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14OCxampic 4scuiILCaIdlpicvious provici CACCpL for the model shown below is based on data from 2020 instead of 2019.udldThis data includes the cost of cars and the ...

Question

14OCxampic 4scuiILCaIdlpicvious provici CACCpL for the model shown below is based on data from 2020 instead of 2019.udldThis data includes the cost of cars and the fuel tank capacity and a variable indicating if manual transmission is available or not: Below is a model output to predict the Midrange Price for Cars_ Response Variable: Midrange Price for cars built in 2020 (units are in thousands of dollars).Fuel Tank Capacity: Number of Gallons the Gas tank can hold.No Manual Transmission: This i

14O Cxampic 4s cuiILCa Idl picvious provici CACCpL for the model shown below is based on data from 2020 instead of 2019. udld This data includes the cost of cars and the fuel tank capacity and a variable indicating if manual transmission is available or not: Below is a model output to predict the Midrange Price for Cars_ Response Variable: Midrange Price for cars built in 2020 (units are in thousands of dollars). Fuel Tank Capacity: Number of Gallons the Gas tank can hold. No Manual Transmission: This is an indicator variable: This is coded as a 1 if no manual transmission and 0 otherwise. Term Estimate Intercept 16.64 Fuel Tank Capacity 2.33 No Manual Transmission 25.51 No Manual Transmission*Fuel Tank Capacity 1.46 Question: What is the predicted difference between price of two cars Assume both cars have NO Manual Transmission: One car holds 2.5 additional Gallon in the Fuel Tank Capacity: We predict a car from 2020 which can hold 2.5 additional gallons in the fuel tank to cost dollars more than the other car assuming both have no manual transmission: Record your answer to the nearest dollar: If the answer is 2.50658 thousand dollars record 2507



Answers

Fuel Tank Capacity. Consumer Reports provides information on new automobile models, including price, mileage ratings, engine size, body size, and indicators of features. A simple random sample of 35 new models yielded the following data on fuel tank capacity, in gallons. Use cutpoint grouping with 12 as the first cutpoint and classes of equal width 2. $$\begin{array}{lllllll}\hline 17.2 & 23.1 & 17.5 & 15.7 & 19.8 & 16.9 & 15.3 \\18.5 & 18.5 & 25.5 & 18.0 & 17.5 & 14.5 & 20.0 \\17.0 & 20.0 & 24.0 & 26.0 & 18.1 & 21.0 & 19.3 \\20.0 & 20.0 & 12.5 & 13.2 & 15.9 & 14.5 & 22.2 \\21.1 & 14.4 & 25.0 & 26.4 & 16.9 & 16.4 & 23.0 \\\hline\end{array}$$

So for this problem, what we're given is, and as our sample size, that's 3 11. We're going to let x one br displacement X two will be class. Mid size X three will be class Large x four will be fuel premium x five will be. Actually, it's not x five. Why is going to be fuel efficiency? And that's going to be calculated by a highway mpg. So now we have our regression equation, and we first have to determine the necessary sons. So you look at the sum of X I and that's 10 38. Then you look at the sum of X, I squared, and that's 3833.68 Then we look at the sum of why I and that's 80 36. And the sum of why I squared is 212,638 than the sum of X. I Y II gives us 2 25 432 7. So now we want to determine the slope of B um, and that's accomplished by B equals and times the sum of X y, minus the sum of X times the sum of why all over end times the sum of X squared minus the sum of acts square. This is going to end up giving us approximately negative 28825 So the mean is the sum of all the values divided by the number of values. So we're going to get that X mean is 3.3145 and the mean of why is 25 points 8392 The estimate of a of the intercept Alpha is going to be the average of wide decreased by the product of the estimate of the slope. So this is going to look like a is equal to the mean of Roy minus B times the medevacs, which is gonna give us 35.3933 Um, so we can replace Alpha or a with Alpha and be with beta. So now that's going to give us this value right here, which will now be 35 933 minus 2.8825 x one and then for part B, we will use. We can use excel to generate the multiple in your aggression model, and it will give us an output. Since this can't be done on Excel, Uh, we get a bunch of values, but if we put in the correct values will end up getting the correct output. So now we can move on to purchase E um, or actually, with part B, we can do part of us. So once you get the numbers in Excel, we see why hat is equal to be not. Plus B one x one plus b two, x two plus B three x three. Um, so we see that be not is going to equal 29 0074 b one is negative. 16581 B two is 4.4860 b three is 1.8190 and with that we end up getting as a result that are y is equal to 29. 0074 minus 1.6581 x one plus four point 44860 x two Move this for our last term, which is B three x three. That's gonna be plus one point 8190 x three. That's our final answer for beam and then foresee. We have our significance level Alpha. That's gonna be a 0.5 And the given claim is that b I or beta? I rather equals zero. So the null hypothesis states that even claim that the slope is zero. Um, So what we have now is that a church not equals. Uh, actually, h hot is given as b I equals zero and h A is given as b I. There's not equal zero. So the p values in this case we would end up getting abated. To corresponds to P equals zero, and beta three will correspond to p equaling zero point. Um, it's 1234 12345678 zeros 704 It's a very close to zero, but not quite. So if the P value is less than the significance than we will reject the null hypothesis hypothesis. So in this case, he is, um, less than zero and b two case, it's less than zero. So we reject the null hypothesis, which is h not. And in this case, um, it's less than zero is that we reject the significance is too Well, so you reject the null hypothesis again. Then, for part D, we do another excel generation of multiple linear regression model. Um, so we can't do anything with the sell side of things, but we have another one of these equations where it's be not, plus B one x one plus B two, x two plus B three, x three plus B four x four and then with all of our values what we end up getting as our estimated regression equation. It's going to be 29 7123 minus 1.6383 x one plus 3.9984 x two +16700 x three minus 1.585 x four In this case, the significance level. This is the last part. E. Alpha is going to equal 0.5 again. Um, and we and then all hypothesis we'll have data. One is equal to beta two, which equals beta three jiggles beta four, which is zero. And our H sub A is going to, um, ST that at least one of the FBI's is non zero. Besides, for some hi then the P values, uh, corresponding we want to look at, so we C p equals zero and P is less than zero. So we reject we reject h not because the P value is less than the significance. So I reject each nut are null hypothesis.

You're given a set of data where you know what the weight is in pounds of 10 vehicles and you know what they're MPG would be. And so you should put all the that data into your calculator into list one and list to. And then you can determine what the linear correlation is and or excuse me what the linear regression line is, and we get that the predicting the line is equal to 37.357 minus 0.46 77 x and and part B at access. Did he discuss? What does it mean for the slope? And what does it mean for the Y intercept? And the slope for us is negative. 0.0 Let's just say five and the why variable is in MPG MPG, and the X variable is in pounds. So we see that as the number of pounds that a car goes up by £1 we expect the number of MPG to drop by this much. So as poundage goes up, MPG seems to go down. It doesn't go down by a lot per pound. But as you move along and hundreds of pounds, and that's going to add up now, what about the Y intercept? The Y intercept is that 37 point, uh, roughly four, and that is in MPG. And so that would tell us that if the car had a weight of £0 so it weighed £0 that the number of MPG it would get would be 37.4, which again doesn't make any sense, because if it has no wait, it's not going to be able to move at all. So that's kind of extrapolating. That's what we call it when we use some value to describe to plug into this model. Um, that doesn't seem to fit with the domain updated that we had, but that's what that value is is that's the Y intercept. And that would be the meaning. The car weighed zero. That's how many MPG with yet. Then it looked at the C. T s and the C T s. It said, which is not part of our data set, has, uh, weight of £3649 and it has a MPG of 20 MPG. And it said, Is this consistent? Above or below average? And if you plug into your model, um, go back and plug, get your model in there and plug this 36,000 or 3649 in place of X. You'll find that, and I'll just write it like this if you put that many pounds and it came out to be 20.29 MPG, so it was very close to what the average was. But the CTS was just a little bit lower than what the average expected MPG was. Then it talks about on letter D about looking at some hybrid cars or some electric cars and said, Should we use these this equation to judge those vehicles and no, absolutely not. No, because these vehicles that this model was from was all they were all vehicles that use gasoline and they're not hybrid vehicles. So it would not make sense to use this model that comes from data from all gasoline driven vehicles and try to use a hybrid or an electric car and do a prediction. Yeah,

We need to use the following data to come up with an equation between the city, city MPG and the highway MPG. We're going to round a slope to the nearest 100. Then using the model, we're going to predict the highway miles per gallon. Who's fuel efficiency is 18 MPG and we're going to run to the nearest whole month number. So when we find our slope, we get our slope to equal to 1.45 And RB is zero. So are highway mileage Is equal to 1.45 times our city mileage. So it again, to answer question be were given, we're looking for our highway mileage, we're going to evaluate our function H times with 18, So we get 1.45 times 18, so 1.45 times 18 gives us and we're rounding to the nearest whole number 26 miles per gallon.

All right guys. So for this question, were given a data set that contains Uh, information on uh, the fuel economy of different cars. From 2012 were asked to create a cross tabulation report for all the cars with the size of zero highway mpg as the column, and then group them by highway mpg, An interval starting up five, Beginning at 15 and ending at 14. Well then comment on the relationship between size and highway mpg. We'll create another cross tabulation report this time with dr as the row and highway mpg at the column grouping again by the same intervals as we did in question one then comment on the relationship between the drive and the city npg. And finally, we'll look at the relationship of fuel type and city mpg by creating another cross tabulation report. So let's create our cross tabulation reports on a different tab here to do so we'll go to another tab and insert a pivot table, collect our data from our data set here. Yeah. All right. And then we need to fill in the rows and columns. So this first one we were asked for uh size and highway mpg so size as the rose highway mpg as columns. Mhm values will be the account of highway mpg s. And then we were asked to look at only car. So we need to filter this. Um, these rose to just the values that our cars so large car. Midsize car. Small car. Yeah, there we have it. So looking at our relationship, what do we see? Let's movies around a little bit. So large car in the bottom, Small car up top. Yeah. So what we see here is that these small cars, a lot of them are In the fuel range of this 22 35 We see as we get a larger car, um We see also a smaller amount of cars but also that the fuel economy seems to go down. So mid sized cars, we see similar trends with most of the Um car. Fuel ranges being from 25 to about 35. But as we get a larger car we see the fuel economy go down so we can answer this one. I think it's Yeah. Mhm. That fuel economy trends downward as vehicle size increases. Next. We want to create a cross tabulation for drive and highway mpg. So we'll do the same thing here. So insert a pivot table again, Select our data set. Yeah. Okay. Yeah. And then this one we're going to put dr on the rose and city mpg on the columns and here. So we'll also have a filter by um uh huh. By size or only cars. So when you go back here, yeah, we want to filter this down to just the cars again. So, large cars, mid sized small cars and then group Our Fuel Economy Guide five. You took Up to 45 In groups of five. So what can we say about the relationship of this? Uh We're really, what we see is that Uh two wheel drives tend to get better gas mileage. We can see that here by these um columns out here where we have a lot of cars are getting really good gas mileage and your are no four wheel drive cars that are even getting that kind of gas mileage. So For this question # four, we can put two wheel drive cars tend to get better mpg. Last question, we're looking at fuel type in city mpg for our cross tabulation reports. So we'll do the same thing and we can actually copy this pivot table and just cut it so that it's easy to update. So instead of drive right, we're not looking at dr we want to look at a fuel type, I believe. Yes, your tape. So here, what do we see? We see most most are gasoline. Um, we see most of the gasoline cars are getting anywhere between 15- 30 for most of them. We see electric cars are getting really good gas mileage and your diesel cars are getting uh, slightly better than your gasoline cars. So You can put that for question six years. So which yeah, electric cars get the best mpg followed by you saw and then gasoline. So that's it. You just created a couple cross tabulation reports to look at the data set by size of car and gas mileage, the drive and the type of fuel and we concluded, um, fuel economy turns downward as vehicle size increases. That too, will drive. Cars tend to get better gas mileage than four wheel drive, um, and finally, based on field type electric cars get the best image E, followed by diesel and then data.


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