Use the problem/opportunity and research variable Manpower/ Quantitative methods

Question

Use the problem/opportunity and research variable

Manpower/ Quantitative methods for data collection

Do not actually collect any data; think hypothetically.

Develop a 1,050-word report in which you:

Identify the types of descriptive statistics that might be best for summarizing the data, if you were to collect a sample.

Analyze the types of inferential statistics that might be best for analyzing the data, if you were to collect a sample.

Analyze the role probability or trend analysis might play in helping address the business problem.

Format your assignment consistent with APA guidelines.

Explanation / Answer

Business Research Report Proposal

Business Research Topic:

The management team of the manufacturing industry wants to develop an equation for the future estimation of the overhead costs in the company’s production cell. For this business research proposal, we have to study the relationship exists between the different variables which are related to the overhead costs. Also, company wants to take a general idea about the average values for the different variables included in the study. We have to find the regression equation for the overhead costs and other given variables such as number of machine hours worked, the number of direct labour hours and the number of indirect labour workers. Let us see this business research proposal in detail given as below:

Research Questions:

Research questions are required to achieve the target in a proper direction. For this study, we have to find

Let us see the research methodology for the analysis of this business proposal.

Research Methodology:

For achieving the answers for the above research questions, we need to follow appropriate research methodology. We have to use the statistical methodology for the purpose of getting answers to above research questions. We have to find the descriptive statistics for the getting the overall idea about the variables included in the study. Also, we have to find the relationship exists between the given variables under study. We have to find the correlation coefficients for the pairs of variables. We have to find the equation for estimating the overhead costs. Let us see this data analysis for business research proposal in detail given as below:

Data collection:

For this business research proposal, we collected the data for 60 months of five years for the overhead costs in $ and other three variables such as number of machine hours worked, the number of direct labour hours and the number of indirect labour workers. The data is taken from the records of company.

Data analysis:

First of all, we have to see some descriptive statistics for getting the general idea about the variables included in the study. The average overhead cost is given as $5116.25 with the standard deviation of 1909.89. The average number of machine hours is noted as the 22697.19 hours while the average number of direct labour hours is noted as the 4718.57 hours. The average number of indirect labour workers is found as 8.5 or approximately 9.

The correlation coefficient between the two variables overhead cost and the number of machine hours worked is given as 0.979 which means, there is very high correlation or linear relationship or strong association exists between these two variables. The correlation coefficient between the overhead cost and number of direct labour hours found positive perfect, this means, and the correlation coefficient between these two variables found as approximately equal to 1. The correlation coefficient between the overhead cost and the number of indirect labour workers is found as 0.262. This means, there is less correlation or association exists between the two variables overhead cost and the number of indirect labour workers.

The regression model for the given variables is given as below:

The dependent variable for this regression model is given as the overhead costs and other variables are taken as the independent variables.

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

1.000a

.999

.999

60.64409

a. Predictors: (Constant), Number of indirect labour workers, Number of direct labour hours, Number of machine hours worked

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

2.150E8

3

71669536.569

19487.565

.000a

Residual

205951.542

56

3677.706

Total

2.152E8

59

a. Predictors: (Constant), Number of indirect labour workers, Number of direct labour hours, Number of machine hours worked

b. Dependent Variable: Overhead cost in $

The regression equation for this regression model is given as below:

Overhead cost y = 401.229 + 0.002*MH + 0.989*DLH – 0.010*ILW

Where, MH = number of machine hours,

DLH = number of direct labour hours

ILW = number of indirect labour workers.

Now, we want to check the claim whether the average overhead costs are same for the given five years or not. For checking this claim, we have to use the one way analysis of variance. The ANOVA test gives the p-value for this test as 0.119 which is greater than 0.05 so we cannot reject the null hypothesis that the average overhead costs are same for the given five years.

Research Outcomes:

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

1.000a

.999

.999

60.64409

a. Predictors: (Constant), Number of indirect labour workers, Number of direct labour hours, Number of machine hours worked

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