Q. In which scenario would you prefer linear regression over other algorithms?
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A.
When the relationship between variables is non-linear
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B.
When you need to classify data into categories
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C.
When you want to predict a continuous outcome with a linear relationship
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D.
When the dataset is very small
Solution
Linear regression is preferred when predicting a continuous outcome variable that has a linear relationship with the independent variables.
Correct Answer:
C
— When you want to predict a continuous outcome with a linear relationship
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Q. In which scenario would you prefer using a linear regression model?
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A.
When the outcome variable is categorical
-
B.
When the relationship between variables is non-linear
-
C.
When you need to predict a continuous variable based on other continuous variables
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D.
When you have a small dataset
Solution
Linear regression is preferred when predicting a continuous outcome variable based on one or more continuous predictor variables.
Correct Answer:
C
— When you need to predict a continuous variable based on other continuous variables
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Q. What does R-squared measure in a linear regression model?
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A.
The strength of the relationship between the independent and dependent variables
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B.
The average error of the predictions
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C.
The number of predictors in the model
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D.
The slope of the regression line
Solution
R-squared measures the proportion of variance in the dependent variable that can be explained by the independent variable(s) in the model.
Correct Answer:
A
— The strength of the relationship between the independent and dependent variables
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Q. What is the purpose of using a training and test set in linear regression?
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A.
To increase the size of the dataset
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B.
To validate the model's performance on unseen data
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C.
To reduce the complexity of the model
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D.
To improve the accuracy of predictions
Solution
Using a training and test set allows for the evaluation of the model's performance on unseen data, helping to prevent overfitting.
Correct Answer:
B
— To validate the model's performance on unseen data
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