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# Regression Analysis Models for Marketing Decision Making - Essay Example

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The writer of the essay "Regression Analysis Models for Marketing Decision Making" seeks to identify various regression analysis models being applied by businesses in trying to get information from quantitative data and the information being used in making a business decision…

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﻿Regression Analysis Models for Marketing Decision Making
Synopsis
Introduction
Decision-making is the core of business, and correct decisions result into businesses thriving. Marketing decisions in a company are made based on information that is as a result of data interpretation or analysis. Analysis of quantitative data which is mainly measurable is done through several statistical models. The research seeks to identify various regression analysis models being applied by businesses in trying to get information from quantitative data and the information being used in making a business decision especially in the marketing department.
Regression Definition
Regression analysis is a statistical technique that determines linear relationships between two or more variables. Businesses mainly use regression as a causal inference and for predictions. The major regression models available are linear regression model, non-linear regression model, logistic regression and multinomial logistic regression. Simple regression models use only two variables to achieve a particular statistical result. Multiple linear regression is a regression that applies more that two variables.
Logistic regression procedures in quantitative statistics will produce all predictions, residuals and influence statistics. Logistic regression also produces goodness-of-fit tests using sales and marketing data in the case where it has to make predictions for the marketing department. The goodness-of-fit tests are created at the individual case level, and this is regardless of methods of data insertion and whether or not the number of covariate patterns is lesser than the total number of instances in question. On the other hand, multinomial logistic regression procedure aggregates all cases internally to form subpopulations with identical covariate patterns for the predictors, residuals, and goodness-of-tests.
Non-linear regression is a quantitative statistical method of finding a nonlinear model of the relationship between the dependent variable and a set of several independent variables. Current non-linear models can be used to estimate models with arbitrary relationships between dependent and independent variables. Iterative estimation is mostly used to achieve non-linear regression.
Problem Statement
Predicting future marketing trends is business is an essential requirement for the management if they have to beat the competition. This is because a lot of the data is available for use by business nowadays due to advancements in technology like the web that collects a lot of statistical data for analysis. The primary problem facing businesses is identifying the optimal data analysis model to use in the analysis the quantitative data and getting valid information for predicting the future marketing trends.
Objectives and Aims
The research seeks to define and identify the optimal regression analysis models available today. The paper will describe several procedures available in using the regression models to perform data analysis. The paper will then simulate some marketing data to identify the best regression that a business can employ within various marketing situations. The paper will consider and determine all the assumptions that businesses are allowed to apply in using the regression models. The paper will suggest the optimal or the best regression model that a mid-sized business entity can apply depending on the several amount of data and variable they can acquire. Finally, the paper will suggest and analyze how several software application like SPSS can be used in using the optimal regression model identified in the research.
Challenges
The research will mostly use on-line articles in studying the various regression models based on previously published research without the use of actual sample data in testing the regression models. Acquiring statistical software application will also become a challenge due to most of them being commercially available.

Bibliography
David F. Groebner, Patrick W. Shannon, Phillip C. Fry, Kent D.A Decision-Making Approach. 8Th Ed. New York.
Douglas A. Lind, William G. Marchal, and David Wathen, (2012). Statistical Techniques in Business and Economics. 13Th Ed. Prentice Hall. New York.
Anderson and Sweeny, (2010). Essentials of Modern Business Statistics with Microsoft Excel.3rd Ed. Prentice Hall. New York.
StatSoft (2015). General Regression Models. Retrieved From;http://www.statsoft.com/Textbook/General-Regression-Models
David W. Stockburger, (2014). Regression Models.Retrieved From; http://www.psychstat.missouristate.edu/introbook/sbk16.htm
Brian Caffa, (2014). Regression Models. Retrieved From; https://www.coursera.org/course/regmods.
References
Arnold, Carolyn L. (1992) An Introduction to Hierarchical Linear Models. Measurement and Evaluation in Counseling and Development, 25, 58-90.
Douglas A. Lind, William G. Marchal, and David Wathen, (2012). Statistical Techniques in Business and Economics. 13Th Ed. Prentice Hall. New York.
Field A., 2006. Discovering Statistics using SPSS. Second Edition. London. Sage Publications.
Greene W.H., 2002. Econometric Analysis. Fifth Edition. New Jersey. Prentice Hall.
Render, B., R.M. Stair and M.E. Hanna, Quantitative Analysis for Management.11th Ed. Prentice Hall.
Curwin, Jon; Slater, Roger, Quantitative methods for business decisions.
Mark Berenson and David Levine, Basic Business Statistics. New York. Prentice Hall.
Taha, Hamdy A, Operations research: An introduction. Prentice Hall. New York.
Malhotra N., 2010. Marketing Research. An Applied Approach. Sixth Edition. Ney Jersey. Pearson Eduction.
Snedecor, G. W. and W. G. Cochran. 1980. Statistical methods. The Iowa State University Press,.Ames, Iowa. Read More
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