Regression Analysis:

Case II: Regression Analysis Consider the following output of a regression model to predict home prices ($ thousand) in a region based on area of the house (sq ft), age of the house (years), number of bedrooms and number of bathrooms. Use this table for questions #4-10. You will need to scroll up and down to refer to the table.: SUMMARY OUTPUT Regression Statistics Multiple R R Square Standard Error 3.487569725 Observations 20 ANOVA df SS MS F Significance F Regression 4 422.353 105.588 5.607 ).002504 Residual 15 282.447 18.830 Total 19 604.800 Coefficients Standard Error t Stat P-value Intercept 300.00 3.5773 4.3141 0.0006 Age (Years) -3.00 0.3726 -2.8058 0.0333 Area (Square foot) 0.05 0.0238 2.9589 0.0098 Bedrooms (number of) 12.00 2.0845 3.0827 0.0076 Bathrooms (number of) 4.00 1.0000 3.0000 0.0226 Question 4 (2 points) Is the regression significant at the 5% level? Choose the most correct statementIs the regression significant at the 5% level? Choose the most correct statement from the following: Yes, because significance F (also known as, F significance) is more than 5%. Yes, because significance F (also known as, F significance) is less than 5%. No, because significance F (also known as, F significance) is more than 28%. No, because significance F (also known as, F significance) is less than 5%. Question 5 (3 points) Is the Age significantly related to the price of the home at the 5% level? Choose the most correct statement from the following: No, because P-value for variable Age is more than 33%. Yes, because P-value for variable Age is less than 5%. Yes, because P-value for variable Age is more than 5%. O No, because P-value for variable Age is less than 5%