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