In the model diagnosis step in regression modeling, we are interested in whether the overall regression model and individual variables are significant in addition to satisfying regression analysis assumptions.

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In the model diagnosis step of regression modeling, the focus is not only on checking if the regression analysis assumptions (like linearity, homoscedasticity, normality of residuals, etc.) are met, but also on assessing the significance of the overall regression model (using tests like the F-test) and the significance of individual predictors (using t-tests), to ensure that the model and the variables within it contribute meaningfully to explaining the variation in the dependent variable.

In the Model Diagnosis Step in Regression Modeling, We Are