Which different presumptions underpin the multivariate regression analysis?
What are the various assumptions on which the multivariate regression analysis rests?
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Multivariate regression analysis, a statistical technique used to understand the relationship between one dependent variable and two or more independent variables, is based on several key assumptions. Ensuring these assumptions are met is crucial for the validity of the regression model. The primary assumptions are:
1. Linearity
2. Independence
3. Multivariate Normality
4. No or Little Multicollinearity
5. Homoscedasticity
6. No Endogeneity of Regressors
7. Adequate Sample Size
8. Model Specification
Conclusion
Meeting these assumptions is crucial for the reliability and interpretability of a multivariate regression analysis. Violations of these assumptions can lead to biased, inconsistent, or inefficient estimates, affecting the conclusions drawn from the model. It's important to conduct diagnostic tests and consider remedial measures if any of these assumptions are violated.