Q. How can you improve a linear regression model's performance?
-
A.
By adding more independent variables
-
B.
By using a more complex model like a neural network
-
C.
By transforming variables to better meet model assumptions
-
D.
By reducing the size of the dataset
Solution
Transforming variables can help meet the assumptions of linear regression and improve model performance.
Correct Answer:
C
— By transforming variables to better meet model assumptions
Learn More →
Q. In a business context, how can linear regression be applied?
-
A.
To determine customer segments
-
B.
To forecast sales based on advertising spend
-
C.
To classify products into categories
-
D.
To cluster similar customer behaviors
Solution
Linear regression can be used to forecast sales based on advertising spend, predicting continuous outcomes.
Correct Answer:
B
— To forecast sales based on advertising spend
Learn More →
Q. In a linear regression model, what does the slope of the regression line represent?
-
A.
The predicted value of the dependent variable
-
B.
The change in the dependent variable for a one-unit change in the independent variable
-
C.
The correlation between the independent and dependent variables
-
D.
The intercept of the regression line
Solution
The slope indicates how much the dependent variable is expected to increase or decrease as the independent variable increases by one unit.
Correct Answer:
B
— The change in the dependent variable for a one-unit change in the independent variable
Learn More →
Q. In which scenario would linear regression be an appropriate model to use?
-
A.
Predicting customer churn (yes/no)
-
B.
Estimating house prices based on square footage
-
C.
Classifying emails as spam or not spam
-
D.
Segmenting customers into different groups
Solution
Linear regression is suitable for estimating continuous values, such as house prices based on features like square footage.
Correct Answer:
B
— Estimating house prices based on square footage
Learn More →
Q. What is a potential consequence of using linear regression on data with outliers?
-
A.
Increased accuracy of predictions
-
B.
Decreased interpretability of the model
-
C.
Bias in the estimated coefficients
-
D.
Improved model performance
Solution
Outliers can bias the estimated coefficients in a linear regression model, leading to inaccurate predictions.
Correct Answer:
C
— Bias in the estimated coefficients
Learn More →
Q. What is the effect of multicollinearity on a linear regression model?
-
A.
It improves model accuracy
-
B.
It makes coefficient estimates unstable
-
C.
It has no effect on the model
-
D.
It simplifies the model
Solution
Multicollinearity makes coefficient estimates unstable, leading to difficulties in interpreting the model.
Correct Answer:
B
— It makes coefficient estimates unstable
Learn More →
Q. What is the primary purpose of linear regression in real-world applications?
-
A.
To classify data into categories
-
B.
To predict a continuous outcome based on input features
-
C.
To cluster similar data points
-
D.
To reduce the dimensionality of data
Solution
Linear regression is used to predict a continuous outcome based on one or more input features.
Correct Answer:
B
— To predict a continuous outcome based on input features
Learn More →
Q. Which evaluation metric is most appropriate for assessing the performance of a linear regression model?
-
A.
Accuracy
-
B.
F1 Score
-
C.
Mean Absolute Error (MAE)
-
D.
Confusion Matrix
Solution
Mean Absolute Error (MAE) is a suitable metric for evaluating the performance of a linear regression model.
Correct Answer:
C
— Mean Absolute Error (MAE)
Learn More →
Q. Which of the following is a common assumption made by linear regression?
-
A.
The relationship between variables is non-linear
-
B.
The residuals are normally distributed
-
C.
The dependent variable is categorical
-
D.
There is no multicollinearity among predictors
Solution
Linear regression assumes that there is no multicollinearity among the predictors, meaning they should not be highly correlated.
Correct Answer:
D
— There is no multicollinearity among predictors
Learn More →
Q. Which of the following is NOT a characteristic of linear regression?
-
A.
It assumes a linear relationship between variables
-
B.
It can only handle two variables
-
C.
It can be used for multiple predictors
-
D.
It minimizes the sum of squared residuals
Solution
Linear regression can handle multiple predictors, not just two variables.
Correct Answer:
B
— It can only handle two variables
Learn More →
Showing 1 to 10 of 10 (1 Pages)
