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Ace Linear Regression Practical Exam
·
Module 4
Diagnosing Model Assumptions
1
Detecting Curvature in Residual Plots
Assess the linearity assumption by identifying patterns (e.g., curvature) in a residual vs. fitted values plot.
2
Identifying Heteroscedasticity in Residual Plots
Diagnose heteroscedasticity (non-constant variance) by identifying a funnel shape in a residual vs. fitted values plot.
3
Interpreting Normal Q-Q Plots for Residuals
Evaluate the normality of residuals by interpreting a Normal Q-Q plot.
4
Detecting Autocorrelation: Understanding and Identifying Independence Violations
Explain the independence assumption and how its violation (autocorrelation) can be detected.
Previous module
The ANOVA Table & Overall Significance
Next module
Interpreting Multiple & Categorical Predictors