Analytic Methods Proposal: Case of Moving Company

Analytic Methods Proposal: Case of Moving Company

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Abstract

This paper is a proposal and justification of an appropriate inferential statistical technique in an attempt to solve a business problem faced by a moving company. The project analyzes two states based in the US. The moving company has been established in the US and is trying to find the best business potential state in which to move its headquarters. The analysis covers the current company’s state of residence Mississippi and the California State where it intends to shift its headquarters. The paper identifies the migration, years and population estimates from the economic research service to facilitate the research. Additionally, prediction and test inferential statistical techniques are preferred to address the business problem. Thus, simple linear regression is proposed for the prediction. Linear regression is distinguished from the t-test hypothesis testing due to its outstanding match for the business problem. Moreover, the tool has been supported by experiences and examples that prove its reliability.

Keywords:  inferential statistical techniques, moving company, headquarters, states, linear regression, t-test hypothesis, and proposal.

Analytic Methods Proposal: Case of Moving Company

An inferential statistical technique is one of the two branches of statistics. The technique uses a random sample of the population to make predictions of its properties. According to Lowry (2014), the use of inferential statistics is valuable when examination of the properties of every member of an entire population is impractical. This paper aims to select the appropriate inferential technique by proposition and support to recommend the U.S founded company to move its headquarters to the best state with significant business opportunities. The most appropriate inferential technique to address the problem of moving the company’s headquarters is a simple linear regression.

Business Problem

The executive leadership of a newly opened moving company in the US is faced with the challenge of deciding the best state that has significant potential business in which to move their headquarters. Therefore, the manager has asked the analyst to conduct a research process by comparing the current state of residence, Mississippi, to another state of choice, California, and make recommendations based on the findings.

Data Used

The data used for the research will include the population estimates for Mississippi and California states for the years between 2010 and 2017 (Economic Research Service, 2017). The data related to the Mississippi State is identified by labeling all the state’s records as ‘MR’ while those relating to California are identified by labeling the state as ‘CR.’ It is assumed that the company has a preference for domestic moves. Thus, the net migration for the years 2010 to 2017 and domestic migration for the same period are reviewed for analysis of the two states, Mississippi and California. Also, the availability of variables such as migration data from the United States Department of Agriculture and duration in years from 2010 to 2017 will determine the appropriate inferential technique to solve the business problem.

Analytic Inferential Methods

There are different inferential approaches to address the business problem. The analyst made the inference of prediction to identify the future migration pattern of the two states, Mississippi and California using simple linear regression analysis. The inference of prediction is based on domestic migration for the period between the year 2010 and 2017 (Bowerman, 2016). The other inference made was a test which involved a t-test hypothesis testing that is based on the difference in means of the net and domestic migration for Mississippi and California states. If the mean of the yearly differences in net migration of the two states between the period between 2010 and 2017 is equal to 0 vs. not equal to 0, the paired t-test hypothesis on net migration will test (Bowerman, 2016). The paired t-test hypothesis test on domestic migration would test if the mean of the annual differences in domestic migration from 2010 to 2017for Mississippi and California are equal to 0 vs. are no equal to 0.

Inferential Statistical Technique Proposal

 The proposed inferential statistical technique for prediction is simple linear regression. The technique shows how the dependent and independent variables are related using a line chart (K. Chen; H. Chen, 2014). Thus, it is best applied to predict the future migration rates given the duration and migration variables. The technique is popularly used in pricing, marketing effectiveness and promotions (K. Chen; H. Chen, 2014). As an example, companies use the simple linear regression formula to know whether the amounts invested in marketing a particular brand has yielded a substantial return on investment.

Inferential Statistical Technique Justification

The best tool for the situation is simple linear regression. According to Statistics Learning Centre (2015), the tool is cable of predicting the number of people moving in and out of the Mississippi and California based on values of an independent variable (domestic migration) readily available. In case a phenomenon of more than one variable arises in the future, multiple linear regressions would also be appropriate. Besides, simple linear regression is graphically represented thus easy for the management to interpret. The inferential technique is also supported by its high-rank use in numerous fields including the biology, social and behavioral sciences to describe possible relationships between variables. Also, numerous linear regression estimation techniques including the least-squares and maximum-likelihood techniques have shown those same variables have common relationships. Therefore, the simple linear regression inferential technique is reliable.

Conclusion

The paper intended to select the best inferential techniques to determine the most appropriate state to move the company. Simple linear regression has been perceived to be the most appropriate statistical inference to solve the problem of moving the company’s headquarters from Mississippi to California State, a significant potential business residence. Data to facilitate the research was gathered from the United States Department of Agriculture which included population estimates, migration figures and years. The preferred inferences were prediction and test. However, simple linear regression was proposed for prediction due to its applicability in businesses for pricing and marketing effectiveness. The technique has also been justified as the best due to its high use in other fields including biology, social and behavioral sciences. Also, the technique has various estimations methods that yield the same results proving its reliability.

References

Bowerman, B. (2016). Business Statistics in Practice: Using Data, Modeling, and Analytics. McGraw-Hill Higher Education.

Chen, K. S., & Chen, H. T. (2014). Applying importance–performance analysis with simple regression model and priority indices to assess hotels' service performance. Journal of Testing and Evaluation, 42(2), 455–466.

Lowry, R. (2014). Concepts and applications of inferential statistics.

Statistics Learning Centre. (2015). Understanding statistical inference [Video] | Transcript Retrieved from https://www.youtube.com/watch?v=tFRXsngz4UQ

U.S. Department of Agriculture, Economic Research Service. (2017). Download data: County-level data Sets. Retrieved from https://www.ers.usda.gov/data-products/county-level-data-sets/county-level-data-sets-download-data/