# Economics homework help

(All assignments need to be turned at midnight of the specified date)

Data Prep: Make sure you have 2-3 related X (macroeconomic) variables. Remember that these variables will be quarterly as your Y (Revenue) variable. All variables should be from the same time period.  Recall that the first column is dates and second column is your Y (Revenue) variable.  Your third column will be X1 (the first macro variable), 4th will be X2 (the second macro variable) and so forth.  Label your X variables properly so I can understand what those variables are.

• Create a correlation matrix with p-values (you can find it under Stat>Basic Statistics>Correlation) of Y and all Xs.  Explain which relationships are statistically significant, positive or negative? You need to tell me which Xs are more related to Y and which Xs are not. Also check the correlation among the X variables. Write a paragraph about possible negative or positive relation between each X with Y and the X variables among themselves based on the direction of the relationships and the statistical significance based on the p-value. (Hint: Also think about independent Xs assumption violation, do you see any possible signs of multicollinearity?)

• Use scatter plots to confirm the positive or negative relationships you diagnosed in part 1. (Hint: Also, think about linearity assumption violation, do you see any nonlinear relations?)

• Run your first regression and have MINITAB report the VIF and DW test statistic (Stats>Regression>Regression. You can find VIF and DW under options.) Explain your output. Your explanation should include the following paragraphs.
1. Interpret regression equation variable by variable. Does the regression equation make sense to you?
2. Which variables are statistically significant? Which are not? You can use t-test or p-values.
• Which variables are highly correlated?
1. Do you have a “sign switch”?
2. Do you have outliers? Are you planning to do anything about them?
3. Do you have a good overall model? Use both the r-squared and F-test explanations.

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