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4 cards from this section. Quick, good the night before a test.
What does the linearity assumption imply?
Think about what a scatter plot should look like.
Define homoscedasticity in simple terms.
Consider the consistency of error logs in your data.
What does the linearity assumption in regression analysis refer to?
Think about how figures are represented in scatter plots.
True or False: Homoscedasticity means that error variances are consistent across values of the independent variable.
Recall what happens if residuals change over predicted values.
You are given a dataset with multiple variables. Describe a step-by-step process for checking each of the four assumptions in linear regression.
Remember to visualize your data at every stage to make your conclusions clearer.
You discover a significant level of multicollinearity in your model. Propose strategies to address this issue.
Think about how simplifying your model might help clarify results.
6 more questions in this section. Enroll free to practise them all.
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