Main Study—Detailed Statistical Analysis by Multiple Regression

R. Srinivasan () and C. P. Lohith
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R. Srinivasan: Indian Institute of Science
C. P. Lohith: Siddaganga Institute of Technology

Chapter Chapter 9 in Strategic Marketing and Innovation for Indian MSMEs, 2017, pp 69-92 from Springer

Abstract: Abstract Out of 150 manufacturing firms, 91 firms responded completely, which was used here for the main data analysis. After the preliminary data analysisPreliminary Data Analysis done, the detailed statistical analysis of the collected data by multiple regressionMultiple regression is attempted in this chapter. The first step in the detailed statistical analysis is the verification of the assumptions underlying multiple regression analysis. LinearityLinearity , constant variance (homoscedasticity)Homoscedasticity and normalityNormality are the three assumptions which will be addressed for all the individual variables. Then it proceeds to the estimation of the regression model and assessing the overall model fit. The key takeaways for the reader from this chapter are listed below 1. Assumptions in multiple regression analysis. 2. Concept of Linearity, Homoscedasticity and Normality. 3. Concept of outliers and influential’s. 4. Concept of Multicolinearity.

Keywords: Research design; Multiple regression; Independent variable; Dependent variable; Linearity; Homoscedasticity; Normality; Scatter plot matrix; Box plots; Normal probability plot; Skewness; Kurtosis; Outliers; Influentials (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-981-10-3590-6_9

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