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Introduction
Determination of different cost drivers for overhead costs in a manufacturing firm is very important, as it helps in decision-making. For example, when the factors that are contributing most to the overhead costs are known, it becomes easy to control such costs. This report will involve analysis of different variables that are believed to affect overhead costs, including number of setups, machines hours and labour hours. Some of the methods of analysis that will be used include descriptive statistics, regression analysis, and use of histograms and graphs.
Section A – characteristics of all the variables
Calculating measures of location (averages) and dispersion (variation)

N
Mean
Std. Deviation
Variance

24
172.79
17.939
321.824

Direct Labour hours
24
1134.67
64.629
4176.928

Machine hours
24
1402.58
213.135
45426.514

No. of setups
24
201.42
13.990
195.732

Valid N (listwise)
24

Table 2: descriptive statistics (averages and variation)
Using all 24 months’ data to construct a frequency distribution and histogram for the machine hours

Machine Hours
Frequency

1000
0

1100
5

1100
0

1200
1

1200
0

1300
2

1300
0

1400
4

1500
3

1600
6

1700
2

1800
1

Table 2: Frequency Distribution Table

Figure 1: Histogram for the machine hours
Estimating the median and other descriptive statistics for the machine hours

Column1

Mean
1402.583

Standard Error
43.50599

Median
1428

Mode
1300

Standard Deviation
213.135

Sample Variance
45426.51

Kurtosis
-0.97697

Skewness
-0.22011

Range
710

Minimum
1060

Maximum
1770

Sum
33662

Count
24

Table 3: Median and Other Descriptive Statistics for machine hrs
A brief Report
Determination of descriptive statistics is very essential in decision-making regarding cost. Among the four variables, the highest level of dispersion was reported in machine hours, as shown by the standard deviation and variance, implying that use of machines is likely to significantly affect variations in overhead cost. Number of set ups have the least dispersion, possibly meaning that this variable may not significantly cause cost variation (Table 2).  Table 2 shows the grouped frequency histogram for the machine hours, where it is evident that most of the hours lie between 1001 to 1100 and 1501 to1600. The shape of the histogram exhibits a bimodal distribution or rather it has two peaks. This is a problem that results from stratification and it means that the data could have originated from two different sources. It is, therefore, important to identify these sources and analyse the data separately if appropriate. Figure 3 shows the descriptive statistics for the machine hours. The minimum and the maximum values are very useful in quickly identifying the range between which hours fall, which in this case is 1060(min) and 1770(max). The standard error gives the extent to which the distribution is exposed to sample error, which in this case amounts to 43.5 (Mann, 1995).
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