Homework Answers
| The variable cost per unit in a regression analysis is represented by X variable 1 |
| X variable 1 = 0.718590832 or 0.72 |
| Option D $.72 is correct |
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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...
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 closest to the total cost if the firm uses 4,200 machine hours? i Data Table O A. $12 372,975 38 OB. $2,945 95 O...
Question 3: Jim's Lumber wanted to determine the relationship between its monthly operating costs and a...
Question 3: Jim's Lumber wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis performed using Microsoft Excel showed the following information: 0.92 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 148,38 ANOVA df F 52.06221 Significance F 0.0000 1 Regression Residual Total SSMS 573116.9 573117 110083.1 11008 683200 10 11 t Coefficients Standard Error 1263.34 806.85 0.26 0.05 Stat 1.57 Intercept X...
The Heartlake Corporation manufactures and sells toy gyroscopes. The following data is related to sales and...
The Heartlake Corporation manufactures and sells toy gyroscopes. The following data is related to sales and production of the toy gyroscopes for last year. Selling price per unit Variable manufacturing costs per unit Variable selling and administrative expenses per unit Fixed manufacturing overhead (in total) Fixed selling and administrative expenses in total) Units produced during the year Units sold during year $8.10 $1.86 $4.95 $77,000 $85,000 520,000 170,000 Using variable costing, what is the contribution margin for last year? O...
Rev Corporation wanted to determine the relationship between its monthly operating costs and a potential cost...
Rev 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): What is closest to the total cost if the firm uses 6,000 machine hours? Round intermediary calculato 2 decimal places $1.365.59 57.265.95 $6,538.90 $14.729,732.60
11. (35 pts) An agent for a real estate company wanted to predict the monthly rent...
11. (35 pts) An agent for a real estate company wanted to predict the monthly rent for one- bedroom apartments, based on the size of the apartment (see summary output below). Using the results, identify the coefficient of determination, r2, and interpret its meaning. SUMMARY OUTPUT Regression Statistics Multiple R 0.354314 R Square 0.125539 Adjusted R Square 0.106529 Standard Error 186.0407 Observations 48 ANOVA df Regression Residual Total 1 46 47 Significance SS MS F F 228565.2 228565.2 6.603807 0.013481439...
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...
Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have...
Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow: Month January February March April May June July August September October November December Machine Hours 2,500 2,900 1,900 3,100 3,800 3,300 4,100 3,500 2,000 3,700 4,700 4,200 Electricity Costs $ 18,400 $ 21,000 $ 13,500 $ 23,000 $ 28,250 $ 22,000 $ 24,750 $ 22,750 $ 15,500...
Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have...
Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow: Month January February March April May June July August September October November December Machine Hours 2,300 2,700 1,700 2,900 3,600 3, 100 3,900 3,300 1,800 3,500 4,500 4,000 Electricity Costs $19, 100 $22,400 $14,200 $24,400 $28,950 $23,400 $25,450 $23,450 $ 16,900 $27,400 $32,400 $28,450 Summary Output Regression...
7,10,11 Based on the following regression output, what is the equation of the regression line? Regression...
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....
Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.72 0.51 0.38 99.45...
Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.72 0.51 0.38 99.45 6 Anova df SS MS F Significance F 0.11 1 41497.60 41497.60 4.20 Regression Residual 4 39561.23 9890.31 Total 5 81058.83 t Stat P-value Coefficients Standard Error 1423.60 564.95 2.52 0.07 Intercept X Variable 1 Lower 95% Upper 95% -144.96 2992.16 -0.11 0.72 Lower 95.0% Upper 95.0% -144.96 2992.16 -0.11 0.72 0.31 0.15 2.05 0.11 Assume that Craig's Fresh and Hot Pancake Restaurant does...
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 closest to the total cost if the firm uses 4,200 machine hours? i Data Table O A. $12 372,975 38 OB. $2,945 95 O...
Question 3: Jim's Lumber wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis performed using Microsoft Excel showed the following information: 0.92 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 148,38 ANOVA df F 52.06221 Significance F 0.0000 1 Regression Residual Total SSMS 573116.9 573117 110083.1 11008 683200 10 11 t Coefficients Standard Error 1263.34 806.85 0.26 0.05 Stat 1.57 Intercept X...
The Heartlake Corporation manufactures and sells toy gyroscopes. The following data is related to sales and production of the toy gyroscopes for last year. Selling price per unit Variable manufacturing costs per unit Variable selling and administrative expenses per unit Fixed manufacturing overhead (in total) Fixed selling and administrative expenses in total) Units produced during the year Units sold during year $8.10 $1.86 $4.95 $77,000 $85,000 520,000 170,000 Using variable costing, what is the contribution margin for last year? O...
Rev 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): What is closest to the total cost if the firm uses 6,000 machine hours? Round intermediary calculato 2 decimal places $1.365.59 57.265.95 $6,538.90 $14.729,732.60
11. (35 pts) An agent for a real estate company wanted to predict the monthly rent for one- bedroom apartments, based on the size of the apartment (see summary output below). Using the results, identify the coefficient of determination, r2, and interpret its meaning. SUMMARY OUTPUT Regression Statistics Multiple R 0.354314 R Square 0.125539 Adjusted R Square 0.106529 Standard Error 186.0407 Observations 48 ANOVA df Regression Residual Total 1 46 47 Significance SS MS F F 228565.2 228565.2 6.603807 0.013481439...
Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow: Month January February March April May June July August September October November December Machine Hours 2,500 2,900 1,900 3,100 3,800 3,300 4,100 3,500 2,000 3,700 4,700 4,200 Electricity Costs $ 18,400 $ 21,000 $ 13,500 $ 23,000 $ 28,250 $ 22,000 $ 24,750 $ 22,750 $ 15,500...
Thane Company is interested in establishing the relationship between electricity costs and machine hours. Data have been collected and a regression analysis prepared using Excel. The monthly data and the regression output follow: Month January February March April May June July August September October November December Machine Hours 2,300 2,700 1,700 2,900 3,600 3, 100 3,900 3,300 1,800 3,500 4,500 4,000 Electricity Costs $19, 100 $22,400 $14,200 $24,400 $28,950 $23,400 $25,450 $23,450 $ 16,900 $27,400 $32,400 $28,450 Summary Output Regression...
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....
Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.72 0.51 0.38 99.45 6 Anova df SS MS F Significance F 0.11 1 41497.60 41497.60 4.20 Regression Residual 4 39561.23 9890.31 Total 5 81058.83 t Stat P-value Coefficients Standard Error 1423.60 564.95 2.52 0.07 Intercept X Variable 1 Lower 95% Upper 95% -144.96 2992.16 -0.11 0.72 Lower 95.0% Upper 95.0% -144.96 2992.16 -0.11 0.72 0.31 0.15 2.05 0.11 Assume that Craig's Fresh and Hot Pancake Restaurant does...
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