Essays on Application of Multiple Regression Analysis in Supply Chain and Logistics Management Decision Coursework

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In general, the paper 'Application of Multiple Regression Analysis in Supply Chain and Logistics Management Decision" is a good example of business coursework.   There are as many contexts in which multiple regression analysis can be used in an organisation; it can be used in various processes and functions. The first part of this report indicates that multiple regression analysis is the single most useful method in respect of analysis and decision making or problem-solving in supply chain and logistics management. It indicates that this analysis takes into account numerous variables and hence it is accurate.

It also indicates that it uses research and turns past data into actionable information. In the second part, the report identifies several specific supply chain and logistics management decision and problem-solving functions in which multiple regression analysis is applied. It demonstrates how it is applied directly in decision making or problem-solving as well as how its application helps the decision-makers to reach a more informed decision in the context of the identified issues. It identifies functions such as marketing, manufacturing, human resource, production, sales and advertising. The last part discusses the limitations of the application of multiple regression analysis.

The report identifies limitations such as limited use if there is no straight line, differentiating between correlation and causation variables and it does not indicate the causal agents. It outlines what can be done to minimise the negative consequences of the limitations in the specific decisions. Likewise, the report indicates that the inclusion of many variables can help to increase accuracy, identifying causal agents, and to consider other factors that may affect variables. 1.0 Introduction The later stages of the 20th century have been marked by swift developments of research methods in real problem solving and decision making.

There have been important institutional and structural changes as well as rapid progress of information technology; they have formed new scenery of the economic and corporate environments towards harnessing decision making and real problem solving as Johnson & Christensen (2010) describes. In this light, the process of the contribution that quantitative concepts and methods have made in management and functional decision making has been significant. In all aspect of daily living, quantitative concepts and methods are applied and used to assist in making decisions as well as solving real problems.

In order to function effectively in the modern and ever-progressing business world, irrespective of the organisation, managers and persons running the organisations must be able to apply and use quantitative concepts, methods and techniques in a reliable and confident manner as Punch (2013) reinforces. Accountants, economists, marketers, personnel managers and other persons in an organisation use information that is increasingly quantitative (McNeil, Frey & Embrechts 2010). It is therefore apparent that they need a working knowledge of the concepts and procedures appropriate for evaluating and analysing such information.

Such analysis, as well as business evaluation, cannot be delegated to mathematicians or specialist statisticians, who, skilful though might have stylish numerical analysis will most often have little understanding of business relevance of such analysis (Bonn & Cantlon 2012). For these reasons, this report aims to provide a quantitative concept and or method that are most useful in analysing, problem-solving and decision making in supply chain and logistics management or an equivalent functional management decision making perspective.

In addition, the report identifies a logistics management problem or decision that the quantitative concept could be directly applied to. The last part of the report provides a discussion of the limitations of using the concept or the quantitative method. The part outlines and explains what can be done to minimise the negative consequences of the said limitations in the specific problem or decision situation discussed.


Allison, P, D, 2002, missing data: Quantitative applications in the social sciences: British Journal of Mathematical and Statistical Psychology, 55(1), 193-196.

Bonn, C, D, & Cantlon, J, F, 2012, the origins and structure of quantitative concepts: Cognitive neuropsychology, 29(1-2), 149-173.

Chatterjee, S, & Hadi, A, S, 2013, regression analysis by example: John Wiley & Sons.

Cohen, J., Cohen, P., West, S. G., & Aiken, L, S, 2013, applied multiple regression/correlation analysis for the behavioral sciences: Routledge.

Collard, B. C. Y., Jahufer, M. Z. Z., Brouwer, J. B., & Pang, E. C. K, 2005, an introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts, Euphytica, 142(1-2), 169-196.

Dawson, J. F., & Richter, A, W, 2006, probing three-way interactions in moderated multiple regression: development and application of a slope difference test: Journal of Applied Psychology, 91(4), 917.

Graham, M, H, 2003, confronting multicollinearity in ecological multiple regression: Ecology, 84(11), 2809-2815.

Hair, J, F, 2009, Multivariate data analysis: Pearson Prentice Hall.

Hair, J. F., Tatham, R. L., Anderson, R. E, & Black, W, 2006, Multivariate data analysis, Upper Saddle River, NJ: Pearson Prentice Hall.

Jaccard, J., & Turrisi, R, 2003, Interaction effects in multiple regressions (Vol. 72): Sage.

Johnson, R. R. B., & Christensen, L, B, 2010, educational research: Quantitative, qualitative, and mixed approaches: Sage Publications.

Kutner, M. H., Nachtsheim, C., & Neter, J, 2004, applied linear regression models: McGraw-Hill/Irwin.

Lind, D, A, Marchal, W, G, & Wathen, S, A, 2005, Statistical techniques in business & economics: Canberra.

Lind, D. A., Marchal, W. G., & Wathen, S, A, 2006, basic statistics for business and economics, Boston: McGraw-Hill/Irwin.

McNeil, A. J., Frey, R., & Embrechts, P, 2010, quantitative risk management: concepts, techniques, and tools: Princeton university press.

Mertler, C, A, & Vannatta, R, A, 2002, advanced and multivariate statistical methods; Los Angeles, CA: Pyrczak.

Moffett, S., McAdam, R., & Parkinson, S, 2003, an empirical analysis of knowledge management applications: Journal of knowledge Management, 7(3), 6-26.

Montgomery, D. C., Peck, E. A., & Vining, G, G, 2012, introduction to linear regression analysis (Vol. 821): John Wiley & Sons.

Preacher, K. J., Curran, P. J., & Bauer, D, J, 2006, computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis: Journal of Educational and Behavioral Statistics, 31(4), 437-448.

Punch, K, F, 2013, introduction to social research: Quantitative and qualitative approaches, Sage.

Raudenbush, S. W., & Bryk, A, S, 2002, hierarchical linear models: Applications and data analysis methods (Vol. 1), Sage.

Sincich, T, 2003, a second course in statistics: regression analysis. Upper Saddle River, New Jersey: Pearson Education, Inc.

Yuan, J. S., Reed, A., Chen, F., & Stewart, C, N, 2006, Statistical analysis of real-time PCR data: BMC bioinformatics, 7(1), 85.

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