In the context of multiple linear regression, which of the following conditions is typically assessed first during the steps of hypothesis testing?

• A. Homoscedasticity of residuals
• B. Normality of residuals
• C. Linearity between the independent variables and the dependent variable
• D. Absence of multicollinearity among predictors

Answer: (C)

Verified step by step guidance

1. Understand that in multiple linear regression, before performing hypothesis testing, it is crucial to verify that the model assumptions are met to ensure valid inference.
2. Recognize that the first condition typically assessed is the linearity between the independent variables and the dependent variable, because the model assumes a linear relationship to properly fit the data.
3. To check linearity, you can use scatterplots of each predictor against the response variable or partial residual plots to visually inspect if the relationship appears linear.
4. After confirming linearity, other assumptions such as homoscedasticity (constant variance of residuals), normality of residuals, and absence of multicollinearity among predictors are assessed in subsequent steps.
5. Remember that verifying linearity first helps ensure that the regression model is appropriate before moving on to test the other assumptions and conducting hypothesis tests on the regression coefficients.