Question

c) Using your output from b), compute the difference in estimated salary between the finance and...

c) Using your output from b), compute the difference in estimated salary between the finance and consumer products (consprod) industries (holding sales and roe fixed).

please answer C using the ols regression below

Regression Statistics
Multiple R 0.267775
R Square 0.0717035
Adjusted R Square 0.048839
Standard Error 1338.4138
Observations 209
Coefficients Standard Error t Stat P-value
Intercept 943.96997 289.3127612 3.262801 0.001294
sales 0.0129842 0.008936132 1.453 0.147768
roe 4.9971073 12.48281546 0.400319 0.689343
finance 253.46296 260.4732312 0.973086 0.331668
consprod 560.323 246.8522226 2.269872 0.024266
utility -320.84965 290.8800298 -1.10303 0.27132

Homework Answers

Answer #1

The independent variable finance is equal to 1 for finance while the independent variable consprod is equal to 1 for consumer products .

Holding the ROE and sales constant, the difference in estimated salary between the finance and consumer products here is computed as:

= Coefficient of finance - Coefficient of consumer products

= 253.46296 - 560.323

= -306.86004

Therefore $306.86 is the required difference here, consumer products salary is expected to be $306.86 more than finance.

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
] A partial computer output from a regression analysis using Excel’s Regression tool follows. Regression Statistics...
] A partial computer output from a regression analysis using Excel’s Regression tool follows. Regression Statistics Multiple R (1) R Square 0.923 Adjusted R Square (2) Standard Error 3.35 Observations ANOVA df SS MS F Significance F Regression (3) 1612 (7) (9) Residual 12 (5) (8) Total (4) (6) Coefficients Standard Error t Stat P-value Intercept 8.103 2.667 x1 7.602 2.105 (10) x2 3.111 0.613 (11)
Consider the following computer output of a multiple regression analysis relating annual salary to years of...
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7345 R Square 0.5395 Adjusted R Square 0.5195 Standard Error 2134.9715 Observations 49 ANOVA df SS MS F Significance F Regression 2 245,644,973.9500 122,822,486.9750 26.9460 1.8E-08 Residual 46        209,672,760.0092 4,558,103.4785 Total 48 455,317,733.9592 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 14271.51879 2,525.5672 5.6508 0.000000963 9187.8157 19,355.2219 Education (Years) 2351.3035...
Consider the following computer output of a multiple regression analysis relating annual salary to years of...
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7338 R Square 0.5384 Adjusted R Square 0.5183 Standard Error 2139.0907 Observations 49 ANOVA df SS MS F Significance F Regression 2 245,472,093.5833 122,736,046.7917 26.8234 1.9E-08 Residual 46 210,482,624.6208 4,575,709.2309 Total 48 455,954,718.2041 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 14275.75637 2,530.4400 5.6416 0.000000994 9182.2448 19,369.2679 Education (Years) 2350.2675 338.3625...
Using the attached regression output, answer the following: SUMMARY OUTPUT Regression Statistics Multiple R 0.972971 R...
Using the attached regression output, answer the following: SUMMARY OUTPUT Regression Statistics Multiple R 0.972971 R Square 0.946673 Adjusted R Square 0.944355 Standard Error 76.07265 Observations 49 ANOVA df SS MS F Significance F Regression 2 4725757 2362878 408.3046 5.24E-30 Residual 46 266204.2 5787.049 Total 48 4991961 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -0.46627 14.97924 -0.03113 0.975302 -30.6179 29.68537 X1 0.09548 0.084947 1.123997 0.266846 -0.07551 0.26647 X2 0.896042 0.205319 4.364141 7.16E-05 0.482756 1.309328 a. What...
SUMMARY OUTPUT Regression Statistics Multiple R 0.84508179 R Square 0.714163232 Adjusted R Square 0.704942691 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.84508179 R Square 0.714163232 Adjusted R Square 0.704942691 Standard Error 9.187149383 Observations 33 ANOVA df SS MS F Significance F Regression 1 6537.363661 6537.363661 77.4535073 6.17395E-10 Residual 31 2616.515127 84.40371378 Total 32 9153.878788 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 61.07492285 3.406335763 17.92980114 6.41286E-18 54.12765526 68.02219044 54.12765526 68.02219044 Time (Y) -0.038369095 0.004359744 -8.800767426 6.17395E-10 -0.047260852 -0.029477338 -0.047260852 -0.029477338 Using your highlighted cells, what is the equation...
In models B through D, what seems to be the relationship between the burglary rate and...
In models B through D, what seems to be the relationship between the burglary rate and the percent of the 18-64 population who are young adults (18-24)? Select one: a. It is difficult to describe the relationship; the young adult variables were all significant at 5% in models B, C, and D, but the signs and sizes of the coefficients were very different between models. b. Conclusions about the relationship between young adults and the burglary rate are difficult to...
Given the data and output in the attached file, Construct a 90% confidence interval estimate for...
Given the data and output in the attached file, Construct a 90% confidence interval estimate for the average Sales when Advertising is 25 want a confidence interval. It uses Regressionquizoutput2. Regressionquizoutput1 Regression Statistics Multiple R R Square Adjusted R Square Standard Error N Observations 6 ANOVA df SS MS Regression 1 0.607903685 0.607903685 Residual 4 0.020846315 0.005211579 Total 5 0.62875 Coefficients Standard Error Intercept 1.799069984 0.153403661 GMAT 0.002912113 0.000269635 A graduate school wants to try to predict GPA based on...
Discuss the strength and the significance of your regression model by using R-square and significance F...
Discuss the strength and the significance of your regression model by using R-square and significance F where α = 0.05. SUMMARY OUTPUT Regression Statistics Multiple R 0.919011822 R Square 0.844582728 Adjusted R Square 0.834446819 Standard Error 163.953479 Observations 50 ANOVA df SS MS F Significance F Regression 3 6719578.309 2239859.44 83.3257999 1.28754E-18 Residual 46 1236514.191 26880.7433 Total 49 7956092.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 21.7335244 114.2095971 0.19029508 0.84991523 -208.158471 251.62552...
According to the Data, is the regression a better fit than the one with the Dummy...
According to the Data, is the regression a better fit than the one with the Dummy variable, explain? Regression Statistics Multiple R 0.550554268 R Square 0.303110002 Adjusted R Square 0.288887757 Standard Error 2.409611727 Observations 51 ANOVA df SS MS F Significance F Regression 1 123.7445988 123.7445988 21.31238807 2.8414E-05 Residual 49 284.5052051 5.806228676 Total 50 408.2498039 Coefficients Standard Error t Stat P-value Lower 95% Intercept 5.649982553 1.521266701 3.713998702 0.000522686 2.592882662 U-rate 1.826625993 0.395670412 4.616534206 2.84144E-05 1.0314965 Multiple R 0.572568188 R Square...
Y^ = b0 + b1X1 +b2X2/1 Interpret the value of R2 obtained using the equation above....
Y^ = b0 + b1X1 +b2X2/1 Interpret the value of R2 obtained using the equation above. SUMMARY OUTPUT Regression Statistics Multiple R 0.970383 R Square 0.941644 Adjusted R Square 0.928676 Standard Error 134.4072 Observations 12 ANOVA df SS MS F Significance F Regression 2 2623543 1311772 72.61276 2.8E-06 Residual 9 162587.7 18065.3 Total 11 2786131 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 707.4747 230.4927 3.069402 0.013367 186.0641 1228.885 X Variable 1 -7.39221 7.03366 -1.05098 0.320669 -23.3035...
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT