If the coefficient of determination (R-squared) for a linear regression model is 0.8, what does this indicate?
20% of the variation in the dependent variable is explained by the independent variable.
The model is a poor fit for the data.
80% of the variation in the dependent variable is explained by the independent variable.
There is a weak relationship between the independent and dependent variables.
What distinguishes simple linear regression from multiple linear regression?
There is no difference; the terms are interchangeable.
Simple linear regression uses a curved line, while multiple linear regression uses a straight line.
Simple linear regression analyzes categorical data, while multiple linear regression analyzes numerical data.
Simple linear regression has one independent variable, while multiple linear regression has two or more.
A positive coefficient of the independent variable in a simple linear regression model indicates what?
The independent variable has no impact on the dependent variable.
There is no relationship between the independent and dependent variables.
As the independent variable increases, the dependent variable tends to increase.
As the independent variable increases, the dependent variable tends to decrease.
What does a pattern in the residual plot suggest?
The linear model is not a good fit for the data, and a non-linear model may be more appropriate.
The linear model is a good fit for the data.
The residuals are normally distributed.
Which matplotlib function is commonly used to plot the regression line along with the scatter plot of the data?
scatter()
plot()
show()
hist()
What does a correlation coefficient of 0 indicate?
A perfect linear relationship
A strong negative linear relationship
No linear relationship
A strong positive linear relationship
What is a potential drawback of using a purely automated feature selection technique (like forward selection or backward elimination) without careful consideration?
It can sometimes overlook features that might be important in combination with others.
It completely eliminates the need for domain expertise in model building.
It can lead to models that are less accurate than using all available features.
It guarantees the most interpretable model.
Who is credited with developing the foundational principles of linear regression?
Marie Curie
Sir Francis Galton
Albert Einstein
Isaac Newton
Which method in pandas is used to read a CSV file containing the dataset for Linear Regression?
from_csv()
loadtxt()
load()
read_csv()
Which of these methods can be used to address heteroscedasticity?
Removing outliers
Adding more independent variables
All of the above
Transforming the dependent variable