 I think the recording has started and we are covering, I just like it to start here, October November 2020 Excel paper. So we can start with question number one without any time wasted. How would you answer this question? Most research projects follow this same basic process. Mention and explain the third and the fourth step of the research process. It's two marks, so it means one mark each for mentioning it and explaining it. I'll give it a try, I said the third step is about data collection. It looks at the way the data needs to be collected in order to give you the information you need to answer whatever your research question is. Yes. And then the fourth step, I said this is about data analysis and using statistics to get meaningful conclusions to answer your research questions. Would I get two marks for that? Yes, you get two marks, so remember to mention and explain, mention and explain, yes, two marks. But I get two marks for what I've said. Yes, you get two marks. Yes. Okay, thank you. Anyone who has another explanation, you can all tell me, all four of you would have said the same thing as what Karen has said. I said, but it's similar to what she said. I said the third step is data collection and that is how the information required to answer the research question will be obtained or how the data will be collected. And the fourth step is data analysis, where you analyze the information that you collected in order to get a solution or make sense of whatever your research in order to give the solution. Yes. That also will give you two marks. Okay. Do you want more? Yes, if you have a different answer to the two. Okay. Let me just open. Because you would have written the answers down. So I want you to read to me what you would have written. Okay. Just looking because I put it on here. Just should have just kept the paper. Okay. So data collection addresses the way in which information. Sorry, sorry. The way in which the information needed to answer the research question is obtained. And then fourth step, data analysis. Data analysis describes the way in which we make sense out of the information that has been collected. Yes. You can also include ways like techniques because you're using data analytics techniques. So you can just say your data analytics will be the use of data analysis techniques to answer research questions on the data that has been collected. And that will also give you one mark. And then all the statements you have said about data collection, there's nothing I can even add on those ones. Okay. And question two. So question two, they give you the frequency distribution table. Then they ask you to calculate what is the percentage of students that fall below a high risk score, in other words, determine the percentage of the score of 66. I don't know if I'm wrong. But for this one, it apparently was an error. So I don't know. I didn't, I don't know. Maybe it's just me. But for this very module, it was apparently it was an error on the question. Because I'm doing this module and this percentile is in the textbook, but it's not mentioned anyway in the study guide. Yeah. No. It is mentioned in the study guide, I think, with the reference to your textbook. So you need to be able to have the textbook in order for you to be able to answer this question. There are no errors or coincidences in terms of this. So if you go to your past exam papers, there are formulas in because I think in the 2020, you were given this question paper or the students who wrote this question paper. It was an open book. So they assume that you have your prescribed book, you have your study guide, you have all the resources in order for you to be able to answer the questions. But if you go to your past exam papers, if you don't have a prescribed book, there are formulas that you can use. And in order for you to calculate the percentile rank, you need to use this formula, which is the percent below, which is the percentage below plus your score, which is the score that they have given you minus the real lower limit divided by the class interval width. So you need to determine what is your class interval width. Multiply that with your interval percentage. So let me hope that I will remember what the formula looks like. So in order for you to find the percentile rank, you need percentage below. I think it is plus, plus your score minus the real lower limit divided by your class interval width and multiply by interval percent. So how did you answer this? So because they gave you the score of 66, the interval or the class interval that contains the score of 66 will be the class interval of 65 and 69. So it means we're going to use most of the data that comes from there. We're going to use the percentage. We're going to use the real lower limit. Remember, if you have this information like this, what will be your real lower limit? Your real lower limit for this will be 64.5. And then your class width is the difference between the two. What is your class width? 69 minus 65. Is it not four? Yes, it's four. So you would have used four. And the percentage interval will be the percentage. So that one. So that will be your interval percentage because that is for that interval. And your percentage below will be the one below the score because your table is upside down. If you look at it, it's at the bottom with the lowest and then it goes up. So don't get confused with all this. So we're going to take the one below, not the one above. So the percentage below is 49. So that will be our percentage below. So let's substitute into the formula. The percentage below is 40 plus our score. They gave us the score is 66 minus our real lower limit. We've been finding 16. 4.5. Everything divided by our class width. And multiply everything by the class interval. Our interval is 12. Sorry, that's the other thing. We need to be very careful here. You can either use 0.14 and here it will be 0.12. Because these are percentages. And then the answer we can always convert it to 0.12. So if we use like the 12,000, all that, you might find that you get 100 and something percentage at the end, which is not what we want. So in order to answer this, first calculate this 4 minus equals divided by 4 and then multiply by 0.12. 64.5. Equals 1.5. Divide everything by 4 and say equal and you will get 0.375. And then equals. Oh, sorry. Did we do equal? Yes. Use your calculator. Equals 0.5, multiply that by 0.12. And you get an answer of 0. Equals 0.5. That would be our answer. Because this says we need to find the score that falls below. And if we use the frequency percentage, it's not going to be the right. We need to use the cumulative frequencies. Sorry, my bad. We need to change this to cumulative frequencies. All of them? Only the percentage below is the cumulative frequencies, not your frequency. That would be that 0.82. And 0.82 plus the answer that you got would have been 0.0445. Plus 82. And that would be 82.04. Am I doing? Sorry, 0.82 plus 0.045. What did you get? 0.865. You can multiply that by 100. The answer that you would get, multiply that by 100. And that will be 86.5. Because that's what they want. What is the percentage of students that falls below the high-risk score? Determine the percentile rank of score of 66. That would be 86.5%. Because all of those below that would have scored below 66. So we just need to make sure that the percentage below is always cumulative. The interval percentage is your frequency. And then you will need to calculate your real low-animate. But I also want... So always if they say full below the high-risk score, then we're looking at the cumulative frequency? Yes. Okay. So always remember they can ask you to calculate either the midpoint or the percentile rank or the score. You will never know. Remember in the May, June exam paper, we didn't have to calculate this. But in the October, November now that we are looking at, they're asking you to calculate this. So you need to know how to use all of these formulas as well and use them correctly. And use them correctly. So everywhere where you see percentage below, that will be your cumulative percentage. Interval percentage is your actual interval percentage, frequency percentage. This will be your percentile rank. So they will have given you a percentile rank if you calculate in the score. Your real lower limit, you need to be able to know what the real lower limit is. The same with the midpoint. We know what the real lower limit is. But you need to know how to calculate your real upper limit, which you just added another 0.5 at the end of the upper limit. Okay, so let's move on to question B. What is the score at 60th percentile? What will be the score at 60th percentile? So now, 60th percentile would have been between those brackets because it will be between those ones because 68 percent of the values got below 59. There's got below 59. So our 60th percentile will fall somewhere between that and below. So since we have established that based on the 68 percent cumulative, we can then use the formula to calculate the score. Remember, we need to remember this. Please write it down so that you can tell me when I write on the other side that I write the right one. Is your real lower limit plus your percentile rank, which they have given us at 60 percent minus the percentage below, which will be your percentile below the interval that we are in, and your interval percentage multiplied by the interval width. So now, did you write it down? Yes. Yes, where did I write it? Okay, we can do it here. Okay, so the score, I'm just going to write score. Is your real lower limit, is it plus or minus? Plus. Yes, go ahead. For some reason, my hand is shaky, I can't write properly. So I can be able to write properly. My hands are tired, I've been hitting so many goals. Divide by? Interval percentage. Interval percentage. Multiply by interval width. Inter... Width. Okay, so some of the things we already have established them in the previous one, how to find them so we can do the same. So since we know that we are on this percentile with an error day, what is our real lower limit? 54.5. It will be 54.5. In the lower limit, you always move is 0.5. If they're giving you this information in a decimal format, it makes it easier. But because they give it to us in a whole number or an integer, then we always need to find the real lower limit there. So that is 54.5. So the upper limit here would have been 59.5. Plus our percentile. 60. 40. Minus the percentile goal, which would be the one. 48. 48. 0.48. You can use the whole numbers if it works the same way. I don't foresee any problem. I always like to put the percentage to a decimal percentage. And the interval percentage is the 8.5. Which is 0.5. 8. For that interval, that we are in X, which is 53. And your interval width is then minus 5. Somebody, I'm not sure whether you have your TV on or are people talking in your background. Right, so now let's calculate. So let's see everyone so that we can all get the same answer. Let's see. Remember to start with the fraction one first. 0.60 minus 0.48 equal divide by 0.68. I'm going to get a minus here. 0.6 minus 0.48 equals divide by 0.68 equal. I get 0.1765. Multiply that by 4. And I get 0.7059. So let's write that down. 54.5 plus 0.7059. And the answer, yeah, remember this is the score. It needs to be a whole number. So we can say 54.5 plus 0.7059. I get 55.0 from number. So I'm only going to get at 55. 54.7. You get 54.7059. You get 54. How do you get 54. Because I want no one to get 54. Please check. Oh, sorry, yes. Wrong button is pressed. 55. So you get 55. So your score will be 55. I'm sorry. Disappear. Your score will be 55.255. We can just say it is 55. I'm going to leave it as 55. So that then we don't have any decimals in between. So therefore it means the score where it will be 60. 60th percentile, it will be everybody who's got 55. They would have. And this is it. So if you can just explain to me if you got the 0.68 from. The percentage interval, is that now from the frequency, cumulative frequency one on the right hand side column? Is that 0.68? Yeah. Remember we say at 68% will include any score below 60th will be from 68 because it's 68 and 48. So it means 60th, even if we would have said 50th percentile, it would have been between those two. So you look at the percentage that you are given, look at the cumulative percentage. And if it falls in there or below that, then you're going to use the interval, the class interval that that's the, the central rank is in. Okay. And if they would have said 40th percentile, then we would have used 50 and 54. If they would have said 75% time would have used 82. Because 75. So we would have taken the one above. Okay. Thank you. And that is for two max to do all this. So three max and two max for all this calculation. Your lecturers are very stingy. Okay. So now we are in session three. How did you answer question three? We have employer A and employer B with their distribution. So we know that this Y exists, represents the number of entry level managers. And the X exists represents your annual income. So income entry level. So when you answer this question, you need to take into consideration income and entry level in your explanation. So in the graphs below, I've already read that Y exists represent the number of entry level managers in the organization, while X represent the reported annual income of the entry level managers. As a graduate, if your choice was based on income, which employer would you opt for? Motivate your choice. Using the skewness and the ketosis of the graphs for an explanation. How did you answer the question? I said A, maybe I'm wrong. No, don't worry about being wrong. Remember we're here to help. Okay. So I said in the exam. Okay. I said employer A and then I said, and then I said it's positively skewed. Sorry. And then I said it's positively skewed. Hello mama, hold on. Okay. And then I said it's positively skewed. And then I said, the more, then I said also because the, gosh wait, let me see, because the graph, the money keeps increasing and is more higher. I don't know if that makes sense because it seems, and isn't it the entry level is on the Y, sorry, the money is on the Y and then the X is the weight. I'm looking at the wrong graph here. Yes. The income is on the X axis, right? And then the entry level is on the Y axis. So I can see that as more managers get to the entry level, the higher the salary. I'm also not sure of my answer, but I said I would choose employer B. And it was B. So I was so lost because I didn't know anymore. I was so confused. So I don't know. So I'm also not 100% sure of my answer, but I also said I would choose, I said I would choose employer B. Yes. I said that it's positively, some of the reasons to what Precious has said, is it Precious or Dina? Employer B, but it's positively skewed to the right, which means the positive and larger numbers, so the larger salaries are to the right. And so yes, it has more high and low values and the tail is thin. So it becomes flatter so that yes, I would choose employer B, but I'm not sure if my reasoning is correct. As long as you are able to tie back your reasoning while you're choosing employer B to how you will interpret the skewness and the ketosis of the graphs. Remember that. If you look at the two graphs, what do they mean in terms of the skewness? Even if I leave the skewness out, it means if you choose employer A, the more people with lower salary are in the far end, like the endless salary, because the tail is to the left. It is skewed to the left, therefore it means the majority of the higher people, more people are skewed to the left, whereas in terms of your employer B, the majority of people earn more money because they search based on the income. So you'd rather go and work for employer B than work for employer A. Because employer B, the chances are you will have more money. Anyone who has another explanation why they would have chosen employer B? I was also confused. I didn't even know how to. But I went with employer B. And I said the most population ends in the lower and middle range and the income is positively distributed. And the graph is asymmetrical in nature and the distribution is peaked. And the income distribution is predictive. I was not sure of what I was doing, but I don't know. In terms of the ketosis, you can talk about it in terms of the platyctic because if you look at the two graphs, employer A has the high peak, whereas employer B has the lower peak. So therefore it means more people actually also earn more money. And the salaries are not... Oh, the entry levels are not widely different compared to employer A because if you look at the employer A, it means the higher the entry level, the more salary you will receive. But yeah, alternatively in terms of this, the lower the entry level, the more even the salary you will get as well. You must remember that because if the tail is to the left, it means you will receive more money for even having a lowest entry level. Now, the chances of getting more money are high with the lower entry level, whereas employer A, with the lower entry level, you will receive low income. So employer B is the best option. So you go into... It's the remarks. That you choose employer B and then you give the reason in terms of the skewness of the data that it is positively skewed. You need to talk about... Like the same thing that we discussed previously, you need... But you also need to make sure that in your explanation, you talk about the income. So the lower the entry level, the more income you get because of the tail is to the left. So the higher the... Lower the entry level, the higher the salary you will receive. Because as well, if you look at the peak, it says for employer B, the higher the entry level, the lower the salary you will get. But the tail tells you a different story because the tail tells you that even if you have a lower entry level, you will still receive more income. So there is a potential because we're not talking about the risk here. So there is a potential for high income as well. Okay. And in terms of the ketosis, I can also speak about the employer B having a flat... Close to a... I don't know what to call it. Platyketic. Platyketic. Platyketic. Yes. Distribution, yeah. Yes. Yes. Hi. For some reason, my son placed on my phone. So I get good notifications all the time. I don't know how to stop them. They just pop up anytime they want. Okay. So for the remarks, you should be able to explain that. So I cannot write it down because you will not be answered for that question. But the best option is employer B. Employer B. And you just need to make sure that you motivate based on the tail to the... Tail to the... So at least the municipalities use this care because they seem to pay more than... Yes. No rank jobs. So in terms of the skewness, you will talk about the tail to the right, which is positively skewed, which means the lower the entry level, the higher the income you will receive. And then in terms of the ketosis, you will speak about plati, ketic, the calf is being flutter. Then it is, yes. Then employer B, employer A, sorry. And yeah, you will need to find an explanation there because now your explanation cannot just say it is a plati ketic, but it needs to relate to the income. You need to find a way of mentioning income in there because that's what they want you to do based on the income. So if you want to get full marks, so you need to also include income. Look at your textbook, how they explain it, or how they explain plati ketic, and also you get the notes that you did last week because I think we also did explain, I gave you hint on how to explain the ketosis. Okay, so now moving on to 3.2. Now we've got a bimodal data set. In the graph below, the y-axis represents the number of people tested positive. So tests, COVID, let's just write the COVID positive in a region while the eggs represent the months. So I don't even have to write the months, the period between March and October. Answer the following questions. So your answer should be in relation to the months and the COVID. So what does a bimodal distribution mean? So here you can also even give the textbook explanation because they didn't say in your own words. So if you don't know how to explain it, you can just use what they have explained in your prescribed book or in your study guide. Otherwise, we did discuss what bimodal distribution means. It means there are two peaks, right? But that is not an explanation of what it is. It means there are two areas or values that have the highest or that something like that. So you should talk about not only just about the peak because we can see that there are two peaks. So that doesn't mean anything. What does that those two peaks mean? It means there are two highest values in your data set or in your data. So in addition to you saying bimodal distribution means there are two peaks which refers to two values or two scenarios where there are two highest values or there are two highest values in your data set and you end it right there. You don't have to even include the information given or to remember in terms of you need various data we say if it's bimodal, it means two numbers appear more than the others. So there are two data or two values that appear more than the other values or highest or something like that, highest or most frequent because the mean means the most frequent value or the number that appears more than the others. So because yeah, we're talking about group data as well so there are two highest values. If there were three, we would have said it's a multimodal because there are three modes. Okay, so explain the infection rate in the region. Now, we have just explained what bimodal mean. They want you to use the same kind of information that you have just said and explain the infection rate in the region. It's for two marks. Can I try? You can try. The infection rate has two distinctive periods where the infections were very high. I'm using it. And that can also be the answer. And you can also remember, you can also mention the months that you think were the most highest numbers because it's for two months as well. So that is good. And you can say, and that occurred in April and in September with June, is it June? It looks more like March to May was high. March cannot be high. There is March. Sorry, sorry, sorry, yes. April. Yes, April. April and September, okay, yes. And you can also speak about the low. The low. And this was the highest. And the highest. So you can mention it that way. And you can say in terms, you don't have to say the infection rates there. You can also, depending on what type of questions you have, remember now this talks to the number of people who tested positive, you can include that in your answer as well. That the number of, there were two instances where the number of people tested positive were high, which is in April and in September and June had the lowest number of people testing positive. You can say it like that. You don't have to, but depending on the type of questions that they might have asked you. So just bear that in mind that use the statements given to you in the question or the statement of the question to respond so that they can have an understanding that you know what you're talking about. Because they told you that the Y exists is the number of people testing positive. And they told you that these are the months, the periods between this and this, so that you understand how to interpret this in relation to the information given. Okay. And that is for two months. Okay, so moving on to question four. A study was conducted by an American researcher. And so that the most companies, the employees are more engaged at work than unhappiest employees. A researcher in your company wants this validity of the study in an African country by firstly assessing the relationship between happiness and employees that engage with you. And she found the following. And you are given the very sum of X and the mean and the coefficient of correlation and the slope. And the intercept. The interest. I get confused with how you write the equations. So 4.1. Interpret the coefficient of correlation. 0.91. It's a strong positive. Strong positive correlation. Yeah. A strong. Positive. Relate. A strong. Positive. Relation. So you can say. Remember to always include the statements given. The relationship between happiness and employee engagement is a strong positive relationship or a correlation. Or there is a strong positive correlation between the relationship between happiness and employee work engagement. But you must also include that score. Because it's for two months. I don't know how you're going to incorporate it. So let's see. What if you just say R is equals to 0.91. Like first line and then second line. There is a strong. Okay. Yeah. So R is equal to 0.91. There is a strong positive, which refers to a strong positive correlation between happiness and work engagement. And another person can just. With the coefficient of correlation of 0.91. Or it implies that there is a strong positive correlation between happiness and employee engagement. And those who speak proper English, they can also come with their explanation in a very nice academic English. Because me I'm saying it in Juana. So I'm looking pretty from here. I'm English and I still can't say it in a nice way. Well, thank you for our meeting. I was thinking you did my mother tongue and then I... I'd rather trust your mother tongue than mine. Thank you. Don't know how to say it. It's also English. Oh, wow. So any language in South Africa is English. Okay. So what can you deduce about the nature of the relationship between happiness and employee work engagement score? Now, you should not even talk about 91% and the strong positive correlation. What does that mean? What does this strong positive relationship mean? Remember we did the same last time. What does that mean? The more the employees are helping, the more they engage in... And somebody can also come with another way of saying yes. Employees who are more engaged at work are appear. Yes, you can also say that. And the other person was saying the higher. The higher the happiness, the level of happiness the employees have the better the work engagement. Yes, you can say that again because remember there is this first statement. You can also use that in your explanation as well. There is no right or wrong answer, especially from what you just said. All of you have said the right things that can be mentioned about this relationship. The more... Because it's a positive, it means... And it's a strong relationship, which means it could have been a perfect one. So the more employees are happy, the more the work engagement... The more they will be... The more they will be more engaged at work. Which talks to what the statement was saying. So it agrees with the statement of the research. Okay. I'm not sure if I should even write it. No, it's fine. Don't write anything. Okay, so now calculate the common variance. Remember what the common variance is? It's just arse weight. Always remember that. So you were given arse. So you just take arse and then you just weigh it. I didn't even have to put it in brackets. Yo, I'm so crazy. Before this, I first went and calculated arse without noticing that it was given. I learned my lesson. And what do you get? 0.8281. Yeah. Which we can also just say it is 0.83 if I leave it like that. And remember the last part says illustrate. You just draw a VIN diagram like that. And don't label A and B. Label the employee happiness. And engagement. Depending on where you want to label them, you can label them at the top or inside if you are able to and just highlight that and say this is your 0.83. That will give you a map. Two maps. One map. For drawing the graph or this picture. Okay. Make me please help before we go. So the space that I live in between is what I would have gotten. Yes, that is your common variance. I'm a bit confused. When you draw in the diagram, the space that I shade is the space left or the space included. So this is your common variance. It is where they are both common. But they're overlapping. That is why I'm pointing it. Maybe I must make it bigger. You can see that I see that here and I make an arrow just so that this is your common variance of 0.83. You can even make it bigger. Depending on how you want to draw it, and show where your common variance is at. Okay. Which of the two variables are independent? Happiness. Happiness is your independent, which is your X variable. Defendant variable is your outcome variable, which is your Y variable. Okay. What are the two of the intercepts? I always want to ask you, how do you write your equation? Let's see if on here, do you have? Oh, yeah. No, yeah. So because you have Y hat is equals to B X plus A. A is your intercept. And V is the slope. Come on. Stop flicking. So they have given you the slope. Let's put it here. You have the slope. V is the slope. Since they have given you the slope, they are asking you about the intercept. So it means we need to use this formula. A is equals to the mean of Y minus this. What is the value of your intercept? Therefore, they're asking you to calculate A, which is Y bar minus E. So you just substitute the values. What? 2,93. 2,93 minus. 0,93 minus your B. Open bracket. 0,881. Times 3. Times 3. So you can also use open bracket, close bracket, if you want, if you want to enter all of it at once. 2,93 minus. Open bracket. 0,881. Times 3. Close bracket. Equal. What do you get? 0.5. 0.5, yeah. 0.5. And that's the answer. For two months. You see that's the reason why I'm saying they are stingy on others. This shouldn't have been one month. But you're giving it two months. Okay. Calculate the work engagement score. If happiness score are 3 and 5. So therefore it means for calculating 3, you get two months for calculating 4, you get two months. Now, you have the A. Let's go back to our formula. This formula that we're talking about. Y hat is equals to BX plus A. We need to use that formula. So we do twice. Y hat is equals to BX plus A. Y hat is equals to BX plus A. Now you need to use the information given to you. So we are given B and we calculated A. You just substitute it into the formula. Y hat is equals to our B. 0.8. And our X, we can start with the 3. 3. Plus our A. 0.4. And calculate that and tell me what you get. And this question, I think they like it because you always get this question in your exam. Correlation and regression. So you need to be able to answer these questions with ease. You are very answered. I got 2.93. What was the first one? I also got 2.93 for the first one. It's 2.93. And you can say this side for 5. 0.8. 1. Times 5. Plus 0.5. 4.5. I'm also getting 4.5. 4.5. It's 4 months. 2 months for doing that, 2 months for doing that. If they gave you 1, you just calculate the 1. The last one. Give a graphical representation of the regression line by indicating the intercept, the predicted value. So there are several things here that they want you to interpret. Intercept. The predicted value. And the true and false that we just estimated. Now, the intercept, it is where it passes through the y-axis. So if I draw this graph like this, this is your y-axis. Your y-head. And this is your x-axis, which will be your happiness. So what was our y-intercept? Our y-intercept, it is the value of A. Remember that? 0.5. So we can start here and say this is 0.5. 0.5. Start there. They also said we need to represent the predicted, the new predicted values of 4 and 5. So I'm just going to use this value. So this will be 1. I'm going to use 1, 2, 3. 1, 2, 3, 4, 5. Let me just use 6 as well. So we know that for 3, where it's 3, I can just put dots. I'm just going to put all of the dots going up and also 5. And we know that for 3, it is 2.9. So I don't have enough space. So I'm going to say here it's 1. Our 0.5, let me move it. It looks like closer to 0 again. 0.5 should be there. So I need to have enough space. Right. 1, 2, 3, 4, 5. Probably 6. So yeah, I can just make it dot 0.5. 1, 2, 3, 4, 5, 6. Now, our estimated weight of 2.9. 2.9. So I'm going to use this dot again. And 5. I'm going to keep it up after that. And for 5, it's 4.55. So 4.55 should be closer to 5 some with 8. So I can just put the dot there. And I can. Since these are estimates, we highlight them like that. And you can do a solid line. Solid, solid, solid, up until somewhere there in between. And then you dot, dot, dot, dot, dot, dot, dot, dot, dot. You would have estimated that. You can also use those, release like that. And you need to label, you can label this, not using the X and the Y. You can say this is employee happiness. You can use employee happiness. You write it here at the bottom somewhere here. The label. So you can have the X and Y there, but you can say this is employee happiness. And because it's six months and they have here your label. Let's say engaged something like that. Work engagement. Work engagement. Work engagement. And also remember to put the title. You just need to have the title there. I'm not sure how do you see my. Thank you. Okay. Okay, so don't forget the title. And the title can just say a regression. A graphical representation of the regression line. Employees happiness. Of course. Employees. Work engagement scores something like that. Or you can say the relationship between. That can be your type, your, your radar. Or your head for your, for your graph. Because I don't know how they mark. For six months. That is six months. So they cannot just use this month. Out those so probably. Okay, so. I'm going to stop the recording right now. I'm going to have a five minute. Sorry, what time? What time are you coming back? 845 or 835. Okay. I just want something to snake on. Okay. Thank you. What time are we coming back? 845 is good. 845. Yeah, 45 is okay. Okay. So I'm going to stop this recording. It will be part one and then we'll come back and do part two. Okay. All right. Thank you.