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Main Study—Detailed Statistical Analysis by Multiple Regression

In: Strategic Marketing and Innovation for Indian MSMEs

Author

Listed:
  • R. Srinivasan

    (Indian Institute of Science)

  • C. P. Lohith

    (Siddaganga Institute of Technology)

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.

Suggested Citation

  • R. Srinivasan & C. P. Lohith, 2017. "Main Study—Detailed Statistical Analysis by Multiple Regression," India Studies in Business and Economics, in: Strategic Marketing and Innovation for Indian MSMEs, chapter 0, pages 69-92, Springer.
    • Handle: RePEc:spr:isbchp:978-981-10-3590-6_9
    DOI: 10.1007/978-981-10-3590-6_9
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