What are the characteristics of a good predictor variable

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Reference no: EM132200117

Question: 1. What are the characteristics of a good predictor variable?

2. What are the assumptions associated with the multiple regression model?

3. What does the partial, or net, regression coefficient measure in multiple regression?

4. What does the standard error of the estimate measure in multiple regression?

5. Your estimated multiple regression equation is. Predict the value of Y if X1 = 20 and X2 = 7.

YN = 7.52 + 3X1 - 12.2X2

6. Explain each of the following concepts:

a. Correlation matrix

b. Multicollinearity

c. Residual

d. Dummy variable

e. Stepwise regression

TABLE P-7

Variable Number

Variable Number 1 2 3 4 5 6

1 1.00 .55 .20 -.51 .79 .70

2 1.00 .27 .09 .39 .45

3 1.00 .04 .17 .21

4 1.00 -.44 -.14

5 1.00 .69

6 1.00

Most computer solutions for multiple regression begin with a correlation matrix. Examining this matrix is often the first step when analyzing a regression problem that involves more than one independent variable. Answer the following questions concerning the correlation matrix given in Table P-7.

a. Why are all the entries on the main diagonal equal to 1.00?

b. Why is the bottom half of the matrix below the main diagonal blank?

c. If variable 1 is the dependent variable, which independent variables have the highest degree of linear association with variable 1?

d. What kind of association exists between variables 1 and 4?

e. Does this correlation matrix show any evidence of multicollinearity?

f. In your opinion, which variable or variables will be included in the best forecasting model? Explain.

g. If the data given in this correlation matrix are run on a stepwise program, which independent variable (2, 3, 4, 5, or 6) will be the first to enter the regression function?

Reference no: EM132200117

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