Homework Answers
Cost formula: Total cost = $50 + 0.82% x sales
Total cost = fixed cost + variable cost
Fixed cost = Intercept = $50
Variable cost = revenue = 0.82% * sales
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Problem 2.43 The following simple regression results used revenue as a potential cost driver for research...
You were asked by your manager to evaluate the regression tables below to decide which cost driver would be best to use...
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...
An accountant conducts a simple regression result to identify the fixed cost and variable cost for...
An accountant conducts a simple regression result to identify the fixed cost and variable cost for a product. Based on the following regression results, identify the fixed cost and variable cost. SUMMARY OUTPUT Regression Statistics Multiple R 0980449 R Square 0.98128 Adjusted R Square 0.954827 Standard Error 11.2385 Observations 8 ANOVA dr Regression Residual Total SS MS F ignificance F 1 18814.05 18814.05 148.9585 1.84E 05 6 757.8237 126.3039 7 19571.88 Intercept X Variable 1 Coefficientsandard Em Star P-value ower...
Regression Analysis 2 You run a regression analysis and receive the following results SUMMARY OUTPUT Regression...
Regression Analysis 2 You run a regression analysis and receive the following results SUMMARY OUTPUT Regression Statistics Multiple R 0 .9697622171 R Square 0.940438758 Adjusted R Square 0.92058501 Standard Error 360.0073099 Observations 5 IIIIIIII ANOVAT di SS M S F Sanificance Regression 11 6 139184 2116139184 2111 47 368327870 000 Residual 3 3 88.815.78951129605,26321 Total 146528000T IUSTI Intercept X Variable 1 Coefficients 2056. 58 1.50 Standard Error 4 54.25 0.1816 Stat 6.728812231 .882465029 P-value 0006701290 0.006283174 Refer to the Regression...
Valle Crucis Corporation wanted to determine the relationship between its monthly operating costs and a potential...
Valle Crucis Corporation wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the following information (note: only a portion of the regression analysis results is presented here): (Click the icon to view the portion of the regression analysis.) What is the variable cost per unit answers are rounded)? O A. $2,946 O B. $.98 OC. $.05 OD. $.72 Data Table SUMMARY OUTPUT Regression Statistics Multiple...
Simple Linear Regression Problem Simple Linear Regression Problem QUESTION 4 SUMMARY OUTPUT Regression Statistics Multiple R...
Simple Linear Regression Problem
Simple Linear Regression
Problem
QUESTION 4 SUMMARY OUTPUT Regression Statistics Multiple R Squared Adjusted Rsq Standard Error Observations 0.90 0.80 0.79 82.06 19.00 ANOVA MS 467247.5 6733.3 df Regression Residual Total 467247.5 114466.2 581713.7 17 Intercept Age Coefficients St Error 756.26 10.27 30.41 1.23 t Stat 24.87 -8.33 This output was obtained from data on the age of houses (in years) and the associated amount paid in rates (S). Predict the rates paid (in dollars correct...
What is the SCL equation for your regression output? A D 1 SUMMARY OUTPUT 2 Regression Statistics L3 4 Multiple R 5...
What is the SCL equation for your regression output?
A D 1 SUMMARY OUTPUT 2 Regression Statistics L3 4 Multiple R 5 R Square 6 Adjusted R Square 7 Standard Error 8 Observations 0.147788325 0.021841389 0.004680712 0.112847351 59 10 11 12 Coefficients Standard Error P-value 0.015008141 0.043055201 0.965808003 t Stat 13 Intercept 14 S&P 500 HPR 0.000646179 0.490477908 0.434756954 1.128165755 0.263976201 15
A D 1 SUMMARY OUTPUT 2 Regression Statistics L3 4 Multiple R 5 R Square 6 Adjusted R...
Regression Statistics Multiple R 0.88012 R Square 0.77461 Adjusted R Square 0.77190 Standard Error 56.6927 Observations...
Regression Statistics Multiple R 0.88012 R Square 0.77461 Adjusted R Square 0.77190 Standard Error 56.6927 Observations 253 ANOVA Significance 285.2516 MS 916816.787 3214.0637 Regression Residual Total 0.000 2750450.3598 800301.8665 3550752.226 252 Intercept Income Coefficients Standard Error 70.2382 15.8338 5.45850 .2485 t Stat P-value 4.4360 0.000014 21.96960 .000 Lower 3 9.053 4.969 "pper 95% 1.4234 479 HULLU LIIS TILIR. SUMMARY OUTPUT Regression Statistics Multiple R 0.8778 R Square Adjusted R Square 0.6558 Standard Error Observations ANOVA ANOVA Significance Regression 45.3528 de...
Using the output below, what is the coefficient of determination SUMMARY OUTPUT Regression Statistics Multiple R...
Using the output below, what is the coefficient of determination SUMMARY OUTPUT Regression Statistics Multiple R 0.9236 0.8530 R Square Adjusted R Square Standard Error 0.8346 4.3790 Observations 10 Coefficients 29.73333 3.284848 Standard Error 2.991435587 Intercept Factory Options 0.482113498 0.8530 0.4821 29.73 O 0.9236 O 328 • Previous Next
Following a regression analysis output : SUMMARY OUTPUT Regression Statistics Multiple R 0.719422 R Square Adjusted...
Following a regression analysis output : SUMMARY OUTPUT Regression Statistics Multiple R 0.719422 R Square Adjusted R Square 0.477366 Standard Error Observations 14 ANOVA df SS MS F Regression 1 3.028885709 Residual 12 2.823257148 Total 13 5.852142857 Coefficients Standard Error t Stat P-value Intercept 1.157091 0.566482479 0.063699302 Satisfaction with Speed of Execution 0.636798 0.177478218 0.003726861 Group of answer choices R Square is 0.517 Standard error is 0.386 Residuals are 2.823 F-test is 11.87 R Square is 0.517 Standard error is...
Problem 6 Part A.5 Consider the following regression output for the Single Index Model where excess returns on Microsof...
Problem 6 Part A.5 Consider the following regression output for the Single Index Model where excess returns on Microsoft Corp (MSFT) index regressed on the excess returns for the S&P500 are RMSFT ()aMFTPrsSPTRS&PS00 ()+eFT (t) S&P500 Regression Statistics Multiple R 0.5764 R Square 0.3322 Adjusted R Square 0.3302 Standard Error 3.1156 Observations 338 Coefficients Standard Error t Stat P-value 0.7064 -0.0639 0.1695 -0.3770 Intercept 0.0000 R_S&P500 0.8139 0.0629 12.9294 Interpret the regression output
Problem 6 Part A.5 Consider the following...
An accountant conducts a simple regression result to identify the fixed cost and variable cost for a product. Based on the following regression results, identify the fixed cost and variable cost. SUMMARY OUTPUT Regression Statistics Multiple R 0980449 R Square 0.98128 Adjusted R Square 0.954827 Standard Error 11.2385 Observations 8 ANOVA dr Regression Residual Total SS MS F ignificance F 1 18814.05 18814.05 148.9585 1.84E 05 6 757.8237 126.3039 7 19571.88 Intercept X Variable 1 Coefficientsandard Em Star P-value ower...
Regression Analysis 2 You run a regression analysis and receive the following results SUMMARY OUTPUT Regression Statistics Multiple R 0 .9697622171 R Square 0.940438758 Adjusted R Square 0.92058501 Standard Error 360.0073099 Observations 5 IIIIIIII ANOVAT di SS M S F Sanificance Regression 11 6 139184 2116139184 2111 47 368327870 000 Residual 3 3 88.815.78951129605,26321 Total 146528000T IUSTI Intercept X Variable 1 Coefficients 2056. 58 1.50 Standard Error 4 54.25 0.1816 Stat 6.728812231 .882465029 P-value 0006701290 0.006283174 Refer to the Regression...
Valle Crucis Corporation wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the following information (note: only a portion of the regression analysis results is presented here): (Click the icon to view the portion of the regression analysis.) What is the variable cost per unit answers are rounded)? O A. $2,946 O B. $.98 OC. $.05 OD. $.72 Data Table SUMMARY OUTPUT Regression Statistics Multiple...
Simple Linear Regression Problem
Simple Linear Regression
Problem
QUESTION 4 SUMMARY OUTPUT Regression Statistics Multiple R Squared Adjusted Rsq Standard Error Observations 0.90 0.80 0.79 82.06 19.00 ANOVA MS 467247.5 6733.3 df Regression Residual Total 467247.5 114466.2 581713.7 17 Intercept Age Coefficients St Error 756.26 10.27 30.41 1.23 t Stat 24.87 -8.33 This output was obtained from data on the age of houses (in years) and the associated amount paid in rates (S). Predict the rates paid (in dollars correct...
What is the SCL equation for your regression output?
A D 1 SUMMARY OUTPUT 2 Regression Statistics L3 4 Multiple R 5 R Square 6 Adjusted R Square 7 Standard Error 8 Observations 0.147788325 0.021841389 0.004680712 0.112847351 59 10 11 12 Coefficients Standard Error P-value 0.015008141 0.043055201 0.965808003 t Stat 13 Intercept 14 S&P 500 HPR 0.000646179 0.490477908 0.434756954 1.128165755 0.263976201 15
A D 1 SUMMARY OUTPUT 2 Regression Statistics L3 4 Multiple R 5 R Square 6 Adjusted R...
Regression Statistics Multiple R 0.88012 R Square 0.77461 Adjusted R Square 0.77190 Standard Error 56.6927 Observations 253 ANOVA Significance 285.2516 MS 916816.787 3214.0637 Regression Residual Total 0.000 2750450.3598 800301.8665 3550752.226 252 Intercept Income Coefficients Standard Error 70.2382 15.8338 5.45850 .2485 t Stat P-value 4.4360 0.000014 21.96960 .000 Lower 3 9.053 4.969 "pper 95% 1.4234 479 HULLU LIIS TILIR. SUMMARY OUTPUT Regression Statistics Multiple R 0.8778 R Square Adjusted R Square 0.6558 Standard Error Observations ANOVA ANOVA Significance Regression 45.3528 de...
Using the output below, what is the coefficient of determination SUMMARY OUTPUT Regression Statistics Multiple R 0.9236 0.8530 R Square Adjusted R Square Standard Error 0.8346 4.3790 Observations 10 Coefficients 29.73333 3.284848 Standard Error 2.991435587 Intercept Factory Options 0.482113498 0.8530 0.4821 29.73 O 0.9236 O 328 • Previous Next
Problem 6 Part A.5 Consider the following regression output for the Single Index Model where excess returns on Microsoft Corp (MSFT) index regressed on the excess returns for the S&P500 are RMSFT ()aMFTPrsSPTRS&PS00 ()+eFT (t) S&P500 Regression Statistics Multiple R 0.5764 R Square 0.3322 Adjusted R Square 0.3302 Standard Error 3.1156 Observations 338 Coefficients Standard Error t Stat P-value 0.7064 -0.0639 0.1695 -0.3770 Intercept 0.0000 R_S&P500 0.8139 0.0629 12.9294 Interpret the regression output
Problem 6 Part A.5 Consider the following...
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