What distinguishes simple linear regression from multiple linear regression?
Simple linear regression uses a curved line, while multiple linear regression uses a straight line.
Simple linear regression has one independent variable, while multiple linear regression has two or more.
Simple linear regression analyzes categorical data, while multiple linear regression analyzes numerical data.
There is no difference; the terms are interchangeable.
What is the purpose of splitting the dataset into training and testing sets in Linear Regression?
To reduce the dimensionality of the data.
To visualize the relationship between variables.
To evaluate the model's performance on unseen data.
To handle missing values in the dataset.
Which of the following is NOT a benefit of feature selection in linear regression?
Improved model interpretability
Potential for better generalization to new data
Increased risk of overfitting
Reduced computational cost
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.
The model is a poor fit for the data.
There is a weak relationship between the independent and dependent variables.
What is the ideal shape of a residual plot for a well-fitted linear regression model?
A U-shape.
Random scatter with no discernible pattern.
An inverted U-shape.
A straight line.
A positive coefficient of the independent variable in a simple linear regression model indicates what?
There is no relationship between the independent and dependent variables.
As the independent variable increases, the dependent variable tends to decrease.
As the independent variable increases, the dependent variable tends to increase.
The independent variable has no impact on the dependent variable.
In forward selection, what criteria is typically used to decide which feature to add at each step?
The feature that results in the largest improvement in model performance
The feature that results in the smallest increase in R-squared
The feature with the highest p-value
The feature that is least correlated with the other features
What does the linearity assumption in linear regression imply?
The data points are evenly distributed around the regression line.
The dependent variable must have a normal distribution.
The independent variables are unrelated to each other.
The relationship between the dependent and independent variables can be best represented by a straight line.
Which of the following is NOT an assumption of linear regression?
Multicollinearity
Normality of residuals
Homoscedasticity
Linearity
What type of visualization tool is commonly used to initially assess the relationship between two continuous variables in linear regression?
Bar chart
Histogram
Pie chart
Scatter plot