 So, let's get you ready for those who already took their first and they are waiting to take their second. Let's see if today's session can make you feel a little bit more comfortable to do your second assignment. Right. So, I'm going to ask you a lot of questions and then you're going to talk to me so that then we can move quickly along. So, let's summarize study unit one because we need to know and understand what study unit one is and I expect everyone to have gone through the study unit one as well. So study unit one, we need to understand and know what is statistics. Do any one of you want to answer that? What is statistics? Anyone? You can try. There is no right or wrong answer here. What do you know about statistics so far? I'll give it a go but I'm sorry if I'm wrong. No, it's fine. As far as I understand it, basically what people use statistics for is to transform information that they've gathered into information that can actually be used to make decisions. Thank you. That is correct. That is what statistics is. It's a method you use to transform the data into information to make decisions. That is that. There are two branches of statistics. Do you want anyone who wants to tell me what those two branches are? What are the two branches of statistics? Hi. Yes, there are descriptive statistics and inferential statistics. What is descriptive statistic? Anyone? What is descriptive statistics when we talk about descriptive statistics? It's in the weight. Descriptive. What is it? Nobody? Care. So when we talk about descriptive statistics, since we know that statistics is a method to transform the data in order to make decisions, descriptive statistics helps us as a method of describing the data either by summarizing the data, presenting the data and visualizing the data. That is descriptive statistics. You need to know all these things, especially for study unit one and inferential statistic. It is a method of inferring your data to about your population from the sample data. That is just inferential statistics. Descriptive statistics, we cover that in chapter or in study unit one, two, three and part of study unit four because it's probabilities. And inferential statistics, we cover it in the later stage when we do the hypothesis, when we do the estimates and the regression and so forth. So in statistics, there are key terminologies that we use sometimes. We have a terminology or a population who can tell me what is a population? How do we define a population, guys? It's all the information used when you are collecting your data to draw the conclusions. Yes. The population is all elements of interest that you want to study. What are the measures that you get from a population? If I have a population and I want to... Parameters. Pardon? Parameters. Don't be. Parameters. Yes. The measures you get from the populations are called parameters. And if the population is too big and we take a sample from there, which is a subset of the population, the measure that we will collect from that will be called a statistic, right? So those are the terminologies that you need to know. But in terms of those measures, there are certain things, because we know that with statistics, we have to summarize the data as well. There are certain things that we need to know about the population, the characteristics that defines that population, and also the values or the measures. We calculate them from the values that we collect from those populations. So therefore, we have two terminologies, variable and data. Can you tell me what is a variable and what is a data? Do you know that? Do you know what is a variable and what is data? Anyone? Again, I'm sorry if I'm wrong. No, it's fine. A variable is something that you're measuring, like the information. And the data is what's the information that you get after measuring it. And I'm explaining it really badly. Yeah, never mind. Sorry. Yeah. I see also on the chat. Yeah, I don't know how to pronounce your name. I'm sure I'm pronouncing it right. She says also a variable is a characteristic of an item or individual that can be observed or measured. So yeah, that is a variable. It's a characteristic that defines that population. And what will be the data? So would the data be what's actually being measured? Like the information you're getting out of it? Yes, the data is the units from those characteristics. For example, like I'm going to go back to Leveen. She says gender. So the data from gender will be female or males or other type of gender. Or if it's height, it will be things like how tall are you in centimetre, 166 centimetre, 172 centimetre and so forth. And those will be your data points or your data units as well. So once we understand what the types of variables are and the types of data are, can give me those two types. Categorical or nominal. Pardon? Quantitative data. Categorical or numerical. Categorical or numerical or we can also refer to them as quantitative and qualitative, right? If it is qualitative data is data that you can put into categories. If it is quantitative data is data that you can observe or you can measure. Now, give me the two types of quantitative data. Yeah, it's nominal and ordinal. Nope, those are not types of quantitative data. It's discrete and continuous. And when data is discrete is data that it is counted. Data that is continuous is data that is measured, like age is continuous, salary is continuous. Discrete is like counting the number of children you have, the number of modules you have. Those are discrete. Now, once you understand the type of variable, there are also the levels of measurement within the variable. Because they also help you in terms of how you're going to analyze the data as well. So what are the four types or the four levels of measurement or what we call scales of measurement? What are the four types? Someone was saying them before. So let's start with the qualitative one. The two that relates to qualitative or categorical data, there are two, which are? Nominal and ordinal. Nominal and ordinal. Nominal data is what kind of a data? Categories, I mean categories maybe. Yeah, it's categories, but what kind? The types that don't really have any meaning in order. So for example, as you're referring to a country, South Africa is more important than America or something like that. There's not really any value there. Yes, that is 100% correctly. There is no scale or order or rank to them. Ordinal, what kind of data will the ordinal data be? Ordinal will have a rank. Ordinal will have the rank, yes. So now between ordinal and nominal, which one of the two has the highest order and which one will have the lowest order? Nominal will be at the bottom. Nominal will always have the lowest order. Now, give me the two levels of measurements in terms of quantitative data. Is it ratio and interval? It's ratio and interval. What is interval, data? Interval will have like a starting point and an end point. Okay, yes. And a ratio? A ratio is one that has a zero point. Like if it's zero, it doesn't exist. So like half you can't be zero centimeters tall. No, yes, yes. So interval has no zero point because an interval data will be data that, like for example, temperature. So your temperature can be zero, can be negative four, negative three. So any number that can go into the negative is an interval data. Any number that has a true meaning of zero, which means zero means something. Zero means nothing. Sorry, zero means nothing. It means it doesn't exist. That will be a ratio. And once you understand all this, you are able to answer any of the questions from the exit or from your assignment as well. And ratio will be your highest. So now, yes. After a random question, with regards to the ratio scale, would any of your numbers ever be negative there? Or is that not a thing? No. Okay, cool. Thank you. Now, since you understand the types of variables, now let's go to study. You need two ways you need to understand how you summarize the data. Can you give me three ways of how we can summarize categorical data, data that we can put into categories? What are the three, Kelly? I can think of a bar chart and a pie chart. I'm having a bit of a forgetful moment right now. Okay, so we have a bar chart and a pie chart. Any other summary we can do? A frequency table. And a frequency table. Those are the three. In terms of this, you just don't need to know only the names, but you need to know the properties that makes each and every one of them. What is a frequency table and how do we create a frequency table? Or what are the properties of a frequency table? A frequency table will give you categories and it will also show you frequencies and it all it can also show you the percentages as well. What are the properties of a bar chart? What are the properties of a bar chart? I would like to know that this is a bar chart. So the bars will represent either the percentage or the frequency. Yes, the bar chart has an X and a Y axis. Yes, sorry. Sorry, the bar chart has an X and a Y axis and then each category gets its own column and you can then compare the data based on how long or short the columns are. Okay, so the bars will represent your categories, right? And the height of the bar will represent either the frequency or also the percentage as well. What about the properties of a pie chart? What does the slices of a pie represent? Sorry, the slices of the pie represent the percentage and the larger the pieces or the... No, the slices of the pie. What do they represent? The percentage of the category. The slices represent the category and the size of the pie will represent the percentage or it will represent the frequency as well. And between the three, the bar chart will be the most appropriate chart to use if you have more many categories. If you have many categories, then a bar chart will be better than a pie chart. But if you have a few categories, a pie chart and a pie chart and a bar chart can also be used to visualize your data. Your frequency table is just a table that summarizes your data. So we now know about the categorical data. What about numerical data? When we work with numerical data, we have different ways of visualizing them. Just give me those... I think there are about six ways that we can visualize the numerical data. But before you start working with numerical data as well, what do you need to do with your data? You can sort your data right or put it in an audit array. And if it's in an audit array, what other table can you or visualization can you create with an audit array? A stem and leaf. You can create a stem and leaf plot with an audit array. And if other visualization that you can do on a numerical data is creating a summary table, which is called a frequency distribution and a cumulative frequency distribution. So all of this, you also need to know the properties and how to create them and how to use them and how to interpret the data from there. So if I have a summary table of numerical data, which is called a frequency distribution table, what other visualization can I create from that frequency distribution table? A histogram. You can create a histogram. What else? We can create a frequency polygon. And what else can we create with a scatter plot? A scatter plot, yeah. But the scatter plot is when we have two numerical values, the X and Y values, and use it for relationship to create the measure of relationships. But in terms of the frequency distribution. We can create a cumulative distribution or cumulative polygon, which is also called an orgif. So there are three things that you can create from a frequency distribution, a histogram, a frequency polygon, and a cumulative frequency polygon, which is called also a orgif. You also need to understand and know the properties of each and every one of this visualization, including that the histogram. It's like a bar chart, but there are no bars in between and the bars of the histogram represent the class intervals and the height of the bars represents your frequencies or sometimes it can represent your count or your percentages as well. So you just need to know all of this before you even go and start answering your assignment questions as well. Now, once you understand the visualization part, now we need to go and understand how we can summarize the data by using other summary measures, especially when we want to calculate statistics or parameters. Now we have two or three, actually three ways we can summarize the data. We can summarize it in terms of measures of central tendency or measures of locality. We tells us the location of your data or the description of your data or the distribution of your data. Now, with measures of central tendency, there are three measures of central tendencies that we use in STA 1610 that you need to know. What are those three measures? The mode, the median. The mode, the median. And the mean. And the mean. What is the mean? It's when you add all the data together and divided by the number of data you have. It is correct. And you can calculate the mean for the population and the mean for the sample. So when you calculate for the population, we call it the parameter and it is represented by for the population. The mean is represented for the population is represented by new right, which is the sum of all observations divided by capital letter n is the new. And for the sample, it's X bar, which will be the same. The sum of all observations divided by small letter n. You just need to remember that and know that. Okay. What is the median? The middle value. The median is the middle value. Oh, before I know, but let's just get the definition out. And how do we find that middle value? The number of observations plus one divided by. Am I right? So I think you divided by two. Yeah, first find the position by using the same formula that you just referred to, which is n plus one divided by two. And then we go find the big, but sometimes the answer you will get is not a whole number. It will be a fraction. It will be 3.5. Therefore it means it's located between two values in that way. What do we do then? You add the two values together and divided by two. We add the two values and divide by two where the position is located between the two values. So therefore it means you need to know and remember that. You have even numbers. So if I have one, two, three, five, six and eight, this is an even number. Therefore it means at the end, I will have the middle number between two values. If I have odd numbers like one, two, three, five, six, nine and 12. If I have odd numbers, this is odd one, two, three, four, five, six, seven. They are seven. It means they are odd. If I have odd numbers, therefore it means the middle value, the position will be a whole number. And then I will just find the position by getting to that position of that whole number that I am referring to. Okay, that is what you always need to remember. If you have even numbers, you're going to take an average between the two values where the position is located. It's an odd number, therefore the position will be a whole number and you will get the median from that position that you're looking at. What is the mode? I think the mode is the number that appears most. The number that appears most. It means the number that occurs in the whole data set more than the rest of the other number. Now, which of these three is affected by outliers? And what is an outlier? Let's start there. Do you know what is an outlier? An outlier is a piece of data that is very far away from the rest of the set. Thank you very much. It's one of the extreme values, which is the value that is far apart from the rest of the other values as well. And which one of these three measures gets affected by the outliers? The mean. The mean. Only the mean gets affected by the outlier. You need to go and learn more about this. In terms of the mode, do you know that you can get a, not even, I'm not even going to mention that. Let me just give you a summary of the mode. Now, remember that you need to know that with the mode, you can have no mode. A mode can have two modes, which we call them by modal. You can have more than two, which we call multimodal. You always need to remember all those things. In terms of the variation, what are the measures of variation? Measures of variations tells us the spread of our data, how diverse or how dispersed our data is away from the mean. So what are the four measures of variation that we work with in STA 1610? What are those measures of variations? Variants. The variants. The coefficient of variation and the standard deviation. And the standard deviation and the last one is the range. So we do have the range, the variance standard deviation and coefficient of variation. The range is your highest value minus your lowest value. The variance, you can calculate it for the standard deviation for the population and also for the sample. And the variance is just the square, the standard deviations away from the mean. The standard deviation is the most commonly used measure of variation and is the square root of your variance. So also with the variance, we use s squared for the sample and we use s for, sorry. We use sigma squared for the population. Always remember that for the sample, we always use the alphabets, the normal alphabets that you know. For the population, we use the big weights or the big letters from the Greek calendar. So we use Greek letters to represent the population because population is huge. We use these huge letters like the Greek letters, which is sigma for sigma squared for the variant standard deviation because it's the square root of your variance. It is your s for the sample, which is the square root of the sum of your observation minus the mean squared divided by n minus 1. That is for the sample. For the population, it will be the square root of the variance, which is the sum of your observation minus the population mean squared divided by n minus 1. Squared divided by n. You need to know these formulas. You need to know how to identify them. You need to know the parameters in there and the statistic symbols that represent population and sample and so forth. I also did send you a post or give you information about how to use the calculator, especially when we calculate all this. If you have a scientific calculator, you should be able to use it in the exam or in the assignment to just work out your questions quicker. The coefficient of variation, which also tells us the variability of the data, it is given by CV and it's always represented by a percentage. It's your standard deviation over sample mean multiplied by 100%. And that is your coefficient of variation. Lastly, in terms of study units three, we do have measures of all the quotas. The quotas, which is the last bit that we're going to look at and then we're going to look at some exercises. The quotas, which spread your data into four parts. I did also share the Quartal video with you guys. So you can go and check under the study unit three what the quotas are. If you haven't read through them or haven't gone through the video that talks to the quotas is split your data into four parts. And you are able to visualize the data by using a box plot because on the box plot, the box will represent your quotas. Quartal one representing all the data that is below 25% or more than 75% and Quartal three representing data that is below 75% or 25% of the data being higher than Quartal three. With the median in the middle of the box, which is also referred to as Quartal two. And Quartal one, you're always going to find it by using the formula, the position n plus one divided by four. That will give you the position of Quartal one and Quartal three. You will find the position by using three times Quartal one, which is n plus one divided by four will give you the position and you need to go and find the position. Now with Quartals, it's also very important to remember the rules of how you find the positions and the values of those Quartals. With Quartals, if its whole number, the value in that position will be the value that you are looking for. If it is 0.5, which is fractional half, then you need to take an average of the two value where the position is located. If it is 0.25, then you're going to round it down. So let's say the answer there was 1.25, you're just going to say it's on position one. If it is 0.75, let's say it is 3.75, therefore you're just going to round it up and say it is at position four. But when it is 0.5, let's say it says it's 1.5, then you're going to take an average of the two values like we do with the median where we take the average of the two values. You need to remember those things when you answer questions relating to Quartals. The other thing in terms of the Quartal, you need to remember that you can also calculate the range of the Quartals by calculating what we call Inter Quartal range, which is IQR, Inter Quartal range, which gives you the value, not the position, the value of Quartal 3 minus the value of Quartal 1. And those are the things that you need to remember and know about. What I'm not covering as yet, which we will cover next week, it's how we use the measures of variation to calculate the empirical rule, which usually we use the values of central tendency and the standard deviation, because with the empirical rule, it says 68% of the data falls within one standard deviation of the data. And the other one will state that it falls within two standard deviations, depending on whether you're using the population or the sample. And you can also calculate when it is three standard deviations. So if it was a sample, you will say it is the mean plus one standard deviation. For two standard deviation, it would be the mean plus two standard deviation. Let me put S like that. And for the three standard deviation, you will say it is three times the standard deviation. And this is what we call the empirical rule. And you can check if the data that you're looking at falls within the mean standard deviation. And this can also be a plus or a minus, because it is between two values. It will be plus or minus. I only use the plus, but you can find the limit, the upper limit and the lower limit of it. So the minus will give you the lower limit of the value and the plus will give you the upper limit of the value. And you can also write it as such, so the minus and the plus. So it will be the upper limit and the lower limit that will tell you if it falls within those intervals. Okay, so that concludes the summary by now. If you're still uncomfortable with study unit one, two and three, therefore it means you still need to go and revise and work hard. Let's look at if we are able to answer questions, because now we just randomly revised the content. So now let's look at how questions are asked. Complete the following sentence. It's a statistical method that draws conclusion about based on computed from the. Is it the sample? So if you read that the sample is a statistical method. If that fits in that question, then you can use that B is it population is a statistical method. If that is correct, then you can say it's B C inferential statistic is a method that draws conclusions. If it's inferential statistic, then you can say it's C D descriptive statistic is a statistical method that draws conclusions about the and you can then pick from there. So anyone who wants to try check the. I think it's. My guess is D my guess is D and then Jason as well says is D so let's see D D says. Complete the following sentence descriptive statistic is a statistical method that draws conclusions about the population based on the statistics computed from the sample. Is that is that correct? What is descriptive statistics and what is inferential statistics? So descriptive statistics relates to chapter 1 study unit 1 2 and 3 inferential statistics refers to study unit 6 7 8. Maybe let's start with 8 that units 8 9 10. So. If it's study unit 1 2 and 3 what do we do in study unit 1 2 and 3 we describe the data we don't draw conclusions about the population right. So therefore the answer is C because the answer would state. That inferential statistic is a method that draws conclusions about the population based on statistics computed from. The sample the only difference between the two statements is just knowing the definition what is inferential statistics because inferential statistics talks through the hypothesis testing. All the confidence intervals and the regression and so forth. Whereas yeah it talks about visualization summary stats. Summary statistics and so forth. So yeah we draw conclusions so therefore it means it's inferential statistics. Let's look at the second question if how they can ask you questions. Which of the following is not a goal of a descriptive statistics. The first one says summarizing data displaying data aspects of the collected. Data reporting numerical findings estimating characteristics of the population presenting data from. The table so. Anyone please get and say is D Nia Nialanda says also D that is correct that is not part of descriptive statistics remember descriptive statistics is about summarizing presenting and visualization. So yeah we have presenting reporting. Displaying which is visualization summarizing. And so forth so D is the only one because D says estimating we only estimate in inferential statistics. Look at three which one of the following statement is incorrect with regards to. A statistic what do we know about the statistic is a measure from the sample. So. Is it a BC do a sample standard deviation is a statistic. A statistic is an estimate of a population parameter. A statistic is a summary measure calculated from a sample. A population mean is a statistic and a statistic represent a property of a sample. Remember we're looking for the incorrect answer sorry. I'm not sure but I think be be like. I'm going to point are you saying is this. I say B. I think so. I'm not very confident driving so. Nope. Remember we can use statistics to estimate the population parameter so it's not to be. I think it's D. It is the yes all the other statements are correct about the statistic because a sample standard deviation is a statistic statistic is used to estimate the population we just spoke about it right here. In this question based on statistics computed from there to draw conclusion about the population. The statistic is a measure that comes from a sample and a statistic represent the property of a sample so all of them except D which says a population mean which then this should be a para. Right. Get the next one. Which one of the following statement is incorrect. Number one says. We're looking for the incorrect statement a variable is a characteristic of an item or individual be measured as sample is a portion of a population selected for analysis. In a power chart the size of the segment varies according to the percentage in each category. And histogram describes better qualitative data than a bar chart. The mode is the most frequent observation in a data set one two three four or five. Four because the histogram is used for quantitative data. That four is the only question that is incorrect because number one the variable. It's a characteristic we did speak about this that you need to know what the variable is it's a characteristic that defines an item or individual. That is either measured or counted a sample we know that it's a portion that gets selected from the population. And the pie chart we also spoke about the properties of a pie chart that the sizes of the slices of pie chart represents the percentage or the count of the categories. And the last one we know that the mode is the most frequent observation a histogram. We present. Quiet. Fenty. Tative data not qualitative data. Hey. What is a summary measure that is calculated from a sample to describe a. Characteristic of a population. What is a summary measure that is calculated from a sample? Is it called a box plot? Is it called a data? Is it called a parameter? Is it called a population? Or is it called a statistic? It's a statistics number five. It's number five. That is correct because a box plot is a graph that represent your quarters. Data is just a sample unit is the value that you will use to summarize the data. A parameter is a measure that comes from a population and a population is your set of all elements. The only correct answer here is a statistic because a measure that gets calculated from a sample is statistic. Which one of the following statement is incorrect with regards to qualitative and categorical data? Categorical data is measured on an ordinal or nominal scale. So remember we're looking for an incorrect statement. Is categorical data or categorical data is measured on an ordinal or nominal scale? The frequency or count of each element can be determined. The mode can be determined. The mean can be calculated. None of the above. Remember we're talking about qualitative data. So it means you need to know the properties of a qualitative data. Is number one correct? Let's use the method of elimination in terms of incorrect statement. Is number one correct? Yes. Yes, number one is correct about categorical data. In categorical data, can we calculate frequencies and count? Can we count things and calculate frequencies? No. In categorical data, can we count? Can we display or determine the frequency? If I have in my class, I've got Mary, Mary, Paul, John, John, John. Can I summarize this and count how many Mary's I have? Yes. Yes. So the ease can be determined. The count of frequency, and because also remember your frequency table, which is a summary table, will have Mary, John, and Paul. And I can have my frequency here. And I can have my relative frequency here. All my, all my percentage, depending on which one. So here I have two Mary's in my class. I've got three John's in my class, and I've got only one Paul. I can count the frequency of the categorical data. You just need to always remember that you need to think outside of the box as well. Are we able to determine the mode? Yes, that would be the most. So Mary's three John's could be John's. So John will be our modal data in terms of this. So yes, we can determine the mode. Can we calculate the mean? Can we calculate the mean? No. We cannot calculate the mean of the qualitative data. So the only option here, which is incorrect, because we're looking for the incorrect statement is only option four. And to exercise seven, which one of the following statement is incorrect? So we can also apply a process of elimination. A population is a complete set of object in the study while a sample is a subset of a population. We're looking for the incorrect statement. Is this statement correct? Yes. Yes. Correct? A statistic is a property of a population while a parameter is a property of a sample. Is that correct? Yes. Statistics, population, parameter, sample, is that correct? No. No, that is incorrect. That is incorrect. Statistics is for the sample population. We get the measures are called parameter or a measure is called a parameter. So always remember that. With multiple choice questions, it's always tricky, but you need to apply your mind in terms of this. Don't forget about the things that you know. I'm not even going to go on with the rest because the rest are true. Data from a sample are in the form of a variable and they can either be numeric or categorical because it's qualitative or quantitative. Quantitative data is numeric data and it can either be discrete or continuous. We did cover this earlier. And qualitative data can categories and uses labels to identify attributes. As you can see that everything that we spoke about this when we started the session is everything that is on the questions as well. And these are from past example and past tutorial letters as well. Which one of the following variables is not a categorical data? Remember what a categorical data is? It's data that can be put into categories and they say not. So let's start with the process of elimination. Yeah, you need to look at key weights. Identify the key weights that are asked as well. Gender of a person. Gender is that key weight. Is this categorical or numeric? Categorical. That is categorical. That is true. Name of internet provider. Name is this categorical or is this numerical? It's categorical. I'm going to skip see. I'm going to go mental status of a person. Is this categorical or numerical? Categorical. Categorical. Classifying an object as defective or non defective. Categorical. That's categorical. Question here which will be we left out was the height of a person. Numerical. Numerical therefore it means this. Is not categorical. Is numerical. It is quantitative. Which one of the following statement is incorrect? Also we also it's correct. Yeah, we're looking for the correct statement. Okay. So number one also we go with the process of elimination. Gender. Merit status. Religion are examples of qualitative. Now variable. We're talking about levels of measure length. Can we. Is gender, medical status and religion. Can they be placed in order? If not, then that statement is incorrect. If yes, then this statement is correct. Is there order in terms of gender? Is there order in terms of medical status? There is no order. There is no order. There is no order. That statement is incorrect. Right. The amount of money a person spends in a shopping mall. Is discrete variable. Amount of money. Is it discrete? Yes. Remember money. Okay. Did I explain what discrete is and what continuous? I said. Discrete is things that you can count, right? In whole number. Anything that can be a whole number is discrete. Like one, two, three thousand five hundred. Anything that takes up a decimal point. It's not discrete. It's continuous. It's not discrete. It is not discrete. Always remember that. That money is not discrete. Even if we talk about money in rent. But money is in cents. Okay. I think I had seen somewhere. Wait was saying that since rounds around it to. If they ask you. Okay. Here is the other thing that you always need to remember with. Categorical data and numerical data. Let's start here. Let's start with categorical data. Categorical data. We can use numeric. To code the data to represent a category. For example. We can use a scale to say from a scale of zero to five. Identify how you feel. How are you satisfied? How likely are you to use our products. On a scale. Which means zero on that. If we have zero to five. Zero means. Not likely. Five might mean. Very likely. They represent those categories. It means we cannot take the zero at once. And add them up. That is categorical. We use numeric values to represent the categories. In terms of. Categorical data or not. Numerical data. You need to be aware of the following. A continuous value. Can be converted into a discrete value. And a discrete value. Can also be converted into a categorical value. You must read the question. Correct. If the question says. What is. If they say the amount of money in. Rent. Rent. It makes it. Discrete. But if they say the amount of money. Amount of money or salary. Let's say salary. Let's work with salary. If they say salary in rent. It is discrete. If they say salary. It takes its original form. It's a continuous because. Money. It's always to this nearest sense. Age. It's one of those that you always need. To remember that it is continuous. Until they tell you that. Age in years. In years. It will convert from. Continuous. To discrete. Because years are in. Then the years will be. A whole number. So always remember that. Right. Those are the things that. Yes. So amount of money. As long as they don't get you to how. You need to classify it. You take the original form of that. So amount of money. Money is in. It's a continuous variable. It will have. We rounded off to the nearest sense. Because we put comma zero zero. At the end. It is continuous. So this is also. Incorrect. Number three. Number of girls. With blue eyes. Is a. Discrete variable. Number. Of girls. Discrete variable. Yes. That is correct. Because the number of girls I can count. One, two, three, four, five girls. They are discrete. They can be counted. The. Position one finishes in a race. Is discrete variable. You can finish in position one. Position two. Position three. And this is where the other thing that comes in. I'm categorical data. If we. They can also be discreet because. If they represent a position a position is a discreet. It's a counted. Value. Number two, number three, number four. So. But you just need to remember that. Position. Of one, two and three and four can. It is not a quantitative. It is a. Qualitative. So we use positions to represent a. Category. So that. Is also incorrect. The number of times. A mouse makes a long time in a. It is a continuous. Variable. No, because we can count it goes. One, two, three, four. So it should be a discreet. It should be discrete. So that will not be correct. The only correct answer here is option three. So. I know that it's very tricky, especially when. We start talking about the types of. Variables and we also. Use the levels of measurements. You just need to be very cautious. Just when you work with. With that or when you answer questions. Like that. Question 10. I'm a way of the time. We only have seven minutes. But we will continue with this next. Week as well. And we will include the calculations. Which one of the following statement is incorrect. So next week will not start with the summary. Go dive in directly into. The activities. Which one of the following statement is. Incorrect with regard to variables. And their scales of measurements. Number eight. We're going to also go through this. As process of elimination. Incorrect. Remember that. Quantitative discrete variables. Results from counting. Quantitative. Discreet. Counting. That is correct. Quantitative. Correct. Quantitative continuous. Results from measuring attributes. Continuous. Measuring. That's great. That is also correct. Because anything that we can count. It's discrete. Anything that we measure. It's continuous. The mean and the median. Cannot be determined. For a nominal scale. Mean and median. Cannot be determined. For a nominal scale. Is that correct? Or incorrect? Incorrect. That is. Can we determine the mean and the median. For the nominal that is. That is correct. It is correct. Because we cannot determine. We cannot calculate the mean and the median. For nominal scale. That is our categorical data. We cannot add them. And divide. And all that. So we cannot add and divide by how many there are. So that is correct. And that is. The key to the question. To that statement cannot. We cannot determine yes. That's true. Ordinal scale of measurement. It's a higher level of measurement. Than interval. Okay. So if we have that. If you looked at the notes. We do have the order. So at the bottom we have. Nominal. And at the top we have. I think ratio interval. I'm. Not 100% sure. They so. It starts from Nominal. Then it goes to Odina. So. Which one is the highest. Will be. The interval right. So yeah it says the original is higher than. Interval. So that will be there. Incorrect statement that we are looking for. Right. You only have four minutes. I had so many other questions in here. But we next week. Sunday we will go through them. I will post this on my. Mojo. As. One of the notes. And then you can also go through them. And then next week when we meet. You are able to go through all the questions that are in. In here. That will relate to everything. Study unit. One two and three. And then we can also start doing some calculations. As you can see here. I've added some of the questions that has some calculations. So we'll go through them. But if you haven't. Gone through. The. The videos and catch up. I urge you to go and. Do the catch up. I will also upload the videos today. This video today on. On my module. Please go and do catch up on. Study unit one two and three. If you haven't started with your assignment. Please go and do your first. Submission only your first. Submission. And after next week. You can go and do your second submission. If you have done all of them. You will just have to wait for the third submission. But. Make sure that you go through the content. And not wait until a day. Before you submit. I want to also to plan. The. The sessions in a way that. We finish the content. We finish the content. We finish the content. We finish the revision and everything. Two weeks before you. Your final submission so that it gives you. It allows you more time. To look at the. The information again and again and again. And. When you do your last submission. You are able to submit and I also. Want to encourage you. To submit your assignments. Earlier. Don't wait. If the assignment is due on the 27th. Don't wait until the 27th. To do your assignment. Because by the time. The assignment is you would have covered everything. And we would have moved on to the next. The other thing. So that we can allow more time for revision. So a whole month. Doing. Study units one two and three might. Not be sufficient. Or might be. More and then it might also be boring. And all that because we might be. Repeating things again and again. And again and again and again. And might not be benefiting you. So please. As long as you feel comfortable. As soon as you feel comfortable. That you want to take a shot. At the assignment start. With your first. And once you are done with the first. Don't rush to do the second one. Because the closing date is on. The end of the month. Wait. Two three weeks before the closing date. Go through the content. Now you know how the questions are like. That are being asked. Go through the content. Make sure they make sure that you understand. And then. Revisit. And do your second submission. Any other question any comments. Any. Okay I have a question. With regards to the assignment. Is it already posted? Because I checked it today. And then. I saw that they said it's not. The questions are not yet posted. So I don't know if. One. Are you referring to assignment one. Yes I'm referring to assignment one. They should be posted. Aren't you able to see. I did the assignment one already. And the first. And they were all the. I think the problem might. Be that you're on the group. Web site rather than the lecture Web site. Maybe try and look on the lecture Web site. You should find assignment one there. Okay I will do that. Thank you. Any a question. Before I let you go and enjoy. The rest of your Sunday. And your mother's day. If there are no questions. Thank you for coming. And thank you. And thank you. For participating next. Sunday we're going to continue on this. And then I will add more questions. From study units three. Which has so many calculations. So we will do. Most of those calculations. And. Yeah, then I will see you Sunday. Thank you. Thank you very much. Thank you. Thank you. Thank you.