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Full Description
An open and dynamic software for statistical analysis, R has become increasingly popular among students and researchers alike for its powerful language and graphical abilities. This book incorporates a step-by-step approach to the basics of statistical tests, the prerequisites and assumptions, the procedures, and outputs and their interpretation all through the lens of R. It is a concise guide to procuring and using R, identifying the types of tests to examine different types of research questions, and the sequential steps for undertaking statistical analysis. Intended largely for readers who are new to statistics or R or to both, this textbook addresses the problems in statistical analysis often faced by the students of social science, education, and management.
Key Features
• Provides the necessary foundation for exploring the frontiers of data science
• Gives an overview of statistical techniques applicable to both cross-section and time-series data analysis using R
• Exercises provided at the end of the chapters to help the readers reinforce their learning
• Applications of statistical techniques covering wide range of subject areas with examples from social sciences and medical sciences
• Robust companion website that includes practice problem datasets for students and solutions to problems, chapter-wise PPTs and teaching modules for instructors
Contents
Foreword by Dilip M Nachane
Preface
Acknowledgments
Introduction
Data Management in R
Describing Data Graphically
Descriptive Statistics
Parametric Tests
Analysis of Variance
Two-Way Analysis of Variance
Analysis of Covariance
Correlation Analysis
Linear Regression Analysis
Nonparametric Tests
Principal Components and Factor Analysis
Logistic Regression
Cluster Analysis
Multidimensional Scaling
Introduction to Time Series Analysis
Volatility Analysis
Bibliography
Index
