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Participate in this quiz to evaluate your understanding of Regression, a fundamental technique in machine learning used for predictive modeling and to understand the influence of different independent variables on the dependent variable.

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Question 1 of 10

1. Question

How is the residual calculated in a linear regression model?

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Question 2 of 10

2. Question

What is a major drawback of using a large number of predictors in linear regression?

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Question 3 of 10

3. Question

In Linear Regression, the term “linear” refers to:

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Question 4 of 10

4. Question

What does the intercept in a linear regression model represent?

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Question 5 of 10

5. Question

What does a 95% confidence interval for a regression coefficient in linear regression indicate?

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Question 6 of 10

6. Question

What is an interaction term in linear regression?

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Question 7 of 10

7. Question

What is the main difference between collinearity and multicollinearity in the context of regression analysis?

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Question 8 of 10

8. Question

Which metric(s) are commonly used in linear regression to determine if there is a relationship between the dependent variable and independent variables?

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Question 9 of 10

9. Question

In linear regression, which metrics are most commonly used to determine the strength of the relationship between the dependent variable and independent variables?

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Question 10 of 10

10. Question

In linear regression, which metric is commonly used to determine the accuracy of the model’s predictions?