Question : 31. Regression analysis a technique used to: A. estimate the step and mixed : 1291699

 

31. Regression analysis is a technique used to: A. estimate the step and mixed components of total cost.B. estimate the fixed and variable components of a mixed cost.C. estimate the fixed and variable components of a step cost.D. estimate the fixed and mixed components of step cost.

 

32. Which of the following statements is true regarding regression analysis? A. It is usually the most accurate technique used to determine equivalent units.B. It is usually the most accurate technique used to determine net income.C. It is usually the most accurate technique used to determine the total units of production.D. It is usually the most accurate technique used to determine mixed cost behavior.

 

33. Which of the following statements is false regarding regression analysis? A. It is used to predict the fixed and variable components of a mixed cost.B. It is used to predict whether a cost is a product or a period cost.C. It is usually more accurate than the high/low method.D. It uses statistical methods to fit a cost line through a number of data points.

 

34. Which of the following statements is true regarding regression analysis? A. It is often less accurate than the high/low method.B. It is a better predictor of fixed costs than variable costs.C. It can not be used to predict the effect that a change in volume of production has on net income.D. It uses statistical methods to fit a cost line through a number of data points.

 

35. When using regression analysis to predict mixed cost behavior, which of the following would be the dependent variable? A. The highest level of activity.B. The lowest level of activity.C. The mixed cost at a given level of production.D. The variable cost per unit.

 

36. When using regression analysis to predict mixed cost behavior, which of the following would be the independent variable? A. The highest level of activity.B. The lowest level of activity.C. The mixed cost at a given level of production.D. The volume of production that drives a particular amount of mixed cost.

 

37. Regression Analysis 1You run a regression analysis and receive the following results: 

SUMMARY OUTPUT

 

 

 

 

 

 

 

 

 

 

Regression Statistics

 

 

 

 

Multiple R

0.88000000

 

 

 

 

R Square

0.78219168

 

 

 

 

Adjusted R Square

0.70958891

 

 

 

 

Standard Error

1165.19000

 

 

 

 

Observations

5

 

 

 

 

 

 

 

 

 

 

 

df

SS

MS

F

Significance F

Regression

1

14626984.4

1E+07

10.7736

0.0463451

Residual

3

4073015.604

1E+06

 

 

Total

4

18700000

 

 

 

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

 

Intercept

16146.37

8167.49

1.977

0.14249

 

X Variable 1

2.380

0.730

3.282

0.04635

 

 

 

 

 

 

 

Refer to the Regression Analysis 1 above. What would be the equation to predict mixed cost behavior? A. Y = $2.38 + $16,146.37xB. Y = $1,165.19 + $.88xC. Y = $16,146.37 + $2.38xD. Y = $8,167.49 + $.73x

 

38. Regression Analysis 1You run a regression analysis and receive the following results: 

SUMMARY OUTPUT

 

 

 

 

 

 

 

 

 

 

Regression Statistics

 

 

 

 

Multiple R

0.88000000

 

 

 

 

R Square

0.78219168

 

 

 

 

Adjusted R Square

0.70958891

 

 

 

 

Standard Error

1165.19000

 

 

 

 

Observations

5

 

 

 

 

 

 

 

 

 

 

 

df

SS

MS

F

Significance F

Regression

1

14626984.4

1E+07

10.7736

0.0463451

Residual

3

4073015.604

1E+06

 

 

Total

4

18700000

 

 

 

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

 

Intercept

16146.37

8167.49

1.977

0.14249

 

X Variable 1

2.380

0.730

3.282

0.04635

 

 

 

 

 

 

 

Refer to the Regression Analysis 1 above. To the nearest dollar, what would be the estimated total costs if 15,000 units were produced? A. $51,846B. $16,148C. $19,117D. $35,700

 

39. Regression Analysis 2You run a regression analysis and receive the following results: 

SUMMARY OUTPUT

 

 

 

 

 

 

 

 

 

 

Regression Statistics

 

 

 

 

Multiple R

0.969762217

 

 

 

 

R Square

0.940438758

 

 

 

 

Adjusted R Square

0.92058501

 

 

 

 

Standard Error

360.0073099

 

 

 

 

Observations

5

 

 

 

 

 

 

 

 

 

 

ANOVA

 

 

 

 

 

 

df

SS

MS

F

Significance F

Regression

1

6139184.211

6139184.211

47.36832487

0.006283174

Residual

3

388815.7895

129605.2632

 

 

Total

4

6528000

 

 

 

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

 

Intercept

3056.58

454.25

6.728812231

0.006701298

 

X Variable 1

1.27

0.18

6.882465029

0.006283174

 

 

 

 

 

 

 

Refer to the Regression Analysis 2 above. What would be the equation to predict total mixed costs? A. Y = $1.27 + $3,056.58xB. Y = $454.25 + $.18xC. Y = $360.007 + $1.27xD. Y = $3,056.58 + $1.27x

 

40. Regression Analysis 2You run a regression analysis and receive the following results: 

SUMMARY OUTPUT

 

 

 

 

 

 

 

 

 

 

Regression Statistics

 

 

 

 

Multiple R

0.969762217

 

 

 

 

R Square

0.940438758

 

 

 

 

Adjusted R Square

0.92058501

 

 

 

 

Standard Error

360.0073099

 

 

 

 

Observations

5

 

 

 

 

 

 

 

 

 

 

ANOVA

 

 

 

 

 

 

df

SS

MS

F

Significance F

Regression

1

6139184.211

6139184.211

47.36832487

0.006283174

Residual

3

388815.7895

129605.2632

 

 

Total

4

6528000

 

 

 

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

 

Intercept

3056.58

454.25

6.728812231

0.006701298

 

X Variable 1

1.27

0.18

6.882465029

0.006283174

 

 

 

 

 

 

 

Refer to the Regression Analysis 2 above. To the nearest dollar, what would be the estimated total costs if 500 units were produced? A. $   544B. $4,236C. $3,692D. $3,147