In interpreting the quality of a regression model, the following aspects are taken into consideration.
High adjusted R2: R2 is proportion of variation in the dependent variable explained by the independent variables. Radj2 adjusts for number of explanatory terms in a model relative to the number of data points (i.e., df associated with the sums of squares).
Significant F-statistic: The null hypothesis that all coefficients (b1, b2 …) are zero can be conclusively rejected.
Significant t-statistics: The individual coefficients are statistically significant.
Residuals are random, normally distributed, with mean 0, and their variance does not vary significantly, over the values of the independent variables.
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