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9781473947443 Academic Inspection Copy

Applied Statistics

Business and Management Research
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Written for the non-mathematician and free of unexplained technical jargon, Applied Statistics: Business and Management Research provides a user-friendly introduction to the field of applied statistics and data analysis. Featuring step-by-step explanations of how to carry out successful quantitative research, and supported by examples from IBM (R) SPSS (R) Statistics, this textbook is an essential resource for students and researchers of business and management. A range of online resources for both students and lecturers, including a teaching guide, PowerPoint slides and datasets, are available via the companion website. Andrew R. Timming is Professor of Human Resource Management and Deputy Dean Research & Innovation in the School of Management at RMIT University, Australia.
Andrew R. Timming is Professor of Human Resource Management and Organizational Psychology at RMIT University, also known as the Royal Melbourne Institute of Technology, in Australia. He holds a Ph.D. degree from the University of Cambridge, England. He is the inaugural Registered Reports Editor at Human Resource Management Journal. His previous book, Human Resource Management and Evolutionary Psychology: Exploring the Biological Foundations of Managing People at Work was published in 2019. Professor Timming is mainly known for his research on tattoos and is currently researching mental illness in the workplace. When he's not working in the office, he can usually be found working at home.
Part I: Foundations Chapter 1: Introduction to Statistics Chapter 2: Exploring IBM SPSS Chapter 3: Descriptive Statistics and Graphical Representations Chapter 4: The Principle of Statistical Inference Part II: Comparing Means Chapter 5: The T-Test Chapter 6: Analysis of Variance Part III: Non-Parametric and Correlational Relationships Chapter 7: Chi-Square Chapter 8: Simple Regression and Pearson's r Part IV: Multivariate Modeling Chapter 9: Multiple Regression Chapter 10: Logistic Regression Chapter 11: Exploratory and Confirmatory Factor Analyses Chapter 12: Structural Equation Modeling
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