(1 point) In a multiple regression analysis involving 15 data points, the mean squares for error is 7, and the sum of squares for error is 70. The number of the predictor variables must be

(1 point) Suppose all we know about a car in the sample is that it weighs 4500 pounds.  What would we predict its top speed to be?

(2 points) Short question: Name two graphical tools for checking regression model fit. 

(2 points) List two remedial measures for multicollinearity issues.

15. (3 points) Write the null and alternative hypotheses for testing the significance of the regressor “Wages”. Explain how to calculate the t statistics and p-value. What’s your final conclusion at significance level 0.05? ————————————————————————————————–   Please show all your work and submit in PDF files.  

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Questions 12-15. A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies. She proceeds to randomly select 23 large corporations and record information in millions of dollars. The software output below shows results of this multiple regression.   SUMMARY OUTPUT Regression Statistics Multiple R 0.817 R Square 0.667 Adjusted R Square 0.641 Standard Error 18768.39 Observations 23   ANOVA df SS MS F Signif F Regression 2 15579889265   7789944633 22.11467 0.0001 Residual 20 7045045703 352252285 Total 22 22624934968   Coeff StdError t Stat P-value Intercept 15742.78     6138.7823 2.564 0.0185 Capital 0.1115 0.1923 0.580 0.5684 Wages 7.0753 1.4797 4.782 0.0001    

Which option below best describes the relationship between WEIGHT and TOPSPEED?

Questions 8-11. We will now consider factors predicting the top speed of the n=77 cars.  The relevant variables are: TOPSPEED: Top speed of the car (in miles per hour) WEIGHT: Vehicle weight (in pounds) HORSEPOWER: Maximum horsepower of the car’s engine   Summaries of two regressions predicting TOPSPEED are below:     Model 1: TOPSPEED regressed on WEIGHT Predicted TOPSPEED = 85.34 + 0.00788 WEIGHT R2 = .301   R2 (Adjusted) = .292    Residual Standard Deviation  = 8.69   Model 2: TOPSPEED regressed on WEIGHT and HORSEPOWER Predicted TOPSPEED = 96.17 – 0.00692 WEIGHT + 0.350 HORSEPOWER R2 = .951    R2 (Adjusted) = .944    Residual Standard Deviation  = 1.29

(1 point) The Variance Inflationary Factor (VIF) measures the: