Results and Discussion
The regression models were estimated in the previous chapter. The regression variate was specified and the diagnostic tests that confirm the appropriateness and the assumptions underlying the regression models were administered. In this chapter, we examin
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Results and Discussion
Abstract The regression models were estimated in the previous chapter. The regression variate was specified and the diagnostic tests that confirm the appropriateness and the assumptions underlying the regression models were administered. In this chapter, we examine the predictive equation based on four independent variables and five dependent variables. The regression coefficients become indicators of the relative impact and importance of the independent variables (Strategic Marketing) in their relationship with the dependent variable (Innovation performance). The final task in multiple regression is the validation process of the regression model. The primary concern of this process is to ensure that the results are generalisable to the population and not specific to the sample used in estimation. Here in our research the split sample validation process is adopted for the models validation and followed by the final conclusion regarding the study made with respect to both Karnataka and Tamil Nadu. The key takeaways for the reader from this chapter are listed below 1. 2. 3. 4. 5.
Interpreting the regression variate and coefficient. The predictive equation for models based on the confirmatory approach. Overview of results and its validation. Conclusion and important findings of this study. Limitations of this study and directions for future research.
Keywords Regression variate Regression coefficient Predictive equation Confirmatory estimation approach Standardized coefficient
10.1
Explanation with Multiple Regression
Multiple regression provides a means of objectively assessing the degree and character of the relationship between dependent and independent variables. It is accomplished by forming the variate of independent variables and then examining
© Springer Nature Singapore Pte Ltd. 2017 R. Srinivasan and C.P. Lohith, Strategic Marketing and Innovation for Indian MSMEs, India Studies in Business and Economics, DOI 10.1007/978-981-10-3590-6_10
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10 Results and Discussion
the magnitude, sign, and statistical significance of the regression coefficient for each independent variable. In this manner, the independent variables can be considered for their individual contribution to the variate and its explanations. Interpretation of the variate may rely on any of the three perspectives: 1. The importance of the independent variables. 2. The types of relationships found 3. The interrelationship among the independent variables. The most direct interpretation of the regression variate is the determination of the relative importance of each independent variable in the explanation of the dependent measure. Multiple regression analysis provides a means of objectively assessing the magnitude and direction (positive or negative) of each independent variables relationship. In addition to assessing the importance of each variable, multiple regression also provides a means of assessing the nature of the relationships between the independent variable and the dependent variable. Finally, multiple r
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