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Full Description
Applied Multiple Regression/Correlation Analysis for Aviation Research describes and illustrates multiple regression/correlation (MRC) analysis in an aviation context, including flight instruction, airport design, airline routes, and aviation human factors research. Structured in four parts, the book first reviews the major concepts of bivariate correlation and regression and then extends the bivariate case to two, four, and k predictors coupled with discussions on statistical inference, underlying assumptions, and regression diagnostics relative to MRC analysis. The book then builds on this foundation by presenting MRC variable selection strategies (simultaneous, hierarchical, and statistical regression), analyzing sets of predictors, and introducing coding strategies for nominal predictors. The book concludes by presenting how MRC can be used to conduct an analysis of covariance (ANCOVA), interactions, mediation analysis, and binary logistic regression.
Throughout the presentation, the book provides a balance between procedural knowledge as well as conceptual understanding. Detailed guided examples are presented at the end of each chapter that apply the topics and concepts of the chapter from the perspective of conducting a research study. The analytic strategies demonstrated via these guided examples are clearly explained, enabling readers to understand, conduct, and report results correctly. Pedagogical features associated with each chapter include a set of student learning outcomes and an end-of-chapter package that consists of a summary of the chapter's main topics/concepts, a list of key terms, and review exercises, including multiple-choice items and a research scenario with data for students to analyze.
Tailored to the needs of aviation students, Applied Multiple Regression/Correlation Analysis for Aviation Research is the ideal textbook for research-oriented graduate aviation programs such as a thesis-based master's degree or doctoral program that require knowledge of advanced statistical strategies for analyzing research data.
Contents
Part A Fundamental Concepts of Correlation and Regression 1. Bivariate Correlation 2. Bivariate Linear Regression Part B Establishing the Foundation of MRC 3. MRC with Two Predictors 4. MRC with More Than Two Predictors 5. Preliminary Data Screening: Regression Assumptions and Diagnostics Part C Data Analytic Strategies for MRC 6. Multiple Regression Strategies for Variable Selection 7. Analyzing Sets of Independent Variables 8. Coding Strategies for Nominal IVs Part D Additional Applications of MRC 9. Combining Nominal and Continuous IVs, ANCOVA, and Interactions 10. Other Regression Models: Mediation and Binary Logistic Regression Appendixes: A. Key Concepts in Research and Statistics B. Statistics Tables C. Answers to Part A Review Exercises
