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Fax uncountable zip code

hi everyone in this video we're going to begin to talk about chapter 2 which is titled data visualizations and numerical summaries at least that's what i have titled chapter 2 as so as an outline of we're going to talk about in the videos about chapter 2 we're first going to talk about the types of data okay we learned what is data sort of in the first chapter in this chapter we're going to get into seeing different types of data and we'll also look at graphical summaries of data okay so we'll take a good bit of time to look at graphical summaries and in fact you're going to have a project where you explore many different kinds of graphical summaries and then lastly we'll talk about numerical summaries of data and you'll be very familiar with most of these at least have seen them before we may get a little more technical than you're used to but we'll talk about measures of center okay how to tell where the center of the data is because that's usually a question of interest measures of variability how spread out are the data because that's also of interest also of interest yes and so uh let's start uh by looking at some data so we never really looked at data in the first chapter so this is our first taste of what data is okay remember why am i so interested in talking about data so much statistics is about gleaning information from data okay and so we're especially interested in pieces of information okay so let's start by talking about some data that has to do with irises okay and so here's the data now uh when we'll see data in this class it'll be in many different formats but this is the format that we'll see most often every column you'll usually see it in a spreadsheet of some sort so you might see it in excel you'll see it in other ways that i will show to you but any time that you take data or that you see data hopefully it's in this form where every column is a different variable okay so we have five variables here uh sepal length simple width petal length petal width and species these are all things about irises and i will show you a picture and tell you what sepal and petal mean okay but we have five variables here some are numbers and some are uh not and that's okay they're all data and so here's a picture of what these irises look like okay the petal is the part from the very middle of the flower out to the part that that i would actually usually call a petal myself okay but it's the sort of stem ish thing from the middle to the end and then the sepal is is the end of that flower okay so that's petal and sepal and they took length lengths and widths of these irises and and collected this data okay a variable are the characteristics that we observe when we take measurements i used the word variable before and i'll try to not do things like that but it's just in my vocabulary so when i slip especially if we're in class make sure to tell me okay so the variable is usually taken in some type of study so usually we're interested in a question like i said in the first chapter and so we take data that hopefully is going to answer that question or help to answer that question for us so here's data i'm interested in talking about the different types of data that we see in the sort of matrix looking table that we see above okay so i've rewritten it on the side there are different types of variables okay so the first type of variable that i want to talk about is a categorical variable so there's categorical variable and then the other type of variable that we'll talk about is quantitative or numerical variables so first sorry i was getting ahead of myself talk about a categorical variable it's a variable whose values belong to one of a set of categories okay so what does that mean usually you think of a categorical variable as a value with as a value with no units associated with it okay so usually people think of a categorical variable as not a number okay something that doesn't have a number in it some examples of a categorical variable are a year classification in high school okay are you a freshman a sophomore a junior and senior and some people do that for college as well another one that's fairly common is something like eye color okay blue green brown so on and so forth okay but one that people usually get tripped up on is something like zip code okay so the zip code in which you live in shippensburg zip code is one seven two five seven okay where i grew up it was one seven three five six okay and we all have a zip code associated with us some way somehow but is a zip code a numerical variable or a quantitative variable and the answer is no because there aren't any units associated with it okay when we talk about quantitative or numerical variables they are numbers but they need units associated with them so we can tell the differences so we can tell a difference between uh two values and so what i mean by that is if i look up at the data and something like and i look at a variable like sepal length okay so it has different values 5.1 and 4.9 in order to be a quantitative variable it needs units associated with that and there are it could be centimeters probably is what these are taken in and there is a difference if i uh look at the first two irises and ice and i subtract the two okay i subtract the two and i get 0.2 so 5.1 minus 4.9 is 0.2 does that difference have any meaning to you and the answer should be yes okay the first iris has a sepal length of 0.2 greater okay so the first iris has a longer sepal length and that has meaning to us but if i uh subtract zip codes 17356 minus 17257 i get a 101 but what is a difference of 101 and zip code mean okay i don't think there actually is one so there is no difference so operations like addition and subtraction multiplication they don't have any meaning with quantitative variables that i think that's the easiest way to remember that there aren't units associated with the value even though the value may be a number okay and not to get off on a rant but oftentimes when people ask you a survey on a scale from one to ten what do you think those questions are actually pretty terrible questions to ask okay because unless you put units associated uh like put units on those questions it's really hard to tell the difference between a four and a five okay you know that maybe four is greater than five but uh like a difference of one has no unit associated with it and so it's really hard to tell a difference okay so if i ask people on a scale of like one to ten how angry are you okay and you say a six and someone else has a seven can i i know that the other person is slightly more angry but how much more angry okay and then and then further on uh when we talk about numerical summaries if you apply a numerical summary to this okay so if you apply a numerical summary to a categorical variable that can be disastrous because there's no meaning in differences okay so the and then they so back to my ranting example they'll say that the average person if i average all of those numbers together and i get an average of 4.5 that sort of tells me something but it's dangerous to do because uh people are not good at distinguishing a six or a seven so like somebody could say oh yeah i'm i'm a six okay when really they're very angry on their scale a six is probably really angry but there are other people who tend to will say like 9 because they are more likely to be extreme although they're not really as angry okay so sorry something to think about i'll stop ranting and get on with the video now but that is an important thing especially when you create a study you have to be very careful about the type of question you're asking what kind of analysis that you want to do on that data so on and so forth because the results could be disastrous if you do it wrong so back to looking at the five variables that we have above species is our only categorical variable here okay there are three types of irises setosa versicolor and virginica and so the species is a set of categories hence a categorical variable so the other one a quantitative variable is a variable whose values are numbers okay so they they really need to be numbers if they're quantitative but those numbers have units associated with them okay so example the height weight iq number of states visited there are lots of quantitative variable examples okay as long as they have units with them and they are numbers they are quantitative back to our example okay all of the other four because they have units associated with them and their numbers are numerical or quantitative okay we can actually dig deeper into those types of data for every type of variable categoric or categorical or quantitative there can actually be like subgroups to those okay so for categorical variables there's the definition again okay but there are different types so the first one is an ordinal categorical variable okay observations here for ordinal variables have an order associated with them so for instance back in my or back in the last slide i gave an example of class year in high school there is a definite order there i know that sophomores are a year ahead of freshman and then juniors are above sophomores seniors are above juniors okay con excuse me conversely eye color which was my second example there's no way for you to say that blue is above or better or anything other than above or better than brown or above or better than green so on and so forth so there's no order associated with eye color it is a nominal categorical variable okay so there's ordinal and nominal phenomenal observations have no order associated with them okay so the the what i was talking about earlier with that likert scale so when you do a scale from like zero to five or um really unsatisfied to really satisfied those scales are called likert scales lik ert i believe is how you spell it likert scales those are all ordinal categorical variables okay and so there's some order there which means you can do some things okay but you can't do everything that you can do with a quantitative variable so there are two types of categorical variables there are also two types of quantitative variables the first type is a discrete variable okay and so the discrete variable i define as that the possible values are countable okay countable and i say usually they're integers right and so if you're have taken enough calculus courses or you're a math major or you're mathematically inclined there can be an infinite number of values that are countable okay so if i start counting from 1 2 3 and then i count all the way up until i can't possibly count anymore and that becomes infinity that's called countable countably infinite okay so uh when things are countable then they're discrete okay but what we what we'll usually deal with in this class are integers okay and then there's a continuous variable and how you define a continuous quantitative variable are that the possible values form an interval interval okay form an interval so uh for this instance can you tell me how many numbers there are between zero and one okay up to an infinite number of decimal places so the number of unique numbers between zero and one okay the interval between 0 and 1 is uncountable there's an uncountably infinite number there anything that follows that sort of uh description is called a continuous variable so uh for the end of this video and the first your turn i'm going to give you another data set on diamonds okay this is very popular data set to work with in the statistical and data science-y world okay that's the first time i mentioned data science but i will mention it a lot the diamonds data set has data on somewhere around 50 to 60 000 diamonds all right and for each diamond there are a number of variables that you can uh talk about so you can talk about cut okay the cut of a diamond uh the most ideal cut is the ideal cut diamond and then it goes ideal premium very good good and fair okay you can also talk about the color of a diamond so usually when people think about diamonds they think about colorless diamonds that you can see through so on and so forth but diamonds can range from colorless to yellow okay so uh there's colorless to yellow a diamond can be yellowish okay then there's also the clarity okay the clarity of a diamond okay and this is on a sort of grading scale the highest is flawless okay but then you can go down internally flawless all the way down to what's called included okay and if you're interested you can look these up online and they will show you pictures of each okay the clarity is that those included diamonds are probably going to be pretty opaque in the middle okay but a flawless diamond is probably going to be pretty clear right so there's cut color and clarity and then there's a bunch of other stuff okay two things that you'll probably know or certainly can understand are carrot how many carrots a diamond is certainly we know that's an important consideration we talked about a diamond and also the price of the diamond then there are a number of other numerical and i'm giving some of this away there are a number of a number of other numerical measurements that you can take on a diamond as well okay depth and table are two examples of that so what i want you to do is i want you to do what we did for the iris data set but now with the diamonds data set so i want you to identify each variable okay as categoric categorical or quantitative okay and then tell me if it's nominal or ordinal if it's quantitative or continuous or discrete sorry if it's categorical or continuous or discrete if it's quantitative okay so just label each of the seven that i have here as uh categorical or quantitative and then tell me which subgroup they're in

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