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Textbook of Parametric and Nonparametric Statistics (Paperback) | Released: 17 Jan 2019
By: Vimala Veeraraghavan (Author) Publisher: Sage Text28.07% Off ₹428.00
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This unique textbook guides students and researchers of social sciences to successfully apply the knowledge of parametric and nonparametric statistics in the collection and analysis of data. This book comprehensively covers all the methods of parametric and nonparametric statistics such as correlation and regression, analysis of variance, test construction, one-sample... Read More
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Author:
Vimala Veeraraghavan
Publisher Name:
Sage Text
Language:
English
Binding:
(Paperback)
About The Book
This unique textbook guides students and researchers of social sciences to successfully apply the knowledge of parametric and nonparametric statistics in the collection and analysis of data. This book comprehensively covers all the methods of parametric and nonparametric statistics such as correlation and regression, analysis of variance, test construction, one-sample test to k-sample tests, etc. The two methods of statistics are presented simultaneously, with indication of their use in data analysis. Through adequate knowledge of both techniques, readers can select the appropriate method of testing as well as the graphical method for representing the data. Key Features - Provides a comparative account of the two statistical methodologies: why, when and how to use either of the two methods - Enables the readers to consider and identify the nature of data and apply the most suitable test - Exclusive chapter on SPSS and Excel-based statistical analysis of data - Special coverage of the use of statistics in psychology and psychological test construction - Contains worked-out problems to help students and scholars for better understanding of the concepts and further practice Table of Contents: Preface Acknowledgement About the Authors Introduction to Statistics SECTION A: PARAMETRIC STATISTICS Frequency Distribution Measures of Central Tendency Measures Of Variability Correlation, Regression and Pr
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