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Listed Deiow are tne tOp annua salaries (in millions dollars) of TV personalilies. Find Ihe range vaniance and standard deviabon Ior the sample data: Given that the...

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Listed Deiow are tne tOp annua salaries (in millions dollars) of TV personalilies. Find Ihe range vaniance and standard deviabon Ior the sample data: Given that these are the top salaries, do we know anything about the variation- salaries 0f TV personalities general?(a) 2 pts The range ol Ihe sample datamillion: (Type inlegerducimal )pts Use the Show-Work Editor calculate Ihe variance and standard deviation of the samole data by hand Suicruncn; Calculator orother program output Is not #ccoptod

Listed Deiow are tne tOp annua salaries (in millions dollars) of TV personalilies. Find Ihe range vaniance and standard deviabon Ior the sample data: Given that these are the top salaries, do we know anything about the variation- salaries 0f TV personalities general? (a) 2 pts The range ol Ihe sample data million: (Type inleger ducimal ) pts Use the Show-Work Editor calculate Ihe variance and standard deviation of the samole data by hand Suicruncn; Calculator orother program output Is not #ccoptod for showing your work: Ihe standard deviaton . tho sample good estimate Ithe vanaton salares of TV personalities general? Yes, because samnln rndom bocuust cotmonrenlaite Giing %nola population Ap" No. because Ihcre oubier Ihe tamele data: Ceo Yes, because the standard deviaton Uadisad Giintalon



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TV Viewing. According to Communications Industry Forecast \& Report, published by Veronis Suhler Stevenson, the average person watched 4.55 hours of television per day in 2005 A random sample of 20 people gave the following number of hours of television watched per day for last year. $$\begin{array}{ccccc}\hline 1.0 & 4.6 & 5.4 & 3.7 & 5.2 \\1.7 & 6.1 & 1.9 & 7.6 & 9.1 \\6.9 & 5.5 & 9.0 & 3.9 & 2.5 \\2.4 & 4.7 & 4.1 & 3.7 & 6.2 \\\hline\end{array}$$ At the $10 \%$ significance level, do the data provide sufficient evidence to conclude that the amount of television watched per day last year by the average person differed from that in $2005 ?$ (Note: $\bar{x}=4.760$ hours and $s=2.297$ hours.)

Okay for this problem were given some human resource data about salaries, um, and jobs. So annual salaries for this job, you're supposed to be on average, $76,503. And the variation or, um, or school precisely. The standard deviation from this 2007 data says, uh, well, salaries go up down, or the typical deviation from the mean is 80 $8850. Okay, as with any problems, probably kind of sketch out what it looks like that's normally distributed on what we're doing with this problem is really what is looking at some likelihoods of what occurs, um, be single value and the probability of something occurring for ah, or the sample mean? So let's look at this for the single values. So what's the probability of gonna translate this right into our probability notation? So we could say, What's the probability that the branch that the human resources person there is over 100,000? All right, so let me like to draw. It kind of gives you a general idea. So really, if this is about $76,000 for the sample mean, uh, $100,000 is gonna be way over here, so it's gonna be way after the side, so the probability is pretty likely that a single person does, but it's possible. So let's see what it is that these are calculated. We've done enough of these. Now that we're gonna take a look and let the calculator look at our distributions, we know it's a cumulative density frequency. So, uh, so we know we're looking from the lower boundary of $100,000 to infinity. We're looking about that's like to draw it out. So we kind of see that we're going this. What s represented is what's on the red on the screen. We know the mean for this problem. We've been told the data tells us the mean salary is five or three, so that $100,000 salary is much higher and standard deviation a typical variation. There is only Colony 150. So pretty big wish over here to get that salary based on the averages at least. So let's see what we get. So and it is a small number, as we kind of expected. So the likelihood of that is, uh, 0.39 So I'd say the 0.4% chance that you get that. Have a salary if the average is supposed to happen in between those two. Okay, so now and it just mark this up a little bit better now. So 76 1000 for party. We want to know that, Um, what's the What's the odds of the sample mean? Salary of this branch falls between what's expected there. So you can also put the sample mean up there. So now we're looking between I'm gonna save a little bit of digits. Space years. So the sample mean between 70,000 and 80,000 Some scar, right, Kay? Okay, 17 80 k So 76 is there. We know that 70,000 and 80 thousands of nice little slice of the middle here, so we should get a mush higher number. And that's what we're trying to figure out. So, um, industry in this up. So and we'll make sure ever good notation of the probability that it's between 70 and 80,000. Party. So we're gonna use our distribution from a calculator and one of the thing here too. I think I need to write down and will be get blue. Just emphasize it. That's based on a sample size Russia gonna sample on average 20 human resource managers. So that's instead of having a single person up here just for reference here in just a single sample or one person never turn about 20 people. Um, so let's see what we get. So go back to our distribution normal CDF and we're looking at a much more representative sample here between 70 and 80,000. So it's put in 70 and 80 upper bound, and we know that means should be 76,000 right in the middle. And we've been told that the salary variation 8 50 is, uh, a little steak there. It's not 76,000 middle. It's 76,503 alone. Okay, so let's see, uh, almost people mistake there. So it's not just this, and the key part of the problem is we take our standard deviation and we divided by the square root of the sample size cause that impacts our outcomes. So it's the square root of 20 you taking your 8815 divided by the square 20 your teachers picky about that? You can show that off as a separate calculation and we'll see. That's much higher. Likely that especially the 30 if you sample 20 there. So in the end, we know the probability is 96% about so the probability that you haven't mean that exists between 70 and 80 given 20 samples. ISS 0.961

Everyone. Today we're solving problem number six from chapter two of the textbook. The essentials of modern business. This is a two part question. So the first part is quite extensive. Um, so basically, we're dealing with which television channels have shown 50 most popular TV shows, like most viewed of all time. Um, and they give you the list of the channels, and we're supposed to figure out the frequency and percent frequency. Then Graf I'm a bar graph results. And then that's before tonight and then in part three years to figure out challenge the two channels who have the most who owned the most, watched the most number of the most watched, um, shows ever. So, um, in part, a trying to get frequencies. So you basically counting up, given what they give you, how many times ABC shows up how many hands NBC shows up, probably from CBS. Years of college guys talk shows up. No, the total house B 50 they tell you that. And again, your son free conceiving of the total of 100%. So we're good to go on that. All right, so for ABC, when you count it all up I can see 50. Oh, NBC. I got 70. Same with CBS Santy and then Fox. I got only one. Um so for frequencies, you know, that's the same thing as 15 out of 50 total. So the easy being what's really nice about this? But there's a total of 50 and you need to get to the total off 100 to your little lost buying each number two 15 times two is going to be 30 30% or even see amount 17 times two years before 34% of the sea. Probably the same number, yes, and then finally, Fox is just one time student. Choose 2%. So those Europe, her son frequencies now moving on to our graph. Um, so we're asked to draw Varga on the bar graph. We're gonna show the frequencies Lobster for ABC TV have 15. We'll try and shape. It's in his best you can. And while I'm shooting it in, you guys should all know that you should label your X and Y axes as a pretty gun. It's on the X axis. I have my TV channel. I have before chance. Let's say ABC, NBC, CBS and then the number of times a channel made a top read it show. Um, that would be this. Why? I guess I just shorten it. Remember that time suffer ABC have 15 so I'm gonna write the numbers on top, just in case scales going off For what? Not integrated. It will pick you up. It's just good to write the number on top. So they know that you know what you're doing. Essentially NBC box that she'd been in 17 as well. So you draw well. I mean, started not box CBS shape. You get the Jets 17 for NBC and then 17 forces. Yes. And then finally, Fox is only one. We'll try it all right ago. It's much easier to shade in boxes. I mean, bars on the bar graph with a pen and paper, but you got to get you All right. So now that we have our shake even bars heart A's now moving on to part beat your ass to figure out the channel. The two channels I'm with the most number of times. It's actually, uh Did you see which operated show so you could look a frequency percent gets in to see which two of the highest, Obviously 17 17 Highest of the foreign office. 3 34 34% percentages. So I see that those two channels are NBC. So NBC and CBS are gonna be is going to be your final answer party. And there you have it. If you guys enjoy this with your peaceable like right next to it. I hope you all have a nice day. Thank you. For what?

Computer exercise and Chapter two and we're gonna use the data set called CEO Sal to which contains information and chief executive officers for US corporations the variable salaries annual compensation in thousands of dollars and CEO 10. This other variable is ah, prior number of years. This company CEO All right, I have imported the data center St Uff. Let's first hit. Described to see what a data set is. As we can see, we have 177 observations, 15 lying variables, and the main variables we're gonna be dealing with this question is salary, which is, um, compensation 1990 in thousands of dollars and actually going to use that log off salary. The transformation with the natural longer than we're also gonna use the variable CEO 10 I guess a sense retainer. And it's measured in It's a discreet variable. Imagine measured in a number of years. A CEO with the company. Okay, Party s us. Has to find the average Sally in the average tenure in the sample. Well, that's very easy within a couple of times already. So we're gonna hit summarize their two variables. It is ah. Is it ever salary and CEO tenure. Okay, as we can see there No missing observations. Both haven't 107 7 Observation that mean CEO salary is, uh is this number right here? Mother played by 1000 because this $1000 So it's 865,000 864 U. S. Dollars. That's a lot of money. And the average CEO tenure is 7.9 to 5 years. Almost eight years as we're going to see, the minimum salary is 100,000 per year. No, that's not too bad. Maximum salary is five point $3,000,000. And, um, in part B, how many CEOs are in the first year? CEO? That is how many CEO tenure minor variables are equal to zero as we know already. Don't do. Ah, civil counted with a variable CEO 10 and the condition is that is equal to zero. All right, so which is half five CEOs with 10 year olds zero years on the other hands? What is the longest tenure as a CEO? We've already seemed that right here when we did the descriptive analysis of the CEO 10. Bye Mary variable. The maximum is 37 years and just out of curiosity. Let's see how many people have sealed in your 37 years. Just two people. Two people have spent basically their whole adult life. A CEO. Is that company okay? In part C, we need to estimate the simple regression model. Ah, the dependent variable would be the national law game of salary. And in the explanatory part, that right hand side, we have a constant term and CEO tenure, plus the return of a disturbance. We need to report the results in the usual form raised. Do as we explain the previous video which hit rag regression followed by the dependent variable look of salary. We did not need to include an intercept because state I includes right away automatically and then the dependent they're independent. Variable will be CEO 10 And of course, we don't need to include the sermons term. It is applied. It's include automatically and we're just going to run the simplest aggression. No robust air terms, no corrections. All right. As we can see, number of observations that correct one. The one we had. I would describe the data set so nothing went wrong. The are square. It is 0.0 13. It's extremely low. That would say, um okay. And, uh, the quantities of interest are these ones right here. The are estimated value for the constantly 6.5 and our slope coefficient with seo 10 0.0 97. So I've written down the estimate equation right here. Estimated log salaries equal to the heir to the constant term plus a slope coefficient. Time CEO 10. Number of variables at 1 77 Our score 0.0 13. Okay. And then we're being asked two, uh, to ah state. What is the approximate predicted percentage increase in salary given one more year as a CEO. All right, so remember, here we don't have ah level levels. Regression will have a love levels. Meaning that the dependent variables in drugs as your longer than an independent variable is in levels. No transformation. So we obtained the approximate percentage change in salary and by approximately mean is gonna be in love. Different. So in his approximate percent of change. And on top of that, we're doing with average pointedly, so we don't need to take his value 100% at face value. Right? Is an approximate average change, and we need to construct the difference in such a way. Uh, as in a ziff, we need to interpret as that one point change in the independent variable. So you can see if we change the independent variable was his tenure by one. Well, we multiply the coefficient and cementing her by 100. And then we get that percentage change because we have been log variable on the left inside. Therefore, one more years of seo is predicted to increase salary by almost 1% 0 for 97% almost 1%. Okay, so remember when we haven't loved levels regression, we estimate our stoke coefficient we multiplied by 100 waste made as the percentage changed in that, um dependent variable. And the last thing I would like to add is that do we Shall we take this prediction seriously? Well, the first thing we need to check is that here at sea, on tenure, the corresponding T value from the T test, the critical values very small and affords the correspondent p. Valerie's very high, which means that this estimate right here it's not statistically significant, at least in the conduct of the by virus progression we're running. And of course, there's socially flaking the very lower squared in the marginally Hi critical, a critical Bali for the F test and this critical while you tells us that our regression is slightly better than one with just a constant term. So what? Leasing this context, the CEO tenure variable does not really explain much of the variation in in log salary, and this prediction should not be taken at face value.

In this problem we have to find a frequency distribution for the given data of the viewing Audiences and Millions for the Top 20 Television Shows. And here we are going to use the cut point grouping as a data or continuous. So it's given we have to use the first class. Wolf 12 213. Okay. Mhm. So here. Yes. Yeah. Medium season limb. Yeah. Uh huh. Mhm. So the first classes Child 213. Yeah. Okay. Mhm. To the next class is 13 to wonder. 14. This will be 14 two Under. Hmm. So it goes still 20 10 to only 16. Thanks, Jonathan Under 17. Mhm 17 to Mhm Under 18. Mhm 18 to UNDER 19, 19, 2. And I can t So first twin Chill. The 13 will be Mhm. And one. Okay. Three. Next will be 13 to 14 Will be 1, 2. Yeah. 345 Yeah. Yeah. Yeah. 14 to 15 would be 12 12 Beautiful. 15 to 16 will be Mhm. Mhm. 123 in four. 16 to 17 would he run? And 17 Tueting Divine. Mm 18-1921. Year one. If you see the total of the sample size is 20. Now we have to find out later frequency distribution which will be the corresponding frequency divided by the total sample size. Which is 20. So three by 20 will give us 0.15, Extremely five x 20. She's going to be 0.25. It's just full by 20. Just point to Thanks jess one by 20. Yeah, That is .05. Yeah. Right. Yeah. Now facing the daughter, It's going to be one. We have to construct construct instagram. Now here is instagram. Yeah. For the frequency, the top broadcast shows. So if we see 12 UNDER 13, we have taken the classes, cut point grouping glasses along the Yeah, horizontal axis and the frequencies along the vertical axis. So for 1213 It will be three. So it's your 13 and 14. Just five, 14 and 15 before it's going to before the rest will be one each. Now, this is the instagram for the later frequency, which is our option D. So we have taken classes Along the horizontal axis and related frequency along the vertical axis, and the vertical bar presents a corresponding later frequency of the particular class, 12- 13 and 13. The related frequency is .15, which is here for the next class it is .25. For the next two classes, it is point to here And for the next four classes at this point 05 Yeah. Right.


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