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We are running a linear regression testto show the relationship between gdp growth rate inflation and crude oil pricesalso impact of covid -19 is a part of our rese...

Question

We are running a linear regression testto show the relationship between gdp growth rate inflation and crude oil pricesalso impact of covid -19 is a part of our researchso this was the suggestions given to us"increase the sample size upto 30 plus dummy variable to capture pandemic crisis"what dummy variable we should take and what will be it's interpretation??ohkk

we are running a linear regression test to show the relationship between gdp growth rate inflation and crude oil prices also impact of covid -19 is a part of our research so this was the suggestions given to us "increase the sample size upto 30 plus dummy variable to capture pandemic crisis" what dummy variable we should take and what will be it's interpretation?? ohkk



Answers

Consider again the post-1950 trend in U.S. GDP we examined in Exercise 11. Here are a regression and (on the next page) a residual plot when we use the log of GDP in the model. Is this a better model
for GDP? Explain.
$$\begin{array}{l}{\text { Dependent variable is: logGDP}} \\ {\text { R-squared = } 98.9 \% \ s=0.13185} \\ {\text { Variable }} & {\text { Coefficient }} \\ {\text { Intercept }} & {5.6579766} \\ {\text { Year-1950 }} & {0.070734456}\end{array}$$

So for number 11 there two parts to that that we need to take into account, the first of which is Ussing. What does that roughly 88% residual mean So that 88% residual, um basically means that 88% and I'm rounding of variability or variation is of that GDP is being shown in a scatter plot. So then the question is, Is that enough? And so, um, that might not be enough by itself. You want to definitely check the residual, and that will finish part A is you'll check the residual. So then, in part B, we look at that residual, and it is definitely occur. So because the residual is her, the original model that we're seeing is not appropriate is not good. So in summary again, the residual is curve. And so it's not, um, inappropriate model

So we are being asked, here's our basic sample that we were given and we wanted to figure out these different transformations and we want to see which one has the highest R value. So that was my table there and then my access change to that and my our value was set 0.787 And then I did the log of X in my ex failures. Rivette and my our value was perfect. So I stopped right there because the highest are a value you can have is the number one. So that would be the log of X. Remember your ex initially was this. Then we had to square it and put that in and find the R. Value and then we did a log of each X. Put those in and that became the best R. Value.

Part one. The regression of the residuals on its first leg with 35 observations, produces a row let of minus point 089 and it's standard error is 890.178 This result tells us there is no evidence of a are one cereal correlation. In Part two, we regret consumption growth On its first leg, we get the residual U T hat, then we regress. UT heads square on the first leg of consumption growth and consumption growth. First leg squared using 35 observations. The F statistic with to 32 degrees of freedom yeah, is 1.8 and the P value is about 0.352 So there is little evidence of hetero scad elasticity in the er one model for consumption growth. This means we need not modify our test of the permanent income hypothesis by correcting somehow for hetero ski elasticity.

For exercise is 10 to 15. Create the scatter plot for the data. Answer the following eight. Does the data have a linear pattern? Be if yes, is the correlation positive or negative for number 11 were given the U. S. Gross domestic product for several years. Right? And first step is to transform our year variable into a necks variable just to simplify things you want X equals zero to correspond to 1990. So these X values can be obtained by subtracting 1990 from the years so x zero for 1990 x equals five for 1995 and so on are why variable is the G D. P in billions of dollars using year 2000 as the base period showed. To answer this question, we're going to use a graphing calculator app. The first step is to go into stack It did, and to type in the X values in l. One. The white values in l two. So we've done it here to save time. The next step is to go into stat blood. Make sure that the onus highlighted for Platt one, and we choose a color for the marks or symbols for the data points type. Make sure you select scatter blood, choose a mark or symbol for the data points and then X list or the X Variables or X values are in l one y values are in l two. Next step is to go into a window so our X values range from 0 to 14. So for X men we can use let's use negative one to make sure that we see all our data points and then X max. Let's use 15 ex scale equals one for why I mean well are y variable are white. Values ranged from 7113 to 10,000 842. So let's use for why I mean 7000 y Max. Let's use 12,000 and for our scale, let's just use 1000. The next step is to hit graph. And so here we see the scatter platform. Our data note that the data has a linear pattern and this linear pattern is such that as you go from left to right, that linear pattern is rising so we can conclude that for letter A, we can say that yes, the data seems to have a linear pattern and let her be. Since that linear pattern is increasing or rising as you go from left to right, we say that the correlation, it's positive.


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