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Quiz on Linear Regression Model for Multiple Variable

Quiz on Linear Regression Model for Multiple Variable

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Question 1 of 10

1. Question

What is multiple linear regression used for?

Predicting a class label
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Grouping data into clusters
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Predicting a value using more than one input feature
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Encrypting sensitive data
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Question 2 of 10

2. Question

In multiple linear regression, the target variable is:

Always binary
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Categorical
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A single continuous variable
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A group of features
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Question 3 of 10

3. Question

Which library in Python is commonly used to implement multiple linear regression?

seaborn
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sklearn
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nltk
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openpyxl
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Question 4 of 10

4. Question

What does the .fit() function do in a linear regression model?

Tests the model
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Fits the model to training data
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Displays the graph
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Removes null values
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Question 5 of 10

5. Question

If you have 3 input variables (features), the regression equation will have:

1 coefficient and 1 intercept
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3 coefficients and 1 intercept
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3 intercepts
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Only intercept, no coefficients
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Question 6 of 10

6. Question

What kind of relationship does multiple linear regression model?

Non-linear between inputs and output
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Linear between multiple inputs and one output
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Causal between two outputs
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Binary between two classes
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Question 7 of 10

7. Question

Which of the following is true about multiple linear regression?

All input variables must be binary
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It cannot handle missing values
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It finds the best-fitting plane/hyperplane
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It uses clustering techniques
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Question 8 of 10

8. Question

Which method is used to evaluate the accuracy of a regression model?

Confusion matrix
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ROC curve
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Mean Squared Error (MSE)
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Silhouette Score
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Question 9 of 10

9. Question

What is multicollinearity in multiple linear regression?

When features have different units
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When features are highly correlated with each other
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When features contain missing values
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When the model has only one variable
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Question 10 of 10

10. Question

What does model.predict(X_test) return in Scikit-learn?

Training accuracy
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Labels of clusters
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Predicted output values
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Feature importance
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