İstatistik Kavramlarına Giriş Özeti

Well, good morning. I hope you are doing good. Today we are going to start with statistics. We're going to start pretty much with, like I said, just some of the vocabulary that we're going to be using the entire semester, but it's a good idea to get this framework down. That way you know what I'm talking about and I know what you're talking about. Good morning. So, we'll start off with section 1.1. We're going to go kind of quickly through the first chapter because it's mostly just vocabulary. So if you're keeping track, we're on 1.1. Just some key words. You see, in statistics... unlike a lot of other math, we have some unique words that we're going to talk about. See you, gotcha. The first thing we're going to talk about, and we talk about this a lot, is data. What do we mean by data? Do you guys know? Have you ever heard the word data before? StarCraft. Yeah, I love that guy. The little android guy. You have no idea what I'm talking about, do you? Data, he's like the white skin, like totally out there. He's the robot on Star Trek, like the next generation. I am a dork, I don't care, whatever. When we talk about data in this class, really all we're talking about is observations. The information that we're collecting, whether it be from people, like you're given a poll, or scientific data, numbers, all the information that we collect is called data. So really it's just like our observations. So when we say data, what we mean are any observations that you have collected. This guy's a noisier over there. Do you hear that? It kind of sounds like that. I'm a little afraid, intimidated. So any observations that have been collected, that's what we're calling data. Now this class, what you're doing here, why this class is called statistics, is because what we do with this data is we collect it, we analyze it, we summarize it, we interpret it, and interpretation is very important for us, and we make decisions based on those interpretations. That's the class of statistics. That's what we're going to be doing here. So we're taking this data, and we're going to do a whole bunch of stuff with it. And all the things that we do with it fall under the classification of statistics. So if you ever wondered what in the world is statistics? Well, it means this whole set of things we're doing to data. It means that we're collecting it, which we just talked about. We're going to learn how to analyze it, summarize it. Interpret it and draw conclusions from it because basically if you go to your boss in the future and you say, Hey boss, I just collected some data and you haven't done any work on it. He's going to go, great, what's that mean for me? I really don't care. to be able to do is get towards the latter part of this. Collecting it, great, that's where we're going to start. Analyzing it and summarizing it, that's going to be like chapters two and three. That's good stuff to know. But what we want to get to is the next parts. These last two parts are like the framework for statistics. We want to interpret it. And most importantly, we want to draw conclusions from it. Collect, analyze, summarize, interpret, and draw conclusions from data. I see data as pretty useless unless you can use it in your decision-making process. A lot of times people will go collect data and then not know what to do with it. All you have then is wasted time and useless information. If you can't do these things, collect it. You need to know how to get it and get it appropriately so you don't have biased data. And that's really important. We'll talk about that in a minute. But if you don't know how to do the rest of this junk, this part really doesn't matter. And the data itself really doesn't matter. You kind of get the point of that? You've got to be able to get here for this class to make any sense. And that's what we're going to get by the end. Okay, a couple of the words that we're going to be using. One of them is a population. Now, we talk about populations in real life all the time, right? The population of people in the United States is about 300,000. What, 70 million people or something like that? That's everyone in the United States. And that's kind of what we're talking about when we say the word population. What we say, or what we mean when we say population... is the complete set of all elements to be studied. So that means like everything that belongs in the group you're looking at. So we can have a different kind of meaning for the things we're talking about. For example, the population of this classroom would be everyone in here right now. That would be the population. The population of this college, if we're talking about students at the college, would be everyone that's attending this college. Does that make sense to you? The population of California, for example, would be everyone in California. California, but we wouldn't really be talking about those other people in Nevada or something. So population, it refers to all the elements, or sorry, the complete set of elements that are being studied. Now answer this question. Do you think it's appropriate all the time to look at the entire population? Do you think people do that often? You can answer. Would it be viable to, like, you see polls, like, when we're going to have the next presidential election next year, right? Just over a year from now, we'll have another election. You're going to see lots of polls, aren't you? Like, polls, I mean, not like, polls, I mean... They ask you what you think and you say, I think this person's going to win. And usually no one has any idea what they're talking about. But that type of poll, do they ask everyone in America what they think? Have you ever been asked for one of those polls? I haven't either. I don't know where they get those. Who has? Have you really? How do you get on that? Because I seriously want my opinion heard. But no, they don't go to everybody and say, what do you think? What do you think? What do you think? What do you think? What do you think? They don't say that for every single person. In fact, in here, we have a lot of people. But I'm not going to go around and ask every single person what they think about the President. What I would do is instead of talking about a population, I might consider a smaller subset, and that would be known as, what do you think? You ever heard that word before? Sample? A sample. A sample of people. A sample is generally smaller than a population. The population would be everybody. A sample would be one subset of that population. Do you guys get the idea between sample and population? generally deals with samples because populations are usually huge or too big for you to really get any information in a timely matter. So while we have populations that's everybody we also have these things called samples and what this is is some subset of a population. It just means a smaller group. Now, after just saying that about samples, are there ever times that you need to consider the entire population? Like get a piece of information from everybody in the population. Are there times for that? In fact, we just had one last year. What was it called? when they went to your house and you got a piece of mail and you filled it out and you sent it back in. That's when you consider the whole population. You guys said it, it's the census. So if we talk about the whole population, it's not really a sample, it's a census. So if we want to talk about the entire thing, census is what we're doing. Census is collecting from every member of a population. Thank you Okay, so we have some keywords. We have this stuff called data. That's just information we're collecting. We've got statistics. That's really what we're doing here is we're collecting, analyzing, subriding, interpreting, and most importantly, drawing conclusions. We're not going to get to this part until like chapter 8. Population, we're talking about the whole group. Samples. The subset and a census would be considered getting information from the whole group. How many people by show of hands feel all right with these words so far? I know it's pretty dry so far. We're just doing the vocab. Trust me, we're going to get more into it as time goes on. Right now, it's a lot of words. Okay. Also, if you're going to take a sample, so that means you're not going to ask everybody in the population. You're going to go to a couple groups of people or one group of people and ask them what they think. Are you just going to go? Let's say about an election. for a president or something, because that's often when they do a lot of polls. So let's say the next time an election comes around, you want to figure out how you think the election is going to turn out. Are you going to go to just your friends and ask them? That might be an easy way to do it, right? Just ask them. But do you think it might be a little biased if you did that? Are you going to get a nice cross-section of America if all you do is go to your friends and ask them what they think? Do you think that's going to be a nice cross-section? that includes all different points of view, or is that just going to be a set of people who probably have similar interests that you do? What do you think? Probably the same interests. Because I mean, my friends generally aren't completely different from me. They usually have my types of likes or dislikes and maybe the same is true for you but if I just chose my friends out what I know is that I'm not going to get people all across America that have different points of views and different reactions to certain things what I'm trying to say here is that if we're going to take an appropriate sample what we have to do is make sure that it is there's a word for this random you can't pick out who you want to be in your sample if I'm going to take a survey about something that's really important I can't bias it in any way which means I can't go to people who I think will answer the way I want them to and ask them questions. Do you get why? Do you get why? Yeah or no? You can talk in here, don't worry. It doesn't really pick up your voice, it just picks up my voice. So do you get why you can't do that? I mean if you just went to people who you know they're going to answer a certain way, do you see how you could completely bias people's decisions on that? Like if they were doing a presidential poll and you went out to a very, let's say, conservative population. and you said, hey, who do you think is going to win for president? And they go, oh, Joe Schmoe, the Republican, has got it in the bag. He's got this thing. And every person in that group answered the same way, or the same thing for Democrats, it wouldn't matter. Every person answered the same way. You're going to say, I know with a 90% certainty that Joe Schmoe, the Republican, is going to be president. Do you think people who haven't voted yet might take that seriously? They probably would. They'd probably go in and go, oh, yeah, well, hey, if he's going to win, no one wants to vote for a loser, do they? No, that's... Seriously though, I mean seriously, people don't. People vote for the winner. And so if you were out there saying that, well, you know, Joe Schmo's got it. Joe Schmo's got it. He's going to win. You could actually bias decisions that way, bias people's decisions. And that's not a good thing. So whenever we're collecting data, one of the most important things we can do is make sure it's random. So if you're going to collect a sample, or if you take a sample, It must be collected randomly. It's got to be collected randomly. Otherwise, it's going to have a bias to it. Maybe something you didn't even anticipate. Now we're going to talk about how we do this in a little bit, I think it's section 1.4 or something. But for right now we're going to get into a couple other vocabulary words and then hopefully we'll get to that by the end of our day today. The next thing we're going to talk about is types of data you can collect. We do have words for these types of data. If you're going to get, if you're talking about characteristics, we use a different word when we're talking about populations and when we're talking about samples. If you're talking about characteristics of a population, so you say, all the people in the population, this is what we're implying on them, that's called a parameter. So when I say the word parameter, What we're talking about is a characteristic of a population. So population, parameter, you can think of it like PEP. Parameters go with populations, not like PP, but you know, PP. Population, parameter. The other thing, when we're talking about samples, that is the word we use for this class, a statistic. A statistic has to do with samples. So while a parameter is a characteristic of a population, a statistic is a characteristic of a sample. Population parameter, sample, statistic. SS, PP, SS. Kind of clues you in on what's going on here. Now, don't you be wrong. These things could be talking about the same thing. It just depends on what group you're referring to, okay? So if we're talking about... about hair color, if you found out everybody's hair color in America, that would be a parameter. The parameter would be everybody is blonde or something like that. That would be a characteristic of the entire population. We took a sample and talked about those characteristics, then we just referred to them. as statistics. They could mean the same thing, it's just what group you are referring to. Does this make sense to you? Raise your hand if it does. You feel okay with the difference between parameters and statistics. Which one is a population-based thing, parameter or statistics? Parameter. Say it with me. Parameter. Actually, you said it and I didn't even say it. And which one goes with samples? Statistics. That's what this class is based on. We're going to be mostly based in samples, sample statistics. That's what we're going to be doing. Okay, so we've got data, statistics, populations, the whole group, samples, the small group. If we're talking about characteristics of populations, we're in parameters. If we're talking about characteristics of samples, we're talking about statistics. They're both characteristics, it's just what group you are referring to. Now we can move on to the types of data.

That way you know what I'm talking about and I know what you're talking about. Good morning. So, we'll start off with section 1.1.

We're going to go kind of quickly through the first chapter because it's mostly just vocabulary. So if you're keeping track, we're on 1.1. Just some key words. You see, in statistics...

unlike a lot of other math, we have some unique words that we're going to talk about. See you, gotcha. The first thing we're going to talk about, and we talk about this a lot, is data. What do we mean by data? Do you guys know?

Have you ever heard the word data before? StarCraft. Yeah, I love that guy. The little android guy.

You have no idea what I'm talking about, do you? Data, he's like the white skin, like totally out there. He's the robot on Star Trek, like the next generation. I am a dork, I don't care, whatever. When we talk about data in this class, really all we're talking about is observations.

The information that we're collecting, whether it be from people, like you're given a poll, or scientific data, numbers, all the information that we collect is called data. So really it's just like our observations. So when we say data, what we mean are any observations that you have collected. This guy's a noisier over there.

Do you hear that? It kind of sounds like that. I'm a little afraid, intimidated. So any observations that have been collected, that's what we're calling data.

Now this class, what you're doing here, why this class is called statistics, is because what we do with this data is we collect it, we analyze it, we summarize it, we interpret it, and interpretation is very important for us, and we make decisions based on those interpretations. That's the class of statistics. That's what we're going to be doing here. So we're taking this data, and we're going to do a whole bunch of stuff with it. And all the things that we do with it fall under the classification of statistics.

So if you ever wondered what in the world is statistics? Well, it means this whole set of things we're doing to data. It means that we're collecting it, which we just talked about.

We're going to learn how to analyze it, summarize it. Interpret it and draw conclusions from it because basically if you go to your boss in the future and you say, Hey boss, I just collected some data and you haven't done any work on it. He's going to go, great, what's that mean for me?

I really don't care. to be able to do is get towards the latter part of this. Collecting it, great, that's where we're going to start.

Analyzing it and summarizing it, that's going to be like chapters two and three. That's good stuff to know. But what we want to get to is the next parts. These last two parts are like the framework for statistics. We want to interpret it.

And most importantly, we want to draw conclusions from it. Collect, analyze, summarize, interpret, and draw conclusions from data. I see data as pretty useless unless you can use it in your decision-making process.

A lot of times people will go collect data and then not know what to do with it. All you have then is wasted time and useless information. If you can't do these things, collect it.

You need to know how to get it and get it appropriately so you don't have biased data. And that's really important. We'll talk about that in a minute.

But if you don't know how to do the rest of this junk, this part really doesn't matter. And the data itself really doesn't matter. You kind of get the point of that?

You've got to be able to get here for this class to make any sense. And that's what we're going to get by the end. Okay, a couple of the words that we're going to be using.

One of them is a population. Now, we talk about populations in real life all the time, right? The population of people in the United States is about 300,000. What, 70 million people or something like that?

That's everyone in the United States. And that's kind of what we're talking about when we say the word population. What we say, or what we mean when we say population...

is the complete set of all elements to be studied. So that means like everything that belongs in the group you're looking at. So we can have a different kind of meaning for the things we're talking about.

For example, the population of this classroom would be everyone in here right now. That would be the population. The population of this college, if we're talking about students at the college, would be everyone that's attending this college.

Does that make sense to you? The population of California, for example, would be everyone in California. California, but we wouldn't really be talking about those other people in Nevada or something. So population, it refers to all the elements, or sorry, the complete set of elements that are being studied. Now answer this question.

Do you think it's appropriate all the time to look at the entire population? Do you think people do that often? You can answer.

Would it be viable to, like, you see polls, like, when we're going to have the next presidential election next year, right? Just over a year from now, we'll have another election. You're going to see lots of polls, aren't you?

Like, polls, I mean, not like, polls, I mean... They ask you what you think and you say, I think this person's going to win. And usually no one has any idea what they're talking about.

But that type of poll, do they ask everyone in America what they think? Have you ever been asked for one of those polls? I haven't either.

I don't know where they get those. Who has? Have you really?

How do you get on that? Because I seriously want my opinion heard. But no, they don't go to everybody and say, what do you think?

What do you think? What do you think? What do you think? What do you think? They don't say that for every single person.

In fact, in here, we have a lot of people. But I'm not going to go around and ask every single person what they think about the President. What I would do is instead of talking about a population, I might consider a smaller subset, and that would be known as, what do you think?

You ever heard that word before? Sample? A sample.

A sample of people. A sample is generally smaller than a population. The population would be everybody.

A sample would be one subset of that population. Do you guys get the idea between sample and population? generally deals with samples because populations are usually huge or too big for you to really get any information in a timely matter.

So while we have populations that's everybody we also have these things called samples and what this is is some subset of a population. It just means a smaller group. Now, after just saying that about samples, are there ever times that you need to consider the entire population?

Like get a piece of information from everybody in the population. Are there times for that? In fact, we just had one last year.

What was it called? when they went to your house and you got a piece of mail and you filled it out and you sent it back in. That's when you consider the whole population.

You guys said it, it's the census. So if we talk about the whole population, it's not really a sample, it's a census. So if we want to talk about the entire thing, census is what we're doing. Census is collecting from every member of a population.

Thank you Okay, so we have some keywords. We have this stuff called data. That's just information we're collecting. We've got statistics. That's really what we're doing here is we're collecting, analyzing, subriding, interpreting, and most importantly, drawing conclusions.

We're not going to get to this part until like chapter 8. Population, we're talking about the whole group. Samples. The subset and a census would be considered getting information from the whole group. How many people by show of hands feel all right with these words so far?

I know it's pretty dry so far. We're just doing the vocab. Trust me, we're going to get more into it as time goes on.

Right now, it's a lot of words. Okay. Also, if you're going to take a sample, so that means you're not going to ask everybody in the population.

You're going to go to a couple groups of people or one group of people and ask them what they think. Are you just going to go? Let's say about an election.

for a president or something, because that's often when they do a lot of polls. So let's say the next time an election comes around, you want to figure out how you think the election is going to turn out. Are you going to go to just your friends and ask them? That might be an easy way to do it, right?

Just ask them. But do you think it might be a little biased if you did that? Are you going to get a nice cross-section of America if all you do is go to your friends and ask them what they think? Do you think that's going to be a nice cross-section? that includes all different points of view, or is that just going to be a set of people who probably have similar interests that you do?

What do you think? Probably the same interests. Because I mean, my friends generally aren't completely different from me.

They usually have my types of likes or dislikes and maybe the same is true for you but if I just chose my friends out what I know is that I'm not going to get people all across America that have different points of views and different reactions to certain things what I'm trying to say here is that if we're going to take an appropriate sample what we have to do is make sure that it is there's a word for this random you can't pick out who you want to be in your sample if I'm going to take a survey about something that's really important I can't bias it in any way which means I can't go to people who I think will answer the way I want them to and ask them questions. Do you get why? Do you get why?

Yeah or no? You can talk in here, don't worry. It doesn't really pick up your voice, it just picks up my voice. So do you get why you can't do that? I mean if you just went to people who you know they're going to answer a certain way, do you see how you could completely bias people's decisions on that?

Like if they were doing a presidential poll and you went out to a very, let's say, conservative population. and you said, hey, who do you think is going to win for president? And they go, oh, Joe Schmoe, the Republican, has got it in the bag.

He's got this thing. And every person in that group answered the same way, or the same thing for Democrats, it wouldn't matter. Every person answered the same way.

You're going to say, I know with a 90% certainty that Joe Schmoe, the Republican, is going to be president. Do you think people who haven't voted yet might take that seriously? They probably would.

They'd probably go in and go, oh, yeah, well, hey, if he's going to win, no one wants to vote for a loser, do they? No, that's... Seriously though, I mean seriously, people don't.

People vote for the winner. And so if you were out there saying that, well, you know, Joe Schmo's got it. Joe Schmo's got it.

He's going to win. You could actually bias decisions that way, bias people's decisions. And that's not a good thing. So whenever we're collecting data, one of the most important things we can do is make sure it's random. So if you're going to collect a sample, or if you take a sample, It must be collected randomly.

It's got to be collected randomly. Otherwise, it's going to have a bias to it. Maybe something you didn't even anticipate.

Now we're going to talk about how we do this in a little bit, I think it's section 1.4 or something. But for right now we're going to get into a couple other vocabulary words and then hopefully we'll get to that by the end of our day today. The next thing we're going to talk about is types of data you can collect. We do have words for these types of data.

If you're going to get, if you're talking about characteristics, we use a different word when we're talking about populations and when we're talking about samples. If you're talking about characteristics of a population, so you say, all the people in the population, this is what we're implying on them, that's called a parameter. So when I say the word parameter, What we're talking about is a characteristic of a population.

So population, parameter, you can think of it like PEP. Parameters go with populations, not like PP, but you know, PP. Population, parameter. The other thing, when we're talking about samples, that is the word we use for this class, a statistic. A statistic has to do with samples.

So while a parameter is a characteristic of a population, a statistic is a characteristic of a sample. Population parameter, sample, statistic. SS, PP, SS. Kind of clues you in on what's going on here.

Now, don't you be wrong. These things could be talking about the same thing. It just depends on what group you're referring to, okay?

So if we're talking about... about hair color, if you found out everybody's hair color in America, that would be a parameter. The parameter would be everybody is blonde or something like that.

That would be a characteristic of the entire population. We took a sample and talked about those characteristics, then we just referred to them. as statistics.

They could mean the same thing, it's just what group you are referring to. Does this make sense to you? Raise your hand if it does.

You feel okay with the difference between parameters and statistics. Which one is a population-based thing, parameter or statistics? Parameter. Say it with me. Parameter.

Actually, you said it and I didn't even say it. And which one goes with samples? Statistics.

That's what this class is based on. We're going to be mostly based in samples, sample statistics. That's what we're going to be doing. Okay, so we've got data, statistics, populations, the whole group, samples, the small group. If we're talking about characteristics of populations, we're in parameters.

If we're talking about characteristics of samples, we're talking about statistics. They're both characteristics, it's just what group you are referring to. Now we can move on to the types of data.

Transcript for:İstatistik Kavramlarına Giriş Özeti

Transcript for:
İstatistik Kavramlarına Giriş Özeti