Practice Assumption Details (6.1) - Regression Analysis - Data Science Basic

Practice: Assumption Details

Lesson 6.1Regression Analysis

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9 questions

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6 sample questions

Question 1.
What does the linearity assumption imply in regression analysis?
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Think about the shape of the graph.
Question 2.
Why is homoscedasticity important in regression?
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Think about what happens if variances change.
3Question 3.
What is the main requirement of the linearity assumption?
- The output should be discrete
- The relationship should be linear
- The independent variables should be correlated
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Think about how we visualize relationships.
4Question 4.
True or False: Homoscedasticity means that the variance of errors can vary at different levels of the independent variable.
- True
- False
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What does 'homo' suggest about the variances?
Question 5.
You suspect that your model violates the homoscedasticity assumption. Describe a detailed approach to investigate and remedy this issue.
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Think about visual diagnostics and their implications.
Question 6.
Your regression model has three predictors, and you detect multicollinearity. Explain how you would analyze and address the issue.
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Consider the impact of correlated variables on model interpretation.

5 more questions in this section. Enroll free to practise them all.