Answer question 31) - 35) based on the problem description below. Round every number to two decimal places. Below is the summary output from Excel for a linear regression model, with some missing numbers. The independent variables are X1, X2 and X3, and the dependent variable is Y. SUMMARY OUTPUT Multiple R A Square Adjusted A Square Missing Standard Error Observations 20 ANDVA F Significance F 8218 827368 Missing 20.84724 2.06328-07 10 Musing Missing 10 0407 102242 Coefficients Standard Orror Stat Avalue Lower 951 Upper 9519 Lower 95.0% Upper 95 04 intercept 1 1 12844 489 913858026 0170203472 0.895442 -12 290906: 0.587352750 0140732749 3.901983327 0.501268 0.24505330 0.8958520-8 0 20506336 0 895052040 0.07439092 0179038718 0.423080978 0.877868 -0.20859203 0.447403057 -0 20 893209 0.447401097 0 172373619 0 317051735 -0.59974015 0.P48495371 -0 39974813 0 944495971 31) (3 points) The estimated regression equation is: 82) (3 points) If XI increases by 1 (and other independent variables are unchanged), the expected change In Y is: 33) (3 points) The percentage of the variation In Y explained by the regression model (after34) (3 points) For a two-sided t-test conducted on the slope of X3, the p-value is: (3 points) Can the null hypothesis that the coefficient of X3 is zero be rejected, with significance level of 5%? (Answer "Yes" or "No") 35) (3points) According to your answer to 34), is X3 is linearly significant to Y at the given significance level? (Answer "Yes" or "No") (3 points) If the true relationship between Y and X3 contradicts with your answer above, this may be due to (answer by only one word): 36) (2 points) To capture the impact of seasonality (spring, summer, autumn, winter) in a linear regression model, the total number of dummy variables needed is