1. Quiz 2 Suppose you are interested in studying the effect of number of rooms on house prices. The definition of the variables of interest is presented in the table below: Intercept bdrms SUMMARY OUTPUT ANOVA Variable price bdrms The regression equation to be estimated: price = 80+ B1 bdrms + U You ran the regression on Excel and below is the regression output: Multiple R R Square Regression Residual Total Adjusted R Square Standard Error Observations Regression Statistics Definition/Unit House prices in thousands of dollars Number of bedrooms 0.508083517 0.25814886 0.249522684 88.98075393 df 88 SS 1 236943.0953 680911.413 917854.5083 86 87 3302 Standard Error Coefficients 72.23111181 41.55326716 62.02456421 t Stat 1.738278 11.3380345 5.470486 Write down the estimated regression equation. (2 marks) MS 236943.1 7917.575 F 29.92622212 P-value 0.085740975 4.34426E-07 Significance F 4.34426E-07 Upper Lower 95% 95% 154.8363 -10.3740493 39.4852976 84.56383 2. Interpret the estimated parameter of bdrms (B1). (2 marks) 3. Using the t-test, is the intercept parameter (BO) statistically significant? (2 marks) 4. Using the confidence interval approach, is the parameter of bdrms (81) statistically significant? (2 marks) 5. Test the following hypothesis: (2 marks) HO: B1= 2 H1: B1#2