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.
Read DetailsHonor Code (Sign this pledge by typing your name in the text field): I affirm that I will not plagiarize, use unauthorized materials, or give or receive illegitimate help on this examination. I will also uphold equity and honesty in the evaluation of my work and the work of others.
Read DetailsQuestions 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
Read DetailsQuestions 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
Read Details