USE THE PAPER BELOW TO CREATE THE SLIDES........

Preparean 11- to 15-slide MicrosoftPowerPointpresentation for the senior management team based on the business problem or opportunity you described in Week 3 and 4. Draw on material you developed in the Week 3 and 4 assignments.

Includethe following in your presentation:

• Introduction slide
• Agenda slide
• Describe the organization, with a brief description
• Explain the business problem or opportunity
• Analyze why the business problem is important
• Identify what variable would be best to measure for this problem. Explain why.
• Apply data analysis techniques to this problem (tell which techniques should be used: descriptive stats, inferential stats, probability, linear regression, time series). Explain why.
• Apply a possible solution to the problem/opportunity, with rationale.
• Evaluate how data could be used to measure the implementation of such a solution.
• Conclusion
• References slide (if any source material is quoted or paraphrased throughout the presentation)

Includeon the slides what you'd want the audience to see (include appropriate visual aids/layout) and include in the Speaker's Notes section what you'd say as you present each slide. If any source material is quoted or paraphrased in the presentation, use APA citations and references.

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Business Research Report Proposal

Business Research Topic:

The manufacturing industrial company is selected study of the different variables regarding the production of the manufacturing company. The management team of the company wants to analyze the data for the study of the overhead costs in the companys department regarding the production division. The manager want to find out the relationship between the overhead costs and other related variables such as direct labor hours, indirect labor hours, number of machine hours etc. For this purpose, the manager wants to collect the data for the given variables and then analyze this data for getting conclusions. Manager wants to develop the regression equation for the future estimation purpose. By using the different statistical methods and techniques, the manager wants to analyze the data for the overhead costs and other related variables for checking some claims. Let us see this research proposal in detail given as below:

Research Questions:

It is very important to establish the research questions or problems for any business study to find out the conclusions for the different claims. For this business research proposal, the research questions for the purpose of the data analysis are summarized as below:

a) To find out the mean values for the overhead costs and other related variables like direct labor hours, indirect labor hours, machine work hours, etc.

b) To find out the relationship between the overhead costs and other related variable

c) To develop the regression equation for the estimation of the overhead costs for the production for future use

d) To test the claim regarding the equality of the mean overhead costs for the different timings.

Research Methodology:

The use of appropriate research methodology is very important for any data analysis of the research. If the research methodology is not appropriate or proper, the results or conclusions will not be appropriate or proper. For this research proposal, we have to study the data analysis for the variables such as overhead costs; the number of machine hours worked, the number of direct labor hours and the number of indirect labor workers. The first step in the research methodology is the data collection for the variables under study. The data is collected for the variables overhead costs, the number of machine hours worked the number of direct labor hours and the number of indirect labor workers. After the data collection process, the use of descriptive statistics is very essential due to finding answers to research questions. To checking the relationship between the different variables is needed for answering research question. For this purpose, we can use the scatter diagram to check whether the linear relationship exists between the given variables or not. Other than this diagrammatic figure, we can use the correlation coefficient to find the extent of the linear relationship exists between the given two variables. To find a regression equation is important for the future estimation of the overhead costs. For this purpose, the use of the regression analysis is useful. To checking the claim whether the average overhead costs are same or not for the given five years, the use of inferential statistics or testing of hypothesis is useful. Here, the one way analysis of variance is useful to checking the claim regarding the equality of average overhead costs for the given five years.

Data collection:

The data is collected for the variables overhead costs, the number of machine hours worked the number of direct labor hours and the number of indirect labor workers. Data is collected for the 60 months or five years for the variables overhead costs, the number of machine hours worked the number of direct labor hours and the number of indirect labor workers. Overhead costs are given in $.

Data analysis:

For the data analysis for the variables overhead costs, the number of machine hours worked the number of direct labor hours and the number of indirect labor workers, the use of descriptive statistics is very essential to getting the overall idea about the data and its nature. The descriptive statistics such as mean, standard deviation, range, etc. Should be collected for the variables overhead costs, the number of machine hours worked the number of direct labor hours and the number of indirect labor workers. To finding the correlation coefficient is important due to finding the relationship between the different variables such as overhead costs, the number of machine hours worked, the number of direct labor hours and the number of indirect labor workers. Also, the use of scatter diagram between the pairs of the variables from overhead costs, the number of machine hours worked the number of direct labor hours and the number of indirect labor workers is useful for getting the idea for the relationship. The use of one way analysis of variance is useful for checking the claim that the average overhead costs for the given five years are same or not. By using the p-value for the ANOVA table, the decision would be taken for rejecting or do not rejecting the claim or null hypothesis. The use of regression analysis is useful for finding the regression equation for the purpose of estimating the values of overhead costs based on the variables overhead costs, the number of machine hours worked the number of direct labor hours and the number of indirect labor workers.

Expected Research Outcomes:

1) The average values for the variables overhead costs, the number of machine hours worked the number of direct labor hours and the number of indirect labor workers should be obtained for this study.

2) Correlation coefficient between the different pairs from the variables overhead costs, the number of machine hours worked the number of direct labor hours and the number of indirect labor workers should be obtained for checking the relationship between the different pairs of variables.

3) The results for the claim whether the average overhead costs is same for given five years or not should be obtained from the test.

HERES THE LAST ONE........

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 companys 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

1. Average values of the given variables in the study.
2. The relationship between the given four variables under consideration
3. A regression equation for estimation of overhead costs
4. To check the equality of the average overhead costs for different years.

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:

1. 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.
2. 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.
3. We conclude that that the average overhead costs are same for the given five years.

References:

• David Freedman, Robert Pisani, Roger Purves, Statistics, 3rd ed., W. W. Norton & Company, 1997.
• Morris H. DeGroot, Mark J. Schervish Probability and Statistics, 3rd ed., Addison Wesley, 2001.
• Leonard J. Savage, The Foundations of Statistics, 2nd ed., Dover Publications, Inc. New York, 1972.
• Robert V. Hogg, Allen T. Craig, Joseph W. McKean, An Introduction to Mathematical Statistics, 6th ed., Prentice Hall, 2004.
• George Casella, Roger L. Berger, Statistical Inference, 2nd ed., Duxbury Press, 2001.
• David R. Cox, D. V. Hinkley, Theoretical Statistics, Chapman & Hall/CRC, 1979.

Appendix:

Data:

Year	Month	OH	MH	DLH	IL Workers
2010	1	6965	30472.79	6535	13
2010	2	5649	23049.19	5330	9
2010	3	6528	26245.8	6111	15
2010	4	5628	22782.49	5131	10
2010	5	2812	11791.2	2446	11
2010	6	2177	10855.45	1799	13
2010	7	2854	11639.49	2537	3
2010	8	6010	28805.33	5567	5
2010	9	7994	33379.08	7612	13
2010	10	5631	26806.75	5315	12
2010	11	6587	27699.56	6093	10
2010	12	5044	22731.96	4677	3
2011	1	2570	12647.84	2110	3
2011	2	5265	23002.28	4812	12
2011	3	5715	22875.85	5302	11
2011	4	2127	9010.031	1805	12
2011	5	6424	27760.06	6052	11
2011	6	2201	10903.91	1747	7
2011	7	2484	11274.12	2124	3
2011	8	6388	31460.33	6008	12
2011	9	3206	14298.72	2803	4
2011	10	7660	31575.42	7194	10
2011	11	6642	26695.4	6274	5
2011	12	7407	36991	7065	10
2012	1	2444	11200.53	2013	10
2012	2	6923	30642.98	6483	6
2012	3	2444	11390	1970	10
2012	4	5393	22897.67	4992	13
2012	5	2934	13368.58	2603	10
2012	6	2317	11430.38	1943	5
2012	7	4051	17603.34	3573	4
2012	8	6821	28177.25	6497	9
2012	9	7830	38008.81	7507	14
2012	10	6550	28232.31	6050	9
2012	11	7303	29859	6987	4
2012	12	3202	15057.59	2872	7
2013	1	6093	25267.34	5736	6
2013	2	7649	32886.79	7272	10
2013	3	7838	31473.04	7426	9
2013	4	3313	14030.16	2935	13
2013	5	7576	36660.5	7241	7
2013	6	2106	8502.565	1640	11
2013	7	6591	30097.55	6283	14
2013	8	7515	32103.6	7056	4
2013	9	7487	30503.66	7107	14
2013	10	5411	26982.59	4951	15
2013	11	6154	27552.2	5849	3
2013	12	7533	35876.61	7127	15
2014	1	5481	23840.89	5014	8
2014	2	2964	13333.93	2617	6
2014	3	4755	23231.41	4347	6
2014	4	3606	14638.3	3150	4
2014	5	2506	11398.75	2194	6
2014	6	5007	23623.45	4520	9
2014	7	3010	14772.02	2570	7
2014	8	5639	24645.81	5295	4
2014	9	4846	23007	4424	6
2014	10	4392	21502.68	4050	9
2014	11	3997	18680.47	3530	4
2014	12	5326	24597.81	4841	5

Descriptive Statistics
	N	Minimum	Maximum	Sum	Mean	Std. Deviation
Overhead cost in $	60	2106.00	7994.00	306975.00	5116.2500	1909.89644
Number of machine hours worked	60	8502.57	38008.81	1361831.62	22697.1936	8331.45808
Number of direct labour hours	60	1640.00	7612.00	283114.00	4718.5667	1912.65552
Number of indirect labour workers	60	3.00	15.00	513.00	8.5500	3.68425
Valid N (listwise)	60

Correlations
	Overhead cost in $	Number of machine hours worked	Number of direct labour hours	Number of indirect labour workers
Overhead cost in $	Pearson Correlation	1	.979**	1.000**	.262*
Sig. (2-tailed)		.000	.000	.043
N	60	60	60	60
Number of machine hours worked	Pearson Correlation	.979**	1	.979**	.269*
Sig. (2-tailed)	.000		.000	.037
N	60	60	60	60
Number of direct labour hours	Pearson Correlation	1.000**	.979**	1	.262*
Sig. (2-tailed)	.000	.000		.043
N	60	60	60	60
Number of indirect labour workers	Pearson Correlation	.262*	.269*	.262*	1
Sig. (2-tailed)	.043	.037	.043
N	60	60	60	60
**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1	(Constant)	401.229	27.390		14.649	.000
Number of machine hours worked	.002	.005	.009	.468	.642
Number of direct labour hours	.989	.020	.990	49.207	.000
Number of indirect labour workers	-.010	2.225	.000	-.004	.997
a. Dependent Variable: Overhead cost in $

ANOVA
Overhead cost in $
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	26414498.167	4	6603624.542	1.924	.119
Within Groups	1.888E8	55	3432728.420
Total	2.152E8	59