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Question Following are the definitions of the variable names in the attached data set: Definitions variables: Period Months&P S&P 500 IndexCPI Consumer Pric...

Question

Question Following are the definitions of the variable names in the attached data set: Definitions variables: Period Months&P S&P 500 IndexCPI Consumer Price IndexM2 Money Supply M2 TD Trade Weighted Dollar Index UN Unemployment Rate CC Consumer Confidence IndexPlease use the attached data set and Excel to estimate the following regression models:

Question Following are the definitions of the variable names in the attached data set: Definitions variables: Period Month s&P S&P 500 Index CPI Consumer Price Index M2 Money Supply M2 TD Trade Weighted Dollar Index UN Unemployment Rate CC Consumer Confidence Index Please use the attached data set and Excel to estimate the following regression models:



Answers

Modeling Data The table shows the Consumer Price Index (CPI) for selected years. (Source: Bureau of Labor Statistics) $$ \begin{array}{|c|c|c|c|c|c|c|c|} \hline \text { Year } & 1970 & 1975 & 1980 & 1985 & 1990 & 1995 & 2000 \\ \hline \text { CPI } & 38.8 & 53.8 & 82.4 & 107.6 & 130.7 & 152.4 & 172.2 \\ \hline \end{array} $$ (a) Use the regression capabilities of a graphing utility to find a mathematical model of the form $y=a t^{2}+b t+c$ for the data. In the model, $y$ represents the CPI and $t$ represents the year, with $t=0$ corresponding to 1970 . (b) Use a graphing utility to plot the data and graph the model. Compare the data with the model. (c) Use the model to predict the CPI for the year 2010 .

So they give us this data here. They want us to come up with a quadratic regression for it. So I'm going to be using Google Sheets to do this. You could also use, like Microsoft excel if you have that or just your graphing calculator. But this is something that's pretty accessible. You don't have any of those. So the first thing that we need to keep in mind is they tell us that we want 1975 to be five when we plug this in. And so then notice that if this is 1975 then if 19 eighties, five years later that means he's going to be 10. Because we're just adding five years. And for the rest of them, we can see it's just increasing by five. So we would just keep on adding fives. Um, and we would end up with these for our values of T. Now, what we're going to do is come over here and right click the data. Uh, not right quick. I'm thinking of how to do this in excel. So we highlight this when we come over here to insert chart and is going to pop this chart here. So now this is a line chart, but they tell us to make a scatter plot. So let's put the scatter plot on here. And so now we come over here to customize Ah Siri's scroll down a little bit Goto trendline. And instead of linear, we want a polynomial because we want a quadratic. So that's the polynomial degree, too, so that there is actually what question be or part B of this question is on. You could see how it estimates that it pretty well. But now to actually get what this linear regression is, we can come down here to label and change that to use equation. And so now this up top here is going to be what are quadratic regression is, And then for the last thing they asked us to find or predict the c p i in the year 2010. So what we can do? So if this is 2010, that means T is going to be 40. Um, Well, actually, I should probably not do that because it's gonna mess with the chart. So, uh, if I write it right here, Okay, So 2010 40. So what we're going to do is just take 40 and plug it into this equation right here. So it's gonna be so if you're going to type this in Google Sheets, it's just gonna be equal 26.9 and then plus 5.73 times, um, 40. So you can just click that there and then add that to actually subtracted 0.273 times. And I'm gonna put parentheses. This value squared. And so now this is what we expect our c p i to be in 2000 and 10. Um, and depending on what your quadratic progression is here, if you had slightly different numbers, depending on how accurate the regression calculator you use is you might have something slightly different than this. But it should still be in the same ballpark for the answer

Okay. In this question, we have a table that provides the closing values of the Dow Jones industrial averages as of the end of December. For the years from 2000 to 2008. Now we have closing values. So this is just a table that is giving us some information. They have just arranged the data. So this is an example of descriptive statistics. Right? So this is descriptive study. This is a descriptive study.

Now This time, what are going to be my individuals? The model column is going to be my individuals. Right? The model column. The things in the model column are going to be my individuals. For example, three cities, five cities, three cities, five cities, etcetera. These are my models. All these rows are going to be my individuals. All the wait'll Z four Roadster. Now, what are the variables? The variables. Our body style body style is what it is. Qualitative in nature. Body style is qualitative in nature. This is qualitative in nature. How about Wait? Wait, is quantitative and continues there it is continuous in nature. This is quantitative and continues. Okay? And how about the number of seats? The number of seats will be discreet. The number off seeds is discreet, right? These are the variables. This is discreet. These are my variables. Okay. And what else do I want to find the data? Now? What are going to be the data? The data is going to be the values and these corresponding columns. In these corresponding columns, the values will become my data. And this would be my answer


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