What is the main difference between forward selection and backward elimination in linear regression?
Forward selection is used for classification, while backward elimination is used for regression.
There is no difference; both techniques achieve the same outcome.
Forward selection starts with all features and removes one by one, while backward elimination starts with no features and adds one by one.
Forward selection starts with no features and adds one by one, while backward elimination starts with all features and removes one by one.
What is the method used in linear regression to estimate the model parameters that minimize the sum of squared errors?
Method of Moments
Least Squares Estimation
Maximum Likelihood Estimation
Bayesian Estimation
Can the R-squared value be negative?
Yes, if there is a perfect negative correlation between the variables.
Yes, if the model fits the data worse than a horizontal line.
No, it is always positive.
No, it always ranges between 0 and 1.
If the coefficient of determination (R-squared) for a linear regression model is 0.8, what does this indicate?
80% of the variation in the dependent variable is explained by the independent variable.
20% of the variation in the dependent variable is explained by the independent variable.
There is a weak relationship between the independent and dependent variables.
The model is a poor fit for the data.
Which of these methods can be used to address heteroscedasticity?
Transforming the dependent variable
Adding more independent variables
Removing outliers
All of the above
A positive coefficient of the independent variable in a simple linear regression model indicates what?
As the independent variable increases, the dependent variable tends to decrease.
As the independent variable increases, the dependent variable tends to increase.
There is no relationship between the independent and dependent variables.
The independent variable has no impact on the dependent variable.
What does a pattern in the residual plot suggest?
The linear model is a good fit for the data.
The linear model is not a good fit for the data, and a non-linear model may be more appropriate.
The residuals are normally distributed.
What graphical tool is commonly used to visualize the relationship between two continuous variables in linear regression?
Bar chart
Scatter plot
Histogram
Pie chart
Feature selection in linear regression primarily aims to:
Make the model more complex and harder to interpret
Increase the number of features used for prediction
Improve model performance and generalization by focusing on the most relevant predictors
Ensure that all features have a statistically significant p-value
What is the ideal shape of a residual plot for a well-fitted linear regression model?
A U-shape.
An inverted U-shape.
A straight line.
Random scatter with no discernible pattern.