Introduction to Regression Analysis (Easy Statistics)

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Free Download Introduction to Regression Analysis (Easy Statistics)
English | 2020 | ASIN: B089FLTKK7 | 121 pages | PDF | 7.00 MB
Introduction to Regression Analysis made it to the Best Statistics eBooks of All Time BookAuthority Best Statistics eBooks of All TimeI'm happy to announce that my book, "Introduction to Regression Analysis (Easy Statistics)", made it to BookAuthority's Best Statistics eBooks of All Time

Regression Analysis is a major coursework for many research and statistics students. Although many know only about simple linear regression analysis, it actually expanded into non-linear regression analysis and multiple linear regression analysis as well. Non linear regression analysis is used for data sets which do not have a linear relationship and multiple linear regression analysis is used for data sets with more than one independent variable. Most importantly, residual tests in regression analysis are used to confirm the stability and reliability of the regression model. Residual tests are a crucial point in any regression analysis, that a student must have knowledge on. Many students and researchers fail to analyze data sets properly due to lack of knowledge in all types of regression analysis. If you do not get desirable and fitting regression model, then it must be that another regression model should be used for the data set. Furthermore, the books which are written on complex topics on regression analysis are not easy to understand. Identifying this shortage , writer has dedicated her time into writing a comprehensible book on regression analysis covering the topics related to Correlation Simple Linear Regression Residual Tests Non-Linear Regression Multiple linear regression. Even the complex calculations are presented step by step, in an uncomplicated manner. The examples are solved using manual calculations and statistical software such as Minitab and R .
There are examples for simple linear regression, non-linear regression and multiple linear regression using R language, with necessary codes. Each example outputs are explained clearly
Concepts such as parameter testing, residual testing, ANOVA table, exponential regression models, quadratic regression models, partial F test, multi-collinearity , best subsets regression and stepwise regression are also explained with relevant examples.
"Introduction to Regression Analysis" is a basic but complete guide to regression analysis Even a beginner can easily self-study. Necessary commands and outputs are explicitly presented in each example
Interpreting outputs is one of the major struggles a student go through in his or her research work.
This book guides how to interpret regression analysis outputs properly
If you want to champion your knowledge on regression analysis, use this book
Note: If you find any content of this with more simple explanation please send me an e-mail to anusha.illukkumbura@yahoo.com stating the page number and the paragraph which should be explained more.

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