all right this is chapter one on data collection if you print the guided notes this will follow those you might also want to print tables and formulas pages there are eight pages I mean might just want to print them and kind of staple them together so you can always have them to refer to the first pages formulas chapter 1 does not have any formulas that starts in chapter 2 so you don't need the first page right now you might want to label it page once I will refer to those page numbers a throughout the video page 2 is formulas page 3 is formulas so again I'm just numbering at the same page fours formulas we don't go that far in the textbook so we won't even be using page 4 and for chapter 1 we are gonna need the fifth page of the formula booklet this random numbers table table Roman numeral 1 so I will be referring to this on later in the chapter 1 videos but you might want to have this printed just print all 8 pages now so you can have it throughout the course so just number the pages so that you know which page I'm referring to so how'd that handy we'll refer to that later in on in chapter 1 so chapter 1 is just an introduction to statistics really it's just a lot of vocabulary vocabulary that you might have heard before but maybe not in a math classroom and so we're going to kind of get familiar with the process of statistics and key vocabulary and what it looks like in a statistics environment which is a little bit different than how you might have seen it before so we're going to start with the process of Statistics anytime you want to gather any sort of data this is the process we go through give or take a little differences that most research follows this process the first thing you want to do is identify the research objective the first thing you want to do is decide on the question to be answered what is it that you're trying to figure out what question are you trying to have answered with your research so that takes some time you can really have to look into what is it are you trying to research and and how and so decide on the question to be answered and you also have to decide on the population to be studied are you going to study people animals objects if you're going to study people what kind of population women men young old bilocation who are you going to study who are you trying to collect this information on it could be animals could be objects could be humans so identifying the research objective is both of those tasks signing on the question to be answered along with the population to be studied and then once you know what you're trying to study and who you're trying to study you'll collect the data and say you have to use proper sampling techniques which we'll talk about later on in Chapter one that's the most important part of collecting data is to use proper sampling techniques and we'll get again into that more when we collect our data so using proper proper sampling techniques to collect your data once you've collected your data you describe the data this is summarizing the data sometimes this means getting an average of percentage all kinds of different ways we can summarize the data and you can display the data and it really is just to give an overview of the results you know what kind of results did we get from our data this will be discussed in chapters 2 through 4 and so collecting the data we're going to talk about here in Chapter 1 we don't really get into reading proper questions and descending on populations to be studied that's more at the graduate level but we start with sampling techniques then we get into how do we describe our data how do we summarize it how do we make displays about what we've collected and then the last part of the process is performing inference and that is extending the results from a sample to a population and there's a lot that goes into performing that inference and so we're going to talk about reliability in chapters 5 through 8 and we're going to talk about actually doing inference actually making a predictions in chapters 9 through 10 it's actually the rest of the textbook what we stop at chapter 10 so again we start with proper sampling techniques then we learn all the different ways we can summarize data and the second half of the course units 3 & 4 is about extending our results from a sample to a population and there's more details of all of this in your textbook this is just kind of the main ideas of each phase in the process of statistics so let's look at an example of how to break down this process the example from your textbook is about minimum wage it says that see me News in the New York Times conducted a poll and asked as you may know the federal minimum wage is currently this do you favor or oppose raising the minimum wage to that so the following statistical process allows the researchers to conduct their study so we're going to figure out what is the research objective what could data do they collect how do they describe the data and perform the inference so this is again all in your textbook and I'm just going to kind of summarize and recap each phase of this process so the first part is the research objective so their question to be answered was to determine the percentage of adult Americans who favor increasing the minimum wage yeah that's what they're trying to figure out what percentage of adult Americans favor increasing the minimum wage so the population that they were studying they specifically focused on adult Americans so you have to be specific of who are you trying to study in this case it was adult Americans I want to figure out the percentage who favored increasing the minimum wage that is their focus so then they collect their data we cannot ask everyone right I cannot go door to door across the entire United States and ask everyone so instead we take a sample and again we'll get into sampling techniques later so you take a sample in this example it looks like they surveyed 1,000 $9 mericans and found 706 we're in favor the question of increasing the minimum wage and again all of this is in your textbook I'm just kind of short handing what went on so we cannot ask every single person so they took a sample of 1,000 900 mericans and found that 706 of those 1,000 nine were in favor of increasing the minimum wage so collecting the data takes some time and we'll talk about proper and improper ways to collect that data once we've collected the data we can describe describe our results so of the 1009 they're saying that 70 percent and they got that from seven oh six divided by one 9 we'll talk about how to do that more later 70% of them favor increasing the minimum wage so you take your results and you should usually turn them into an average or a percentage some sort of summary of your results will talk more about displaying those results and and interpreting those results on later on so we've asked our question we've gathered our data we've summarized our data we've described it and now we're going to make some predictions and so to extend these results and again we'll talk about how to do all of that later to extend these results we must account for they call it uncertainty it's more variability so even though our sample is 70% there's no way to know if the population would also be 70% so to account for variability which is also sometimes called uncertainty we give a what we call a margin of error we'll talk about that later on in chapter 9 I call that kind of a cushion in this example so in this example there is a 3% margin of error so when we'll perform inference we can't be a hundred percent confident in what we're doing we have to give ourselves a little bit of buffer a little bit of flux and so we would say we are usually it's 90 to 95% confident that the population I'm going to run myself out of room here the population percentage given that our sample was 70 percent we thought the population percentage is somewhere between and we would take 3% off of that and add 3% to that so we can say between 67 percent and 73 percent so our sample is 70 we give or take a little bit to give ourselves a little kind of a window of confidence and I apologize for having to crush that into the bottom of the page again all of this is explained in further detail in your textbook I'm just kind of summarizing so that is the process of Statistics given any sort of scenario we can break it down into the research objective collecting data describing the data we've collected and finally performing inference and again this is a very long process we will break down each step of it and the second half of the course so let's move on to some vocabulary in the course again these are words that you might have heard of before but maybe not necessarily used in the same way as we're going to use them and what I've done is I've given you the definition for each word and I'm just going to fill in the vocabulary term as we go along and I'll kind of explain how each word works as we go so the first word here is statistics the science of collecting organizing summarizing and analyzing information to draw conclusions or answer questions in addition this science provides a measure of confidence in any of your conclusion this is a whole process of statistics we collect the data we organize the data we summarize the data and finally we analyze the data that entire process is statistics so that's kind of the field of Statistics if you want to become a statistician those are all the things you're going to do more specifically the entire group to be studied is referred to as the population we've seen that term before the population everyone to be studied a person or object that is a member of the populations be studied we call an individual so within a population there are individuals and a subset of the population that is being studied is called a sample so we have everybody we have each individual person and from that population we draw a sample so the entire group to be studied is the population individual is just one person in that population and we focus our energy on samples in this class a portion of the population that represents the population then we have the word statistic so it's a singular statistic so a statistic is a numerical summary from a sample so if I took a sample and got an average that average would be called a statistic and then I'm gonna skip down here going with statistic is the word parameter so you have a numerical summary of a sample and a numerical summary of a population if I have a numerical summary for an entire population I call it a parameter if I have a numerical summary for a sample I call it a statistic so there's statistics and there's parameters and we'll talk about the differences between those two I'm in the next video underneath statistic we have organizing and summarizing data through numerical summaries tables and graphs that whole process of organizing and summarizing data is called descriptive statistics when we're describing the data we could describe the data with tables and charts we could describe the data with percentages all kinds of different ways to describe the data so it's called descriptive statistics if we're organizing and summarizing the data and then if we're taking the results from a sample and extending it to the population that is called inferential statistics so influential me inferring or predicting what might happen so taking a results from a sample and extending it to a population is the process of inferential statistics so there's descriptive statistics and inferential statistics so these words kind of go in sets you notice I'm kind of changing colors as we kind of change sets of words here they're kind of all grouped together and you're broke again what kind of group them and give you further examples as we go along the next word says the characteristics of the individuals within the population so the characteristics of individuals we call those variables in this class so if you've taken an algebra class variables are very different than the variables were referring to here so the variables are just describing the characteristics of individuals it could be shape color size GPA income level it could be all kinds of variables that we're studying and there's two different kinds of variables that we're going to look at and the first kind is called a qualitative variable qualitative variable looks for a classification of the individuals based on some characteristic qualitative you kind of think of the word quality and so this could be favorite color it could be rank it could be like junior senior sophomore freshman sort of ranking so it could be any sort of classification where you'd put people in different categories this is sometimes referred to as a categorical variable you put people in different categories or animals or objects in different categories then there's also what we call a quantitative variable and you'll see the word quantity quantitative so this is a numerical measure of individuals where you could add or subtract or average the results this could be height this could be weight it could be number of courses completed or taken or anything that you would measure or count this quantitative this is a numerical variable so if you put things into categories or classify them it's qualitative if you count things up and how many of this and how many of that and and can average them or measure someone's height that would be a numerical measure and that's called quantitative so there's qualitative variables and quantitative variables and we'll practice classifying those in the next video within quantitative variables there are two specific types there's a quantitative variable that has a finite number of possible values and this is called a discrete quantitative variable this is countable if I wanted to know how many classes you were taking how many siblings you had how many cars you've owned anything I could count how many of this how many of that I'm anything countable is called discrete and then a quantitative variable that has an infinite number of possible values that are not countable this is more of a measurement oh hi how tall are you how much do you weigh a volume of something that is called a continuous quantitative variable and that's a measurement as opposed to counting so within quantitative they're discrete and there are continuous variables and we'll get into the difference between those two in the next video I'm going to stop there and then I'll continue in the neck Vidia