B. The table below lists the sales, y (in millions of dollars) and the number of employees, x (in thousands) for a random sample of 20 Fortune 500 companies. The regression results based on the model are given below. Some of the numbers in the regression tables have been taken out.
|
SUMMARY OUTPUT |
||||||
|
Regression Statistics |
||||||
|
Multiple R |
(1) |
|||||
|
R Square |
(2) |
|||||
|
Adjusted R Square |
0.837364 |
|||||
|
Standard Error |
(3) |
|||||
|
Observations |
(4) |
|||||
|
ANOVA |
||||||
|
df |
SS |
MS |
F |
Significance F |
||
|
Regression |
(5) |
(7) |
(9) |
(10) |
9.7801E-09 |
|
|
Residual |
(6) |
(8) |
7825568.1 |
|||
|
Total |
19 |
914226344.8 |
||||
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
|
|
Intercept |
816.7943 |
767.9534976 |
1.06359862 |
0.3015718 |
-796.617401 |
2430.206 |
|
Employees, x |
(11) |
15.04320104 |
9.94110437 |
9.78E-09 |
117.9414144 |
(12) |
B7. Answer for (7):
a. 7825568.104
b. 140860225.9
c. 773366118.9
d. 914226344.8
B8. Answer for (8):
a. 7825568.104
b. 140860225.9
c. 773366118.9
d. 914226344.8
B9. Answer for (9):
a. 7825568.104
b. 140860225.9
c. 773366118.9
d. 914226344.8
B10. Answer for (10):
a. 7825568.10
b. 15.0432
c. 98.8256
d. 816.794
B11. Answer for (11):
a. 816.7943
b. 149.5460
c. 117.9414
d. 98.8256
B12. Answer for (12):
a. 181.1506
b. 117.9414
c. 9.9411
d. 149.5460
| ANOVA | ||||||
| df | SS | MS | F | Significance F | ||
| Regression | 1 | 773366119.00 | 773366119.00 | 98.8255561 | 9.78E-09 | |
| Residual | 18 | 140860225.8 | 7825568.1 | |||
| Total | 19 | 914226344.80 | ||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | 816.7943 | 767.9534976 | 1.06359862 | 0.3015718 | -796.617 | 2430.206 |
| Employees, x | 149.546032 | 15.04320104 | 9.94110437 | 9.78E-09 | 117.9414 | 181.15062 |
B7. Answer for (7):
a. 7825568.104
b. 140860225.9
c. 773366118.9
d. 914226344.8
B8. Answer for (8):
a. 7825568.104
b. 140860225.9
c. 773366118.9
d. 914226344.8
B9. Answer for (9):
a. 7825568.104
b. 140860225.9
c. 773366118.9
d. 914226344.8
B10. Answer for (10):
a. 7825568.10
b. 15.0432
c. 98.8256
d. 816.794
B11. Answer for (11):
a. 816.7943
b. 149.5460
c. 117.9414
d. 98.8256
B12. Answer for (12):
a. 181.1506
b. 117.9414
c. 9.9411
d. 149.5460
B. The table below lists the sales, y (in millions of dollars) and the number of...
B. The table below lists the sales, y (in millions of dollars) and the number of employees, x (in thousands) for a random sample of 20 Fortune 500 companies. The regression results based on the model are given below. Some of the numbers in the regression tables have been taken out. SUMMARY OUTPUT Regression Statistics Multiple R (1) R Square (2) Adjusted R Square 0.837364 Standard Error (3) Observations (4) ANOVA df SS MS F Significance F Regression (5) (7)...
2. A financial analyst measures the monthly returns of two stocks (A and B) over a twenty year period. Over that time period, stock A had an average return of 11% (per year) with a standard deviation of 20%. Over that same period, stock B had an average return of 13% with a standard deviation of 17%. The analyst estimates the CAPM for both stocks. The results are below: Stock A: SUMMARY OUTPUT Regression Statistics Multiple R 0.734020234 R Square...
You were asked by your manager to evaluate the regression tables below to decide which cost driver would be best to use for the production department. Since your manager is new and does not understand the regression analysis tables, you will need to explain why one set of statistics is better than the other and why you have chosen the better driver. Manufacturing Direct Labor Hours Regression Statistics Multiple R 0.799304258 R Square 0.638887297 Adjusted R Square 0.602776026 Standard Error...
Dep.= % WRK Indep.= % MGT SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Significance df SS MS F F Regression 102.1488 148.9539 Residual Total 12.0000 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Intercept % MGT 0.4543 SE CI CI PI PI Predicted Predicted Lower Upper Lower Upper x0 Value Value 95% 95% 95% 95% 67.0000 67.8474 65.8779 69.8169 72.0000 70.1189 68.2003 72.0375 76.0000 71.9361 69.7884 74.0838 Dep.= % MGT...
7,10,11
Based on the following regression output, what is the equation of the regression line? Regression Statistics Multiple R 0.917214 R Square 0.841282 Adjusted R Square 0.821442 Standard Error 9.385572 Observations 10 ANOVA df SS MS Significance F 1 Regression 3735.3060 3735.30600 42.40379 0.000186 8 Residual 704.7117 88.08896 9 Total 4440.0170 Coefficients Standard Error t Stat P-value Lower 95% Intercept 31.623780 10.442970 3.028236 0.016353 7.542233 X Variable 1.131661 0.173786 6.511819 0.000186 0.730910 o a. 9; = 7.542233+0.7309 Xli o b....
Develop an estimated simple linear regression model that can be used to predict the alumni giving rate, given the graduation rate. Below is the data sets and the regression, I just need to know what it means so that I am able to write about it. SUMMARY OUTPUT Regression Statistics Multiple R 0.749592336 R Square 0.561888671 Adjusted R Square 0.552152864 Standard Error 5.752079289 Observations 47 ANOVA df SS MS F Significance F Regression 1 1909.537 1909.537 57.71362 1.34E-09 re Residual...
Calculate the 95% prediction interval of y when x=5 using the 2000 pairs Mean of x = 4.51 Regression Statistics Multiple R 0.012848 R Square 0.000165 Adjusted R Square -0.00034 Standard Error 2.869737 Observations 2000 ANOVA df SS MS F Significance F Regression 1 2.716416 2.716416 0.329847 0.565814 Residual 1998 16454.31 8.235388 Total 1999 16457.02 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 4.509054 0.119572 37.70997 1.7E-235 4.274555 4.743552574 4.274555 4.743553 X 0.012884...
A sample with two variables (x and y) is given in the table. According to the sample, please develop an excel spreadsheet to calculate the missing values given in the table of “summary output” attached below. The excel spreadsheet needs to be submitted together with your assignment. Note: You can use “data analysis toolpak” to check your answers, but calculations should be based on the formulas that we learnt in module 2. x y 1.0 5.2 1.5 7.2 2.0 5.5...
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...
Based on the below data what will be the value of multiple R? Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 8 ANOVA df SS MS F Regression 1 29 29 7 Residual 6 26 4 Total 7 Coefficients Standard Error t Stat P-value Intercept 1 31.274666 3.984284 0.007248 Advertising (thousands of S) 42 6.19330674 1.610802 0.158349 Submit Answer format: Number Round to: 2 decimal places.