 Statistics and Excel introduction. Got data? Let's get into it with statistics and Excel. Course first a word from our sponsor. Well actually these are just items that we picked from the YouTube shopping affiliate program but that's actually good for you because these aren't things that were just given to us from some large corporation which we don't even use in exchange for us selling them to you. These are things that we actually researched, purchased and used ourselves. Acer 27 inch monitor. I've been using an Acer monitor as my primary monitor for a few years now. This is the first Acer monitor that I have used after having used a series of different brands of monitors in the past. The Acer monitor has been performing well and I'm trusting the Acer brand more and more as I use the monitor. I have a 27 inch monitor which I think is ideal for what I do which is of course the screen recording and the editing. If you would like a commercial free experience consider subscribing to our website at accountinginstruction.com or accountinginstruction.thinkific.com where we have many different courses. You can purchase one at a time or have a subscription model given you access to all the courses. Courses which are well organized have other resources like Excel files and PDF files to download and no commercials. Structure and intentions. This course deals with the analysis of data and statistics emphasizing its essential role in our everyday decisions and understanding of the world. We generally have a sense that statistics it's important. It's involved in many different things but usually we don't have a sense of just how important statistics is and just how involved it is in many areas of our lives including our personal decision making, our place of employment, our entertainment, our news for like let's take the news for example. Just about every claim that is made by the news we expect to be supported by some form of statistics. Now unfortunately the news alludes to this usually by saying the experts say this so they refer to the experts. But when we hear the experts we don't assume that they said something these experts and the world changed to conform with what the experts say. We assume that the experts took some kind of data made some analysis about it that gives some some insight about the world. When we think about entertainment for example when we watch sports watching the sports is entertaining but usually we want more information about the sports we want to know the stats of the team we want to know the stats of the players we're comparing different teams to other teams and players to different players we compare in team stats in the current year to prior years and so on but in the prior year they had different rules so how can you compare the current player to the prior year player and so on we get deep into these statistical analysis and like when you're thinking about sports really sports is just a form of a job so all of that kind of analysis that people apply to sports really applies to our place of employment oftentimes as well if we go into our our boss's office and we're like we would like a raise it's not enough to say well look I could really use some more money right now it's we have to say something like look that this is what we're doing this is why we deserve a raise based on this kind of statistical type of analysis and clearly with our personal decision-making when we make purchases at the store or purchase like a car we're usually going to do some kind of statistical analysis to help out with that decision-making process we don't have to we could just be like hey look the label I like the label on this one and the other one the labels not as you could do something like that but usually we're probably better off if we do some kind of analysis right so we will examine data's role in various fields such as medicine finance weather prediction education and social trends so as we do our example problems we'll try to pull data sets for many different areas so we get an idea a sense of where statistics is involved in many different areas and we can't of course do examples on all the areas that statistics is involved in because again statistics is involved in many many many areas of our lives it's also reflected by the way if you look at if you look at college college degrees any field that you go into is usually going to have some kind of statistical requirement in it so you can't really get away from the statistical requirement not because they want to hurt you with statistics and you don't want to take it or something like that but because statistics is going to be important no matter what field you are in clearly science it's going to be important clearly you know any kind of economics or finance it's going to be important but even if you're in like English it still can be quite important because you're going to be analyzing different texts and you might analyze things like well how often do they use this rhetorical trope how often does Shakespeare use this kind of rhetorical trope or how often do they use a certain phrasing of words you might actually be using English to try to determine who the author was of a certain thing and one way you might do that is to look at the words of and how often the words are used to try to determine if it's the same person that wrote one thing versus another thing so even in like English and philosophy and that kind of stuff statistics is going to be important so ever it's crucial to remember that the data themselves do not inherently hold meaning but require appropriate interpretation through statistical tools so we often hear the term the data is telling us this now note that it's not too bad to hear that term because but what we're really saying if someone says the data is telling us this they're saying hey look I took this data which is just random it's not telling us you know the data is just data we can't get anything from just random data we took the data and we compiled it in such a way that we think we can infer something from the data that is true about the world so the compiling of the data is going to be important just data itself doesn't just tell us something and notice that that's really important to note because today we're we have data we have too much data data is everywhere so so we have to put the data into a format that it tells us something otherwise the data is really useless to us so the two sides of data are use use and misuse of statistics so we will explore how data can be used to clarify and mislead echoing Mark Twain's attributed quote there are three kinds of lies lies dang lies and statistics I cling that up a little bit but we've probably heard that that phrase there's lies dang lies and statistics so the idea being there is that the statistics are meaningless it's useless we're not getting anything from the statistics because people just lie with statistics however note that that there's truth to that you can lie with statistics but the same thing can be said of just words right people can lie with words words are just a tool to help us to put meaning to the world to try to convey meaning from the world to other people so we can use the same thing with words we can take something that is true with words and then apply go down a bad path from there so what does a liar usually do a liar is going to take something true and they'll say this is true this is a fact and you're gonna say yeah it is a fact right and then and then they distort it from that point and we end up going down a road because there's been entered into the into the to the to the lie has been entered in some place that we didn't catch right because it started out with truth then the lie happened and now we went down a road and we don't even know who we are or what we are anymore because somehow we we got that distortion in fault and if you think about this from a philosophical perspective people some people think that evil in essence is a distortion of the truth right because everything is good so where does evil come from some kind of distortion of what is good at a lie of deception has been entered into it and we've taken a wrong path so what are we trying to do with our tools whether it be language or whether it be statistics we're trying to find truth we're trying to be alignment with what actually is and that's the general strategy note also that there's some philosophies that basically have the idea that there is no truth right that everything is relatives and the tools that we use which are even language even language itself is just is just a relative tool there's no actual meaning to it and if you adopt that kind of mentality or that that philosophy it becomes difficult to apply reason itself and it's going to be difficult to do statistics right because the idea of logic is that there is truth you you almost have to accept that a priori you have to that's going to be your first assumption there's truth about the world and we're using our tools that being language that being statistical tools in order to infer what that what that truth is so that it will be revealed to us through through some of some of these are some tools that we can use to reveal the truth so obviously the same things can be done with statistics we can use one piece of statistics one little thing we say hey that's true look at this is true you can't deny that and then they go from there and and they have missed they introduce the lie from that point and then we go down a road that's that's not correct just like we do with words but that's not the statistics fault just like it's not the words fault you misused the statistics so what we want to do is say just like we're pretty good at seeing what a liar is with words we want to apply those same skills and see how people lie with statistics which is almost as important if not as important as seeing how people can tell the truth with statistics so that we can tell the truth from the world with the tools and we can see when how people will mislead with these same tools not blaming the tool but blaming the liar that's using the tool to mislead or possibly making a mistake if we're having a good so so that the some people will say however it's it's also easier to lie without statistics in other words if we didn't have the tools of statistics that would allow us to catch the problem then then everything would be relative like if there was no truth there was no statistics it's actually easier you know to lie because there's no way to really test what is actually you know true so purpose of statistical tools this course aims to arm you with the principles and ideas of statistics necessary to draw meaningful conclusions from data we will cover probability and how it aids us in understanding and quantifying the unknown so what we would like to do is be able to apply these tools to try to be able to see when people are lying and see how we can extract truth from a set of data not everything can be quantified notice that's another thing that people often haven't have an issue with they take issue with and say well not everything can be quantifiable there's but a lot of things can be right and the thing and the more that we can actually apply a a rigorous test you know in those areas we want to we want to be able to do that if we can fairly measure something we want to be able to fairly measure something recognizing that not everything can be measured you know exactly that way but primary challenges in statistics so you can kind of combine statistics or group statistics into two main buckets so the one bucket comprehensive data analysis and the second being the statistical inference now in my experience most people when you say statistics kind of think about this second concept because oftentimes they're thinking about polls for example election polling or trying to find out something about a population from a sample which is a common practice within statistics scientific practices often are doing a similar type of thing we're taking some kind of sample and trying to understand the full population the other would be a comprehensive data analysis so in this case this challenge involves extracting meaning from a complete data set for example using comprehensive records of a university students we might predict future performance based on incoming SAT scores and high school rank so in other words we might have the full data we know all the data about the SAT scores and their high school performance we have all the data we're not trying to infer what everybody's SAT scores were we have all the data what we're trying to do is organize that data set to possibly give us some understanding about something possibly in the future right such as college performance that's going to be happening in the future whereas the statistical inference this challenge relates to making inferences about a larger population based on a sample so examples include predicting election outcomes based on a poll or predicting the average height of a population based on a sample data so this is the one that like if you're trying to predict who's going to win the poll then you can try to see what people are going to vote for right early and so what you're doing is you're taking a sample and trying to infer what's going to happen in the full population a lot of scientific analysis if you're trying to determine something you know about a population or species or something like that or if you'd like the height of something or whatever you can measure you're going to take a sample you're going to take a sample of the population and then see if you can make some kind of statistical analysis about the full population and so so we'll take a look at those in the future so course goal of this course will provide you with numerous intriguing examples demonstrating the practicality and wide-ranging applicability of statistical analysis we will look beyond the notion of merely plugging data into formulas to comprehend the logical foundation and strategies underpinning statistical reasoning in other words oftentimes when people are learning statistics they might be taking a statistics course in college just because they're required to take it and they're just learning formulas and how to plug numbers into formulas to get the proper result the answer so they can pass the college course the problem with this approach is that you're not really getting a deep understanding as to the why you're plugging the numbers into the formula and what are the underpinning principles for plugging those numbers into the formulas now oftentimes to get a more full picture of the rationale for the statistics we're doing we can use pictures we can use charts and graphs and excel is a great tool to help us to do that now it used to be that when we learn statistics we would have charts and graphs that would be in a demonstration in like a lecture format but then when we actually do the statistics we're just plugging numbers into formulas but these days of course we have the application of excel which allows us to create our own pictorial formats of the charts and graphs much more easily and we'll try to do that as we go through our practice problem so the other go the other problem oftentimes is people that learn how to do formulas in excel without understanding the math or the rationale underpinning the formulas in excel so excel will actually shorten up the math so it's even easier to plug the numbers into excel to get a result however again the problem is that if we don't understand the rationale for the data we're putting into the system the results not going to give us as much information that we can make decisions on as it otherwise would so our ultimate goal is to import a genuine understanding of one of the most useful influential and universal modes of reasoning in today's world so again just noting the common use of statistics it's basically everywhere it's not too difficult once we understand the rationale of the statistics and if we understand the statistics that are being used everywhere we have a tool that really a lot of people don't have that can be used you know quite often so this understanding is becoming increasingly vital as technology continues to facilitate access to more significant data sets and advanced analysis techniques so clearly with the internet that we're flooded with data these days so we the getting the data is not the problem it used to be it's still a problem in some areas but there's a lot more data it used to be that getting the data was a lot more difficult now we have a whole bunch of data all over the place and the problem often is to try to put that data in a format that we can make decisions on now if we can't understand how to do that we're going to be reliant on quote the experts in quote in order to compile the data and tell us what the data means and I'm personally less or I guess I'm more skeptical about relying on the experts I would rather have some idea of what the experts are doing to you know interpret the data so in those skills I think I'll be useful in in everyday life so tools obviously we're going to be using math tools for statistics the math is important because that'll help us to get the underpinning principles and concepts but we'll also be using Excel now Excel will allow us to use functions which shorten up the math so sometimes it's useful to look at the equation the math equation because we can intuit meaning from the equation but it's also useful in Excel to get them the answer more quickly but Excel can also be used to make those pictorial representations now I just want to point out oftentimes when we think about pictorial representations of data many people kind of have an a disparaging idea in their mind of that as though there's someone like Einstein with his crazy frizzy hair and whatnot you know the mad scientist is able to actually do all the calculations and is learning everything just strictly from looking at a formula this is the this isn't true I don't think but this is the idea that we have and then in order to dumb that down to give to give it like like we're children like a picture book they have to make a an image of that to explain the genius Einstein to us to us mortal people right so then they make an image of the data so they can tell it to us but that's not really how it works in practice right it doesn't even if you're you are the Einstein you are you are often working in images as well as formulas it's often the other way around I mean Einstein was actually quite good at visualizing things so so he's famous for basically visualizing what it would be like if he was falling at the same speed as a beam of light right what would that look like so I so he actually kind of envisioned that which helped him to formulate his mathematical formula so it's kind of difficult to say you know which came first his visualization or the you know he had this idea in his head so the pictures are quite important even if you're like an Einstein right that the point is that we want to have a pictorial representation because the pictorial representation will tell us things about the data that a formula representation may not and just organizing the data certainly you know will not tell us or just a random set of data certainly will not tell us excel great tool for being for being able to do that and many people of course have access to excel now so the ability to put some data in excel and do some some basic analysis of it can help you in any area like I say like your personal decision-making and your and your work decision-making you're you're comparing yourself to other performance at your job you know what kind of purchase purchases you should make even your entertainment when you're looking at who's the better sports player and this kind of stuff being able to put the data in excel useful so two key terms in statistics data and statistics pose a common grammatical question are they singular or plural now notice that when we think about statistics I do want to point out that terminology is important because we want to come up with actual definitions because the definitions will help us to communicate with with others about statistics but this particular kind of issue I don't think is as a problem for normal communication because if you if you if you're an English speaker the normal kind of use of statistics I think people will understand what you're saying for the most part although some people might get a little you know have their preferences on exactly how you should be using the term data when it when it comes to plural versus singular so just to point out that pointed out data this word originates from the Latin date home which signifies a single piece of information therefore data is the plural form and is typically used to refer to multiple pieces of information so when you think about data based on where it came from you say well the data is the plural and then datum would be a singular piece of data but oftentimes when people say data then they might be saying it's a piece of data right if you're saying it's one day you know people might say it's one datum it's one piece of data you know as long as you have to be somewhat specific to know what exactly what you're saying but we have this this the poll the plural and the singular is a little bit different in English than other plurals and singular is kind of the issue right because you would think it'd be like data is with an S for the plural versus the same and it was however in contemporary use data is often treated as singular when referring to a collection or body of information so clearly when you're talking about the whole set of data like if you're talking about a bunch of data a bunch of pieces of data the plural would be the data there's the multiple data if you're talking about the whole set itself now you're talking about one singular thing and and and and people will still usually refer that to that as the data data now to be more specific you might call one piece of data like that's one singular piece of data right a piece of data or datum but and then if you're talking about all of the data that is all of the data right or if you're talking about the data set you might say like well that's the data set you know or something like that to kind of try to be more specific but again statistics is is similar kind of issue because it's got this S on the end which usually indicates a plural in English a lot of English words but statistics the term statistics can can be both singular and plural depending on its context so for instance statistics is singular when it refers to the field of study so when you're talking about the field of statistics you don't say singular it's the field of statistics statistic and drop the S that sounds funny if you're an English native speaker it would sound funny to you you wouldn't do that anyways but if you think about it these words are a little bit weird with regards to English singular versus plural because of you know they're not structured with like an S for the plural so on the other hand statistics is plural when it refers to numerical facts or pieces of information derived from the data set so when you're talking about this is the this is the infrom these are the statistics that the me and the median and whatnot those now you're talking about multiple things and you're still going to say statistics right for multiple so for example the statistics from research show a significant trend okay common misconceptions about statistics is that it's about distilling complex situations down to a word or two this oversimplifies the reality of statistical analysis so simplistic perception so many people perceive statistics as a means of condense complicated scenarios into a few words or numbers this viewpoint while it captures one aspect of statistics doesn't fully appreciate the intricacy and potential statistical analysis so in other words you know we're usually as human beings we want to kind of get a quick answer to something so when we look at a set of data we basically want to say okay what does that data tell us let's go on to the next thing we do this we do the same thing with everything right we trip because otherwise we would get bogged down with too much information so we use shortcuts heuristics and things like that so like when we talk about people we can say well that person's like this he's like a he's like this kind of person or something like that as if obviously when we say that about someone it doesn't mean that that's way too simplified of or if we say a book the book is saying this or a philosopher is saying that that's what that philosopher says you know and we try to put everything into a very specific box and then we can move on to the next thing so we can learn something else but clearly if we're talking about a book or a philosopher or a person it's way that's a way too simplified if you if you want to get into a more nuanced understanding and normally the same is going to be with data it's like a book right it's a bunch of information so if you're taking if you take a whole set of data that has been compiled there are oftentimes going to be multiple stories that you might be able to compile from that data now when you look at data from a scientific perspective the point is that you're trying to trim down all the other noise so that you can test one thing at a time a hypothesis and the testing of the hypothesis but when you're looking at just a set of data and you're trying to say what is coming from that data there could be I mean multiple different stories you know coming from the data right so the reality of statistical analysis rather than being a simple summarization tool statistics provides a set of methods to analyze and interprets complex collections of information that challenge lies in extracting meaningful insights without losing significant detail from the data so the other way people often simplify the data is they'll try to break it down to one number usually the average with the mean sometimes the median and then they'll and then they'll say well that that gives us the full picture of the data but clearly it doesn't right because if we if we just if we don't know the spread of the data around the the mean or the the median then we're not we then we don't have as much information as we would if we knew the actual in the picture of it actually helps with that a lot so the need for tools and vocabulary so that suit so to accurately convey the complex information inherent in data it's crucial to develop and utilize appropriate statistical tools and vocabulary these tools help us interpret and describe data in ways that maintain a balance between simplicity and detail so clearly when we're trying to communicate more detailed information then coming up with tools that can give a more specific description about characteristics of data like the spread and stuff like that is is quite useful the pictures are still useful as well because that gives us a different visualization but the the definitions are important too so don't don't let the deconstructionist tear down our definitions of of statistical analysis because we need to we need we need to agree on terms to in order to a to actually communicate coherently about the truths that are in the world that we can discern with our our analytical tools in our senses so the ultimate goal of statistical analysis is not just to simplify but to create summary descriptions that are both straightforward and meaningful capturing the richness and diversity of the data without losing vital information so it's a balancing act that balancing act can be quite complex and is somewhat of an art form because we want to simplify the data so that we can we can find meaning from it but not oversimplify it to the point that we have actually taken meaning away or worse we we come to conclusions that are incorrect