decrease, the value of X stays the same. So at the end, that will be equals to zero. So there will be no relationship between X and Y because X is constant or Y is constant in a way. And that is what is happening with that one. So we can go to another exercise that we just did, this one. So on this one, you can see when the value of X are increasing, the value of Y, they are decreasing. When the value is the value of Y are decreasing. And we did the scatter plot just to demonstrate and we find that because it's perfect, they line up straight in a linear fashion, therefore your R will be a negative one because it's a perfect relationship. Now on this one, let's go back there. And we also did some exercise there where we looked at whether it's a negative relationship or a positive relationship. But when it comes to X and Y value on a scatter plot, because right here it's zero, you can see that it's constant for the value of X is constant, but the value of Y is either going up or down. Therefore, there is no relationship here. So we cannot say it's negative one or positive one because there is no relationship. And when there is no relationship, remember how you interpret the values because then it means your R is equals to zero in that manner. And our last question of the day. TSN correlation coefficient R represents. Is it one, the size of the relationship between two variables? Two, is it the shape of the relationship between two variables? Or three, is it both? As they represent the size and shape, I know in the beginning I said, what did I say? I said, R represents length and direction. Now let's think about it. If I have this and I say R is positive, I'm just going to add a few more so that I don't get. So if I look at this, you are, what does it represent in terms of this? Does it represent the shape? What does the shape mean? I don't know even what this does. Does the strength talk to the shape or does the size talks to the shape or the strength or because they can both? Yeah, it's a tricky one. I would think it's probably both. I will also think that because if I look at some of the questions, there was some way where they asked about the shape when we were just going to go back again to see if we have any question where it spoke about the shape. There is no relationship. There is no way where it spoke about the shape. This was which one of the closest probable. There was no way. So for this one, I'm going to assume that it's both of the above because this will tell me the size in terms of how big is it? 0.98 and the shape will be is it scattered everywhere? Is it a negative relationship? Does it go towards the negative or is it constant? Whether it's no relationship or it's scattered everywhere or is it scattered like that? Which will tell me about the shape of this correlation like which also doesn't say much because then I'm going to assume that the shape talks to the direction which is either negative or positive or no relationship and the size will talk to the strength which is always determined by a numerical value of 0.98. So this is the shape and this is the size. If I'm reading this question the way they phrase it, therefore it means it's both of them. If I may say something, I also agree with you that it's number three because when you look here it says if dots form a U or S shape number one then correlation coefficient is not relevant so it means that also we deal with shapes. When it comes to this plotting of the graphs you understand so that's how I understand it. So it deals with size and shape also. Yeah, I agree. So it is number three and are there any questions because that's the end of our exercises. If there are no questions then it means we're going to leave early so I'm just going to put the register back onto the chart. Please make sure that those who joined late you just complete the register before you leave and remember that next week is our last session until further notice then we can start looking at exam preparations. Definitely it means I will need to know when you are writing your exam so that we can plan for that. And the time is on the 8th of November. That will be long before the 8th of November. Then it means the whole of October you are on your own but we can arrange in terms of sessions to look at how we look at the exam preparation by looking at different exam papers going through them from question one up until the end covering everything so that then you are prepared at the 8th of November because I'm going to assume that Unisa is going to stop the sessions especially the group sessions very soon as the exam starts because the exam starts in October and my last session is on the 27th but that doesn't stop us from engaging whether Unisa stops the sessions and say we no longer offer the sessions so we can still continue with our our sessions but we can arrange for that. So I'm going to assume that everyone who is here is also on the WhatsApp group if I know more information then I will share there but other than that then I will see you next week. Same time which is six o'clock until half past seven. Thank you very much this is very helpful I really appreciate it. You are more than welcome it's my pleasure. Are there any questions? If there are none then happy learning enjoy your evening I will see you next week. Bye.


Video equipment rental costs paid for by peep code screencasts. All right, so this is another lightning talk, 10 minutes to go over Ruby and Virtual Team. So, cool thing about our community, our group, is a lot of us are freelancers. A lot of us aren't an enterprise, so we got to rely on a lot of different tools to be able to communicate distributedly, if that's a word. So, without further ado, I am not with Netherland, and I wanted a cool graphic, so we put this deal in. So my name is Jim Mulholland, I'm with a small company called Squeegee out of... I'm out of Houston, we're kind of distributed all across Texas. We have one guy in China, who's coming back next month, or I guess this month hopefully. We have clients in Louisiana, Arizona, Austin, Dallas, so we're obviously distributed. So, who's doing this? Obviously, what we're doing, telecommuter, project managers, designers, et cetera, et cetera. So, priority number one, you need to talk to people. Can you hear me now? To do that, we use a lot as a tool called freeconferencecall.com. Completely free, no idea how they do this, what their business model is, but they give you a phone number, and you get to keep this phone number, you call in, you get an access code. We use that for our morning scrums, we have a scrum every day, every morning at 8.30. We all call in, discuss, and then we also use the same conference call number for all of our clients. We've had the same number for a year and a half or so. So, it's been very nice to use, and it doesn't cost us any money. Again, no idea what the business model is. I think it's some hokey about their snaking money from the government for using it and go some lines or something, but not important. Secondly, so one of the problems with freeconferencecall is that it's a long distance number. So, it's like our number is a 616. So, unless you have unlimited long distance or you have a ton of minutes on your cell phone or you have voice over IP, like I do, you use something like Skype. So, actually a guy in China uses Skype. The other guys in here uses Skype. It just makes it easier to call in that conference call every morning and any other meetings that we have.  It's actually the guy in China. I was going to drop here, no money. So, FYI. Communication. So, quote, made famous by Guns N' Roses from the movie Cool Hand Luke. You guys may know it. What we got here is failure. So, anyway, that's all about communication. So, for main communication, we use emails like most people do. We personally use Gmail for your domain. Very easy to set up. We got our own sqg.com domain email address. Again, free. We set up a few clients with it. You can use the Gmail interface, which is, I think, better than most client interfaces for email nowadays. So, great for asynchronous messaging like we've been using for years. Not so great for real-time communication. For that, we can use the campfire, which is 37 signals. We have a campfire for each client. So, we just hang out with tons of campfires on our desktop at any one time just to do a real-time chat to answer questions that a client may have or just to discuss development, et cetera. Other tools. I am. We still use I am. Google talk. I'm the same messenger that kind of stuff. He's going to do some offline. Hey, what's going on? Type of chat. IRC, popular for open source. Conferences like this. There's an IRC channel for this. Not so good for the 9G crowd. Some special setup and some confusing, but other options. Project management. I love deadlines, especially. I like the swooshing sound they make as they fly past. Unknown office. So, for project management, we use Red9. Greg Pollock actually mentioned earlier. We've been using it for about a year and a half and really enjoy it. It's a pretty active community. John Flea Blang is the main developer. He's a madman in terms of code that got busted out of code constantly. So there's always new features coming on. But we've had really good luck with it. It's a lot of deliberate issue tracking. Release management integrates with Git and subversion pretty well. And then it has lookies and forms, document ads and all kinds of other goodies. So we've had good luck with that. We also actually use Basecamp quite a bit. It's actually more of a client choice. A lot of clients use Basecamp for messaging. And other file management, that kind of thing. It's popular. Track was mentioned earlier. It's the Python open source. It's kind of the de facto open source project management tool still. We've tried it a couple times, but haven't in a while. Lighthouse is kind of a new kid on the block. It's been around for about a year. I remember actually talking to Rick Olson about it this time last year at this conference. But he developed Lighthouse. It has integrations with Git. A lot of people, Rails is on Lighthouse now. A lot of other people are using Lighthouse. Unfoddle is very similar to Lighthouse issue tracking. We use that before we moved over to Redmine. Also, Lighthouse and Unfoddle both hosted apps with a free account. But the more you use it, the more you pay. That kind of deal. Sharing your work. That's kind of a funny quote. People say New Yorkers can't get along. Not true.  One guy took the tires, the other in the radio, the other guy took the engine. David Letterman. First thing in sharing is GitHub. It's been great getting GitHub both. We converted to GitHub. Probably back in February as far as that beta and GitHub. Just makes source control and just sharing ideas and code with the team very easy and straightforward. Other tools. Obviously, Subversion is still around. You wouldn't know it's talking to most people around here. And Google Docs is actually... We still use Google Docs quite a bit. Actually, the original version of this presentation was in a bar camp used in a couple weeks ago. When and I actually both used the presentation of Google Docs to go back and forth and create this presentation. So it turned out to be pretty useful. Tracking time. If you're here and I'm here, isn't it really our time? That's time to raise my hand. One or two people. Anyway, that's just going. We actually tried tons of tools. We ended up developing our own. So this is actually us. Take start. But it's just tracking time, reporting time, training, charts, graphs, that kind of thing. Calculates invoice and payroll. And... Okay. How many seconds? It says I got a minute seven here. Anyway. We actually use QuickBooks online, but I couldn't do a screenshot because you can only use it on Windows machines. So we have special machines that use the QuickBooks to do our invoices and stuff. FrenchBooks, Harvest, Cashboard, all full featured apps, and then Slim Timer, Toggle, or other timer apps that we've tried in the past. And last slide, actually, is also considered. If you have an Asian guy on your team that wants to go find his roots in China for four months, make sure he works to your time zone. Because the asset we did, it's actually worked out good. Other than his left sleep to privacy, and I think the hazes get to him and he's starting to get lots of headaches and stuff, but he's still working on our time, and it's actually working out really good. And that is it. There's all of the resources, and that's me.


This is a video on how to use the Central Limit Theorem, which uses ideas regarding the normal distribution. The population of weights for men attending a local health club is normally distributed with a mean of 182 pounds and a standard deviation of 26 pounds. An elevator in the health club is limited to 34 occupants, but it will be overloaded if the total weight is an excess of 6,664 pounds, which is dangerous when an elevator is overloaded. Assume there are 34 men in the elevator, what is the average weight which the elevator would be considered overloaded? So average weight, that means I have to take the 6,664 pounds and divide it amongst the 34 people. If the average weight was 196 pounds among those 34 people, then the elevator would be overloaded. Now I want to know what is the probability that one randomly selected male or one data value will exceed this weight. So the probability that a male, which will represent by x, will be more than 196 pounds. Remember we use x to represent data values. So in my distribution, my mean is going to be 182. I plot 196 over to the right and those that are more than it are those to the right of 196. So we find the area under the curve to the right of 196. Keep in mind that the mean is 182 and the standard deviation is 26. This is what we'll use in Google Sheets. So the mean is 182, standard deviation is 26. Lower bound is 196, upper bound is infinity or six nines. You can do more nines if you want, but it really doesn't matter. And you get about 0.2951. That would be 0.2951. That is the probability that one male will exceed this weight. But one person exceeding that weight may or may not affect the fact that the elevator is overloaded. It's if all of these people, all 34 of them exceed the average weight that creates an issue. So that's where the central limit theorem comes into place. So assume that 34 male occupants were in the elevator and were randomly selected to find the probability that the elevator will be overloaded. So what the central limit theorem says is that out of the population of men, if I randomly select 34 and I find their average, I'm going to get some sort of value. Then I take another 34 men randomly selected, take their average, find their mean, X bar. Take another 34 out of the population and then take their average. Another 34. Take their average. Another 34. Take their average. The averages of these averages, the mean of these sample means, follows a normal distribution. So I call this my sampling distribution. It's a distribution that talks about the sample means. It's all of my collections of 34 males. I'm interested the probability that these collections of 34 males will be greater than 196. So some things you have to know. The mean of my sampling distribution or the mean of my sample means is the same as the mean of my original information. So that would be 182. What's different, however, is the standard deviation of the sampling means. The standard deviation of the sampling means is the original standard deviation divided by the square root event. Also known as the finite population correction factor. Now my original standard deviation was 26 and in my sample size is 3426 divided by square root of 34. I'm going to go ahead and say that is 4.45896. The more decimal places you keep, the more accurate your answer will be. For my sampling distribution, I have a mean of 182. And I want to know the probability that the means, the mean of the sample means will be more than 196. So area to the right of 196. Using Google Sheets, your mean is still 182. Your standard deviation though has now been adjusted to 4.45896 thanks to the central limit theorem. Your lower bound still the same and your upper bound still the same. You get .0008. You can keep more decimal places if you want, but 0.0008 will be what we use. So this is the probability that the elevator will be overloaded. Once again, I had to use the central limit theorem because we weren't looking at just one male occupant of the elevator. We were looking at a sample of 34 that their average weight would be more than 196. I had to use my sampling distribution, which had the same mean as we previously used, but the standard deviation does have to be altered slightly. So if the elevator is full on average nine times per day, how many times will the elevator be overloaded in one leap year or one non leap year? So that would mean 365 days in a year. So if you have 365 days in a year and the elevator is full nine times per day, that means in a year, the elevator will be full 3,285 times. And of those 3,285 times, it will be overloaded 3,285 times the probability found in the previous section. It's not very often, but hey, let's see how many times per year the elevator is predicted to be overloaded. And you do the multiplication and you get approximately three times per year. Approximately three times per year, the elevator will be overloaded. Is this reason for concern? Yes, we don't want that elevator to be overloaded at all during a year's time. We want our number as close to zero as possible. Three times per year is a reason to be concerned because if the elevator malfunctions three times a year, I'm pretty sure that business or wherever that elevator is, is not going to be staying in business. So yes, there is reason to be concerned here. Thank you for watching.


Thank you very much. Hey, my name is John Fortin. I'm a Senior Systems Architect with MarketAmericaShop.com. We've been using OKD for quite a while, but let me talk a little bit about what MarketAmerica and Shop.com is. So Market America, we do multiple things. We have different brands. We have isotonics, motives. We have many different ways of bringing people in. We do jewelry, all sorts of stuff. Shop.com, well, and Market America, we're out in eight countries, five different languages, localizations for every country in a language that we have, where we have businesses. With Shop.com, we have approximately 87 million products worldwide, thousands of views, millions of views per day, personalization, all sorts of things that are back in that make Shop.com a very individualized service for people who come in too. What to know about Market America? We were founded in 1982 by JR and Lauren Rittinger based in Greensboro, North Carolina. Last year, we had about 850 million plus dollars revenue worldwide, eight market countries, U.S., Canada, United Kingdom, Australia, Hong Kong, Taiwan, Singapore, and Malaysia. We have about 850 employees worldwide and over 300 IT employees. For technology stack at Market America, we have a mixture of commercial and open source, ranging from Red Hat Enterprise, Microsoft Windows, vSphere, which we do all our virtualization on, Market, Universe, Database, SQL Server, Adobe Cold Fusion, and a lot of the open source stuff that everybody's heard of, Centos 7, Java Python, we have a real-time metrics and reporting stack. We're using DeVolte, NIFI, Druid, SuperSet, real-time monitoring with Prometheus and Grafana. We use Kafka as our message broker in Pub Subservice. And of course, Okade 3.1 and 4, which is the community-supported version of OpenShift container platform. So our microservice journey began back in about Q4 of 2016. We're starting to look at breaking apart some of our monolithic applications and trying to get into more of a way to more quickly move applications and services out for our applications and users. We're looking at containers versus standalone apps and containers had a lot of advantages for us. Easy to build, depending on the complexity of the services. We looked at storage and container repositories like Docker Hub and Artifactory. Easy to deploy individual containers, both for development and potentially for testing and staging and production. And we looked at multiple different container technologies, you know, standalone doc, standalone Docker, which we found great for, you know, individual building and testing on somebody's laptop or on individual servers. We looked at Docker Swarm, which honestly never really took off back in 2016-2017. And we looked at Kubernetes, which, you know, obviously, you know, looking back to 2016 from now is obviously the clear winner for the orchestration stack. Very command line oriented, which, you know, if you're familiar with Kubernetes, you know, everybody here is probably pretty familiar. And there can be a fairly high learning curve, you know, all that EML that everybody loves. And really, you know, we're like, yeah, we like Kubernetes, but how can we go, you know, how can we, you know, move forward and make it a little bit user friendly. Again, you know, Kubernetes is great, declared a configuration, tell us what you want, and it just does it. Namespaces, but not everybody loves the command line. And all that, again, all that EML. So back in that time, we started looking at OpenShift Origin. We had, you know, done a big pivot back from version two to version three and really went into the containerization, Docker containerization framework. And we're like, hmm, is that going to give us what we want. So we looked at it. I played with it for a good little bit, learn how to install it, had some people internally test with it. And we're like, we like this. Nice web based GUI for many activities made, you know, very user friendly for our developers.  We like the enhanced security with roll bait authentication and cluster and project based security context. And those were all winners for us. We did like the additional capabilities based based or built on top of Kubernetes, you know, the build configuration so we were able to easily build our applications on deployment configurations templates, deploying a new version of an application, just by pointing at a get repo and pressing a button that were that was very advantageous to us. And we were able to build the fact that we were able to build applications using a containerized process in this case, almost always as to why your source to image containers. They'll for many languages, you know, Java, you know, Python, pretty much whatever you wanted, and we were able to take, take advantage of that. You know, build your own repeatable process. If it didn't, if the containers that were out there didn't quite meet your meet what you need, you're easy to modify. And we're able to integrate OKD into many CICD tools such as Jenkins, which, you know, we use internally via the REST APIs or CLIs.  Yeah, OC, you know, which is the equivalent of QBCTL. So I was Q1 in 2017. We were pretty happy with OpenShift, seeing to fit all the, all the things that we wanted in a containerized platform. So now we had to figure out how are we going to actually use OKD? How do we move our processes? How do we move our applications? How do we move our services? How do we build our services? What do we have to do in order to make this work? And it was a very iterative process. We had to change how we thought in terms of building from Docker files, you know, or from an application build to a build from source process. We had to look at our source repo design and so we could be consistent between different applications so our build process would be consistent. One of the harder things we had to do was remove hard-coded environment information from our repositories. There'd be environment variables, one for dev, one for test, one for production. And then when things would get built, they'd just say, OK, build test, build dev, build prod. We didn't want to do that anymore. We wanted to make sure that we had one image that was going to be across the board. One image to rule them all, so to speak. No environment-specific images. Everything in those images was to be configured by environment variables or config maps or some other thing that we're passing to the image. The other thing was how do we deploy our OCD services? And again, we only want to build in dev, no building in any other environments. And the non-dev environment pull-from images that we put into our repository that were built from dev. And again, environment configuration is provided via environment variables in a deployment config or deployments, config maps, which are preferred because they can be shared amongst different applications. And like I said, this was a very iterative process. We started with a very simple config map in the template and say, OK, can we deploy an application? How do we have to modify? Do we need to put in various variables? Are there defaults that we have to use or that we can use to make it simpler? So that took a little while, getting the application teams used to them. How do we do an ingress? How do we actually access these services? So, yeah, that took a little while. So once we did that, once we figured out how to do our builds, how do we deploy? OK, let's start moving things through our process. So we built a few clusters. We have a dev and testing cluster. Currently running on OKD 3.11, although we do have a 4.7 cluster that we are migrating to. Three masters and three compute nodes that will probably grow. Especially when we move to OKD 4, we start adding some of the features that we want to add for OKD 4. We do have a production cluster in a separate data center, sized pretty much the same. Again, currently on OKD 3.11. And we are working on moving to OKD 4 after we move to Dev and Test. And we also have a QA environment where we run our Selenium grid testing cluster. This is where we do all of our integration and regression testing. We have a whole team that writes scripts to try to go through and test every conceivable piece of our application stack. Blackbox testing, whitebox testing. Can we make it fail? Does it still work after we do a deployment? All that kind of stuff that we expect QA to do. And that's actually our first cluster to use on OKD 4.7. Each team has their own development and staging or test integration project. Deployments in non-Dev are migrated to staging and production via Jenkins via our deployment team. Application teams do not deploy outside of Dev. We have a set of application templates that allow us to have consistent creation of applications. Again, it's almost a point and click. Click on the template, put in your Git repository and a couple other environment variables. And within a couple of minutes, your application is ready to go and test. Click on the link or click on the URL that we create, point to it from wherever you're pointing to and go off and test. And this allows us to be very, very quick in our development cycle. If we need to make a change, we update Git, commit it, and then click the button again, say redeploy, and it goes out. Rebuilds it, redeploys it to Dev and lets us test. Once Dev is done, deployment team says, OK, we're going to deploy it to staging. They do the testing and then it gets deployed to production. Those are still manual processes. We do not have a complete end-to-end deployment process from Dev all the way to production. We do see us getting to there eventually, but we are not there yet. Applications are front-ended by a NetScaler load balancer via node ports. This is something that's going to change once we get to OKD version four. We're going to be bringing in Istio Mesh, which is allow us to do all of the things that we're doing on the NetScaler to be able to do it natively inside of the cluster and not have to do the setup in the NetScaler. We're kind of excited about that. We're looking to see a performance gain along with a whole lot of additional metrics that we currently cannot capture. So Q4 2021, where are we now? So we have deployed over 200 services in Dev. We are probably adding one or two services a week, if not more, although this time of year we're kind of in a freeze. Primary languages are Java, Python and Node.js. We have a very active deployment and development teams on all three of those. We have over 170 services deployed in our staging test environment and over 170 services deployed in our production environment. As I mentioned before, we are in the active process of migrating to OKD 4, in particular 4.7. QA has already migrated. That was in second quarter of 2021. Do you do several architectural changes? The services that we are migrating are taking a little bit longer. Naming convention migrations, deprecation of the NetScaler to the ingress. That configuration is a little bit more complex, so it's taking us a little bit more time to get that nailed down. And we also ran into the Jenkins OpenShift plugin deprecation between OKD 3.x and OKD 4. So it broke some of our automation and we're still going through that in order to fix that and get our automation processes running as we expect them to run. So that's kind of the basis of what we're doing with OKD at MarketAmericasShop.com. We are using it in production. It's been very, very successful for us. We've had very few issues and the automation and capabilities that has given us have forexceded our expectations. But there are some caveats that you need to really know about using OKD in production. The most important one in my mind is the fact that there is no official support. You cannot call a red hat and say, I'm having a problem with OKD and I need some help. If you need that level of support, you really need to have the enterprise level of OpenShift, open container platform. There's no one to call it 2AM. Community resources such as Slack and GitHub are available and are very, very active. But there's no guarantee that you're going to find a solution in those. And there's also community members provide assistance at their convenience and with the amount of time that they have. We're all busy. Sometimes you have time to look at an issue. Sometimes you don't. Sometimes an issue may look interesting and you want to dig into it. I don't have time for that right now. The community people are great and they're great resources, but you really have to be able to, to an extent, support yourself. You need to become proficient at using the logging tools within OKD. If you're doing an install, you'd be able to use OpenShift Install Gather. If you're having closer install problems, you must be able to use OCADM. You must gather in order to get logs if you have a cluster issue after install. If you open something in Git or if you open something on Slack and you don't provide that, you're probably going to get a gentle reminder that, yeah, you probably need to provide the logs unless it's something, you know, pretty simple, you know, for a solution. You got to get used to using Red Hat Bugzilla. If there's a significant issue within OKD, it's probably something that, you know, it's going to affect OCP also. And if you open a Bugzilla, you're much likely to get some action from a Red Hatter. Because if it's a bug in OCP and in OKD, much better chance of being, of it getting fixed relatively quickly. Self-help is a must.  You know, is there something similar to what I'm doing? You know, is there something that other people have had? You know, if you're good at IT, you know, use your debugging abilities to try to figure this out. It'll make you better. It'll make you more helpful in the community also. And you got to test. If you're planning and using OKD in production, you've got to test. You've got to test upgrades. You've got to test installs. You've got to test pretty much everything and not on your active clusters. You've got to have a sandbox. You've got to have something where you can install the current version or whatever you're using in production or test or whatever you're going to be upgrading. You've got to be able to install it. You've got to be able to run through that process and see if you run into any issues. There's nothing worse than trying to upgrade your cluster and then finding out that it's stuck. And it's something that you could have caught if you were to test it in a test environment or a PLC environment. Another thing to understand is that OKD and OpenShift Container platform run on different operating systems. They're similar, but they're still different. OCP runs on Red Hat Core OS, which is based on Red Hat Enterprise Linux, very well supported and well maintained Enterprise Linux distribution. OKD runs on Fedora Core OS, which is based on Fedora OS, another well maintained operating system that people love Fedora. The issue though is that Fedora and Fedora Core OS have a very fast pace of development. It changes very, very quickly from week to week, bi-weekly, bi-weekly, whatever. And OKD can be very, very sensitive to changes and the underlying Fedora Core OS. And sometimes with unintended instability. We've had it happen multiple times since really we went stable in April, I think it was. Usually the fixes are relatively quick, but you got to be aware.  Don't assume that your upgrades are going to go smoothly. You got to test an environment first just to make sure. Not all features in OpenShift container platform are available in OKD. Many operators are not yet available in OKD. And there are features in OCP that require Red Hat subscriptions, and thus will not be available in OKD. There may be equivalents that are upstream, but you may require some research on your part or discussion within the community. Once a new stable version of OKD is released, that previous version, there are no longer stable releases created. So we just went live with 4.8 not too long ago. So there will not be any 4.7 stable releases. There will still be nightlies and stuff, which may have additional features and stuff, but no more stable releases for the 4.7 branch. When 4.9 becomes available, 4.8 will end stable releases. You'll have a last stable release and then that will be it. Downside with nightly releases or nightly snapshots that they're only available for about 72 hours. So you have to, you know, either you can copy them off somewhere if you need to use them, but realize that those are very, very quickly updated and removed. As I mentioned before, having a sandbox environment is vital. You've got to be able to test installs. You've got to be able to test upgrades. You've got to be able to test if you're putting in some type of new operator which could have impact on the rest of your cluster. For instance, Istio Mesh is very, I'll say intrusive, you know, into the networking of OKD. You may not want to install it initially in your production environment. You want to make sure you have someplace where you can test it safely, be able to uninstall it, reinstall it, reconfigure it, break it. And be able to recover without affecting, you know, the rest of your clusters. You also need to know how to recover from failure. Unfortunately, things do happen. You need to be able to understand how to back up your Etsy database, your Etsy databases. You need to understand how to recover a master in case you lose one. Kind of the basic things that you would do with any application. You need to know how are you going to recover from it if something goes down and breaks.  That's an important process within, you know, of your OKD environment. The other thing to say is become engaged, you know, in the OKD community, you know, hopefully prior to an issue. It always helps if somebody already knows your name and knows, you know, what you've been going through and stuff. They may be more inclined to reach out and say, OK, I see you're having a problem. Maybe you can try this or do that. Sometimes if you're, you know, doing a cold problem and nobody knows who you are, it might be, well, let's see what happens. And maybe somebody will help. Become a tester. You know, learn how to test nightly and stable releases as you can. It helps you. It helps other people. It helps you, you know, helps identify issues that may be in your environment that nobody else is seeing. So it may be a bug that it's a bug, but nobody else is doing it because everybody else is installing a Zsphere and you're, you're installing on, you know, some bare metal or something. It's happened. But, you know, we're always looking for new testers. Testing is great. Keep an eye on the get repo discussions and issues. You'll look at those. See what, see what is being identified. Are there issues that you can help solve? Are there issues that you're interested in?  You know, it's like, oh, how does this work? How do I build this? How do I build, you know, a very piece in component of OpenShift because I think there's a bug.  Join the Slack channels. You can learn a whole lot just by watching the discussions that are happening in the Slack channels. You don't even have to be involved. Join the working group. Working group is what is basically trying to keep, you know, OKD going. What is our, what is our focus going forward? How are we going to get there? How can we get more people involved? You know, Diane does a great job with keeping the working group going. We'd love to have more people, more people to marry her. So feel free to reach out and join the working group. And that's pretty much what Market America is doing with OKD. Our journey, like I said, started in Q4 of 2016 and our journey is continuing. Q4 of 2021. We are looking forward to migrating everything to OKD 4.8. We're looking forward to all the enhanced capabilities that we're getting from OKD 4, all the way from metrics to scalability to automation. Ease of use. The difference between 3 and 4 is amazing. The difference between 4, you know, 4.7, 4.8, you know, and 4.9. There's so many features coming down the road that I'm really excited about it. I can't wait. I hope, I hope that, you know, we get more people that are involved and more people are excited about OKD going forward. That was probably pretty short and pretty quick. I probably talked too fast. But thank you very much and I'll be happy to take any questions as we go forward.


I'm Christian Taylor on the Mustangs Force Minute, joined by head coach Dominic Dawes. Coach, unbelievable win today and overtime, three to two thoughts on your team's performance. I think that was honestly our best game of the year, just in terms of energy. The second period was by far our best period of the year, but just handling the emotions, handling the highs and lows, I mean it was definitely a faster pace, higher tempo game than the last couple of games. And I think that's, we struggled a little bit early on to just kind of catch up a little bit, but I think once we caught up we were good. So obviously you went down two goals to one, found a way to come back, then win it in overtime. What does that say about the resiliency about your team? That's big, I mean that's what we've been searching for and you know you can, this has by far been our biggest game to this point and to respond in the last couple of minutes like that. And even the group that ended up tying it up was the group that gave it up. So for those guys to go out there and show the, I think they all kind of contributed to the goal, to show that resiliency is big. So final stretch coming up month of February, how do you continue this hot streak? Score more goals than the other team. So that's that easy. So coach, congrats on the win in overtime. Big win facing against your former team back this next weekend. Good luck. Yeah, thank you.


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The novel coronavirus disease, COVID-19, has had a serious impact on people's lives for more than three years now. The severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, has caused significant damage to people's respiratory systems and multiple organs. Despite the full understanding of the pathogenesis of COVID-19, there is still a lack of effective and specific treatment for it. Mesenchymal stem cells, MSCs, or MSC-derived extracellular vesicles, MSC versus, are being considered as one of the most promising candidates for treating severe COVID-19. These cells have demonstrated their potential to treat the virus, and they have already been used in clinical trials. MSCs have shown their ability to modulate various immune cells and organs, which makes them ideal candidates for treating COVID-19. They can help reduce the severity of symptoms, improve patient outcomes, and even potentially cure the virus. Additionally, MSCs have the potential to regenerate damaged tissues and organs, making them a valuable tool in combating. This article was authored by Yuyin Feng, Peijun Lao, Longxian Tang, and others. We are article.tv, links in the description below.