Applied Statistics : Business and Management Research/ Andrew R. Timming
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- 9781473947450
- 23 519.502465 TIM
Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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CUTN Central Library Sciences | Non-fiction | 519.502465 TIM (Browse shelf(Opens below)) | Available | 47670 |
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519.502 TIM Data science : a first introduction / | 519.5024 MUJ Statistics for Managers / | 519.502465 SHA Managerial Statistics/ | 519.502465 TIM Applied Statistics : Business and Management Research/ | 519.50285 MAI Data Analysis and Graphics Using R : | 519.50285 MAI Data Analysis and Graphics Using R : | 519.50285 MAI Data Analysis and Graphics Using R : |
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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