Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Introduction to Regression Analysis

Introduction to Regression Analysis

Illukkumbura, Anusha

Paperback

Series: Easy Statistics, Book 1

General Education

ISBN13: 9798649727648
Publisher: Independently Published
Published: May 30 2020
Pages: 122
Weight: 0.50
Height: 0.26 Width: 7.00 Depth: 10.00
Language: English
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 CorrelationSimple Linear RegressionResidual Tests Non-Linear RegressionMultiple 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 (RStudio Version 4.0.0). 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.

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