The following report will provide an insight into regression analysis based on three sections. First, the technique will be described in a non-mathematical and simple way by indicating it in a six-way procedure. Section two will identify limitations to regression analysis indicating when it is appropriate to be used and what limitations arise once it is applied. Finally, the third section of this report will provide two research examples which are established according to the six-stage-procedure exemplified in the technique description section.
Table of Contents
1. REGRESSION ANALYSIS - TECHNIQUE DESCRIPTION
2. USAGE AND LIMITATIONS OF REGRESSION ANALYSIS
3. RESEARCH EXAMPLES
3.1. The Effect of a Quality Management System on Supply Chain Performance: An Empirical Study in Taiwan” (Liu 2009)
3.2 Applying the Theory of Planned Behavior (TPB) to Predict Internet Tax Filing Intentions (Ramayah, Yusliza et. al 2009)
Objectives and Topics
This report provides a comprehensive, non-mathematical overview of regression analysis, detailing its operational stages and practical utility in business decision-making, while critically examining its limitations through the analysis of existing research examples.
- Theoretical framework of the six-stage regression procedure.
- Core assumptions for valid multivariate analysis (linearity, normality, homoscedasticity, independence).
- Categorization of limitations, including conceptual issues and data-related errors.
- Evaluation of empirical research applying multiple regression models.
- Practical guidance on data transformation and model validation.
Excerpt from the Book
Stage 1: Stating the Research Problem
The first stage in multiple regression analysis is to determine the researcher’s objective meaning that a dependent variable must be selected which the researcher wishes to have predicted. Once the researcher knows what he wants to predict, he selects variables that he considers to be influential to the dependent variable, variables that have explanatory intention of why changes in these independent variables influence the dependent or predicting variable.
Based on the researchers theoretical knowledge he can assume which of the independent variables he has selected carries what weight in predicting the dependent variable. Essential to use multiple regression analysis is the selection of metric or quantitative variables and no non metric or qualitative variables unless they are transformed to dummy variables which this report will refer to later on. When selecting the variables, the researcher must be aware of specification errors which are either the inclusion of a non-essential variables or the omission of an essential one which in both cases can lead to a non-model parsimony which further leads to a fraud outcome of the regression analysis. Once the researcher knows what he would like to have predicted and by what means, he has to collect data being the second step of regression analysis.
Summary of Chapters
1. REGRESSION ANALYSIS - TECHNIQUE DESCRIPTION: This chapter outlines the six-stage procedural framework for conducting multiple regression analysis, emphasizing the selection of variables and meeting statistical assumptions.
2. USAGE AND LIMITATIONS OF REGRESSION ANALYSIS: This chapter discusses the practical applications of the technique in business while identifying critical barriers such as measurement errors and multicollinearity.
3. RESEARCH EXAMPLES: This chapter applies the previously defined six-stage methodology to evaluate two specific empirical studies, highlighting strengths and common procedural omissions in academic research.
Keywords
Regression Analysis, Multivariate Analysis, Independent Variables, Dependent Variable, Statistical Significance, Multicollinearity, Homoscedasticity, Normality, Linearity, Research Methodology, Data Transformation, Specification Error, Generalizability, Dummy Variables, Model Validation.
Frequently Asked Questions
What is the primary focus of this paper?
The paper provides a non-technical, conceptual introduction to regression analysis, specifically tailored for business students and practitioners to understand its mechanics and pitfalls.
What are the main thematic fields covered?
The work covers statistical technique descriptions, the identification of regression assumptions, potential limitations, and the critical assessment of real-world research applications.
What is the core objective of the research?
The objective is to explain the six-stage procedure of regression analysis and to demonstrate how researchers can ensure valid, generalizable results by strictly adhering to these stages.
Which scientific method is utilized in this document?
The document utilizes a systematic literature analysis and a comparative evaluation method, benchmarking selected case studies against a defined six-stage theoretical model.
What topics are addressed in the main body of the paper?
The main body details the six stages of regression, from stating the research problem to validating results, followed by a critique of two specific studies regarding supply chain management and internet tax filing.
Which keywords define this work?
Key terms include Multivariate Analysis, Regression Assumptions, Multicollinearity, Statistical Significance, and Research Methodology.
How does the paper differentiate between types of regression limitations?
The paper divides limitations into two categories: those that prevent the use of the technique (preconditions) and those that arise during its application, such as specification or measurement errors.
Why are the two research examples included in the study?
They serve as practical illustrations to test whether published studies correctly follow the six-stage procedure required for valid statistical outcomes.
What conclusion does the author reach regarding the researched examples?
The author concludes that valid results cannot be guaranteed in the researched examples because the authors of those papers frequently skipped critical steps, such as testing for statistical assumptions.
- Arbeit zitieren
- Matthias Zöphel (Autor:in), Christian Egger (Autor:in), Hansjakob Riedi (Autor:in), 2008, A Short Critical, Non-Technical, Non-Mathematical Paper about Regression Analysis, München, GRIN Verlag, https://www.grin.com/document/171083
