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Applied Statistics : Business and Management Research/ Andrew R. Timming

By: Material type: TextTextLanguage: English Publication details: London : Sage Publications Ltd, 2022.Description: xvi, 438 pages : illustrations, form ; 25 cmISBN:
  • 9781473947450
Subject(s): DDC classification:
  • 23 519.502465 TIM
Contents:
TABLE OF CONTENTS 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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Item type Current library Collection Call number Status Date due Barcode
General Books General Books CUTN Central Library Sciences Non-fiction 519.502465 TIM (Browse shelf(Opens below)) Available 47670

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.

TABLE OF CONTENTS
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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