REGRESSION ASSIGNMENT DATA

Month Overhead Costs Machine Hours Labour Hours

January 32,961 301 3,305

February 29,082 240 1,901

March 34,820 224 2,439

April 27,867 218 2,290

May 38,222 305 2,198

June 27,018 205 1,688

July 33,980 209 1,184

August 29,871 240 2,201

July 30,334 278 2,497

August 39,101 308 2,790

September 32,098 240 3,001

October 31,220 223 2,989

November 41,099 490 4,410

December 56,089 660 5,420

TO ANSWER THE FOLLOWING QUESTIONS.

A-What are the equations using machine hours and labour hours as the cost driver? (4 marks)

B-Evaluate the two equations using machine hours and labour hours as cost drivers in terms of the following criteria and the

assumptions of the model. (12 marks)

RUN THE REGRESSION ANALYSIS BASED ON THE TWO INDEPENDENT VARIABLES-SHOW THE OUTPUT IN THE TWO EXCEL WORKSHEETS PROVIDED

AND ANALYZE THE REGRESSION OUTPUTS BASED ON THE FOLLOWING CRITERIA.

1-Economic Plausibility (2 m)

2-Good of Fit (2 m)

3-Significance of the independent Variable (2 m)

4-Standard Error of the Estimate (2 m)

5-Analyze the plot of the residuals based on the four assumptions of the model. Evaluate whether the plot of the residuals meets the requirement

of each assumption or does not meet the requirement of each asumption or cannot be ascertained for that assumption. (4 m)

C-Provide an overall conclusion to your analysis and explain why you came up with your conclusions based on the four criteria and assumptions above. (4 m)

D(i)-Which cost estimate model would you use? (1 m)

D(ii)-What problem may exist if you use a poor cost driver? (2 m)

D(iii)-Compute the total estimated overhead costs if the expected number of machine hours next month is 420 and the total number of labour hours expected is

3,800 hours based on the best independent variable determined above . (2 m)

PART A y= Machine Hours

y= Labor Hours

PART B Criterion B\enchmark Machine Hours Labour Hour

1 Economic Plausibility Does it make sense? A

B

2 Goodness of Fit= r^2 r^2>=30% 3 Significance of the Independent Variable p value is(b coefficient) less than 5%

4 Standard Error of the smaller SEE Estimate (SSE) is better

5 Assumptions of the Model

A LINEARITY

B CONSTANT VARIANCE

C INDEPEND. OF THE RESIDUALS

D NORMALITY

PART C-OVERALL

CONCLUSIONS

PART D

D(i)-Which cost estimate model would you use? (1 m)

D(ii)-What problem may exist if you use a poor cost driver? (2 m)

D(iii)-Compute the total estimated overhead costs if the expected number of machine hours next month is 450 and the total number of labour hours expected is 3,900 using the best independent variable determined above. (2 m)