Regression Statistics Multiple R 0.98 R Square 0.95 Adj R Square 0.94 Standard Error 4.53 Observations 15 ANOVA Df SS MS F Sig F Regression 3 4374.51 1458.17 71.13 0.03 Residual 11 225.49 20.50 Total 14 4600 Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept 79.11 19.78 4.00 0.00 35.57 122.65 Price ($) -4.93 1.61 -3.06 0.01 -8.47 -1.38 Income ($) 0.02 0.01 2.15 0.06 0.00 0.03 Sub ($) 0.17 0.64 0.27 0.79 -1.23 1.58 Using excel output answer the following questions 1. State the hypothesis of this demand model. 2. Is there any relationship between price, income and substitute of commodity with demand, if yes, how much? If no, why not? 3. How much demand is determined by the independent factors such as price, income and substitute of commodity? 4. Develop the demand forecasting model. 5. What is the significance level of that model? 6. Are the signs (+ or -) of the regression coefficients of the independent variables as one would expect? Explain briefly. 7. What would you predict for DEMAND if the price of widgets was $6, consumer income was $1,200, and the price of the substitute commodity was $17? 8. Which of the independent variable(s) is not significant as you expected? 9. State and interpret the standard error of estimate for this problem. 10. Write a brief note on the findings of the study based on above excel output