Applied Statistics

Business and Management Research

Inbunden, Engelska, 2022

AvAndrew R. Timming

2 689 kr

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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® SPSS® 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.

Produktinformation

Utgivningsdatum2022-05-18
Mått189 x 246 x 29 mm
Vikt1 050 g
FormatInbunden
SpråkEngelska
Antal sidor456
Upplaga1
FörlagSAGE Publications
ISBN9781473947443

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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: FoundationsChapter 1: Introduction to StatisticsChapter 2: Exploring IBM SPSSChapter 3: Descriptive Statistics and Graphical RepresentationsChapter 4: The Principle of Statistical InferencePart II: Comparing MeansChapter 5: The T-TestChapter 6: Analysis of VariancePart III: Non-Parametric and Correlational RelationshipsChapter 7: Chi-SquareChapter 8: Simple Regression and Pearson’s rPart IV: Multivariate ModelingChapter 9: Multiple RegressionChapter 10: Logistic RegressionChapter 11: Exploratory and Confirmatory Factor AnalysesChapter 12: Structural Equation Modeling