Abstract
Out of 150 manufacturing firms, 91 firms responded completely, which was used here for the main data analysis. After the preliminary data analysis done, the detailed statistical analysis of the collected data by multiple regression is attempted in this chapter. The first step in the detailed statistical analysis is the verification of the assumptions underlying multiple regression analysis. Linearity , constant variance (homoscedasticity) and normality 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
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1.
Assumptions in multiple regression analysis.
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2.
Concept of Linearity, Homoscedasticity and Normality.
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3.
Concept of outliers and influential’s.
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4.
Concept of Multicolinearity.
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References
Hair JF Jr, Black WC, Babin BJ, Anderson RE, Tatham RL (2007) Multivariate data analysis, 6th edn. Pearson Prentice Hall (Chap. 1, 2 & 4)
Kutner MH, Nachtsheim CJ, Neter J, Li W (2005) Applied linear statistical models, 5th edn. Mc Graw Hill
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Srinivasan, R., Lohith, C.P. (2017). Main Study—Detailed Statistical Analysis by Multiple Regression. In: Strategic Marketing and Innovation for Indian MSMEs. India Studies in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-10-3590-6_9
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DOI: https://doi.org/10.1007/978-981-10-3590-6_9
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