1. n = 2+42+1= 45
3. P-value=F.DIST.RT(7.423,2,42) = 0.0017
Pvalue is less than 0.05 so, model is statistically significant
A real estate research firm has developed a regression model relating list price (Y in 1,000)...
The commercial division of a real estate firm is conducting a regression analysis of the relationship between 2, annual gross rents (in thousands of dollars), and y selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. The regression equation is Y=20.0 +7.22 X Predictor Constant Coef 20.000 7.220 SE Coef 3.2213 1.3625 T 6.21 SS Analysis of Variance SOURCE Regression Residual Error 41,587.3 Total 51,984.4...
The commercial division of a real estate firm is conducting a regression analysis of the relationship between x, annual gross rents (in thousands of dollars), and y, selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. The regression equation is Y=20.0 + 7.21 X Predictor Constant Coef 20.000 7.210 SE Coef 3.2213 1.3625 T 6.21 5.29 Analysis of Variance SOURCE Regression Residual Error Total SS...
A regression model relating number of salespersons at a branch office, to y, annual sales at the office (in thousands of dollars) provided the following computer output from a regression analysis of the data. Where th =26. ANOVA SS MS F Significance F u Significance Regression Residual Total 8756.4 p-value 510 s.com Coefficients Standard Error Stat Intercept 7 7.0 10.723 Number of 5.609 Salespersons Write the estimated regression equation (to whole number). V= b. Compute the statistic and test the...
A regression model relating 2, number of salespersons at a branch office, to y, annual sales at the office (in thousands of dollars) provided the following computer output from a regression analysis of the data. Where Miotal = 30. 0 ANOVA 0 MSF Significance F SS 6361.5 Regression Residual Total 8844.6 P-value 85.0 5.418 Coefficients Standard Error Stat Intercept 10.553 Number of 43.0 Salespersons a. Write the estimated regression equation (to whole number). y= + b. Compute the F statistic...
A regression model relating 3, number of salespersons at a branch office, to y, annual sales at the office in thousands of dollars) provided the following computer output from a regression analysis of the data. Where ntotal = 32. ANOVA df MSF Significance F SS 6863.5 Regression Residual Total 9342.6 Stat P-value Intercept Number of Salespersons Coefficients 88.0 54.0 Standard Errort 12.175 5.865 a. Write the estimated regression equation (to whole number). y= Xx b. Compute the F statistic and...
please help! Following is a simple linear regression model: y = a + A + & The following results were obtained from some statistical software. R2 = 0.523 Syx (regression standard error) = 3.028 n (total observations) = 41 Significance level = 0.05 = 5% Variable Interecpt Slope of X Parameter Estimate 0.519 -0.707 Std. Err. of Parameter Est 0.132 0.239 Note: For all the calculated numbers, keep three decimals. Write the fitted model (5 points) 2. Make a prediction...
A regression model relating x, number of salespersons at a branch office, to y, annual sales at the office (in thousands of dollars) provided the following computer output from a regression analysis of the data. Where n total=26. a. Write the estimated regression equation (to whole number). y=_____+_____x b. Compute the F statistic and test the significance of the relationship at a .05 level of significance. (to 2 decimals) F-value ____ p-value is _______, we _________ h0 c. Compute the...
The commercial division of a real estate firm is conducting a regression analysis of the relationship between x, annual gross rents (in thousands of dollars), and y, selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. The regression equation is Y =20.0 + 7.21 X Predictor Coef SE Coef T Constant 20.000 3.2213 6.21 X 7.210 1.3628 5.29 Analysis of Variance SOURCE DF SS Regression...
The commercial division of a real estate firm is conducting a regression analysis of the relationship between x, annual gross rents (in thousands of dollars), and y, selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. The regression equation is Y = 20.0 + 7.23 X Predictor Coef SE Coef T Constant 20.000 3.2213 6.21 X 7.230 1.3625 5.29 Analysis of Variance SOURCE DF SS...
3. The table below shows the regression output of a multiple regression model relating the beginning salaries of employees in a given company to the following independent variables: Sex : an indicator variable (1=man and 0-woman) ducation years of schooling at the time of hire Experience number of months of previous work experience Source Regression Residual Total Df 4 8822,387,82 254,407 92 MS F-value 23.763,297 5,940,82423.35 46,151,118 Coefficient table Variable Constant Sex Education Experience Months t-value 10.94 6.02 3.22 2.16...