Reference no: EM13247281

Regression Analysis and Cost Estimation

The CEO of Carson Company has asked you to develop a cost equation to predict monthly overhead costs in the production department. You have collected actual overhead costs for the last 12 months, together with data for three possible cost drivers, number of Indirect Workers, number of Machine Hours worked in the department and the Number of Jobs worked on in each of the last 12 months:

Overhead Costs	Indirect Workers	Machine Hours	Number of Jobs
$2,200	30	350	1,000
4,000	35	500	1,200
3,300	50	250	900
4,400	52	450	1,000
4,200	55	380	1,500
5,400	58	490	1,100
5,600	90	510	1,900
4,300	70	380	1,400
5,300	83	350	1,600
7,500	74	490	1,650
8,000	100	560	1,850
10,000	105	770	1,250

(a)

The CEO suggests that he has heard that the high-low method of estimating costs works fairly well and should be inexpensive to use. Write a response to this suggestion for the CEO indicating the advantages and disadvantages of this method. Include the calculation of a cost equation for this data using Machine Hours as the cost driver.

(b)

Using Excel develop three scatter diagrams showing overhead costs against each of the three proposed independent variables. Comment on whether these scatter diagrams appear to indicate that linearity is a reasonable assumption for each.

(c)

Using the regression module of Excel's Add-in Data Analysis, perform 3 simple regressions by regressing overhead costs against each of the proposed independent variables. Show the output for each regression and evaluate each of the regression results, indicating which of the three is best and why.

Provide the cost equations for those regression results which are satisfactory and from them calculate the predicted overhead in a month where there were 100 Indirect Workers and 500 Machine Hours and 1,000 Jobs worked.

(d)

Selecting the two best regressions from part (c) conduct a multiple regression of overhead against these two independent variables. Evaluate the regression results.

Draw conclusions about the best of the four regression results to use.

QUESTION Forecasting

All forecasts are never 100% accurate but subject to error.

1. How is forecast error calculated?
2. Identify and describe three common measures of forecast error. Then illustrate how each is calculated by constructing a 4-period example.

(b) Consider the following table of monthly sales of car tyres by a local company:

Month	Unit Sales
January	400
February	500
March	540
April	560
May	600
June	?

(i)

Using a 2-month moving average develop forecasts sales for March to June inclusive.

(ii)

Using a 2-month weighted moving average, with weights of 2 for the most recent month and 1 for the previous month develop forecasts sales for March to June inclusive.

(iii)

The sales manager had predicted sales for January of 400 units. Using exponential smoothing with a weight of 0.3 develop forecasts sales for March to June inclusive.

(iv) Which of the three techniques gives the most accurate forecasts? How do you know?

(c) Describe the four patterns typically found in time series data. What is meant by the expression "decomposition" with regard to forecasting? Briefly describe the process.