
Regression Basics
Authored by Vici Handalusia
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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main goal of linear regression?
To model the relationship between variables using a linear equation.
To calculate the mean of the variables
To analyze time series data
To predict categorical outcomes
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Explain the difference between simple linear regression and multiple linear regression.
The difference lies in the type of dependent variable used
The main difference is the number of predictor variables used in the regression model.
Simple linear regression uses more predictor variables than multiple linear regression
The main difference is the type of data used in the regression models
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the formula for a straight line in linear regression?
y = mx / b
y = mx - b
y = mx * b
y = mx + b
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How is the best-fit line determined in linear regression?
By minimizing the sum of the squared differences between observed values and predicted values using the method of least squares.
By asking a magic eight ball for guidance
By flipping a coin and choosing the line based on heads or tails
By selecting the line that looks the most visually appealing
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the coefficient of determination (R-squared) in linear regression?
R-squared is a value between 0 and 1.
R-squared is a value between 0 and 10.
R-squared is a value between 0 and 2.
R-squared is a value between -1 and 1.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the difference between correlation and regression?
Correlation and regression are interchangeable terms used to describe the same statistical concept.
Correlation measures the strength and direction of a relationship, while regression predicts one variable based on another.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What are the assumptions of linear regression?
Linearity, Independence, Homoscedasticity, Normality of residuals
Linearity, Dependence, Heteroscedasticity, Normality of residuals
Non-linearity, Independence, Homoscedasticity, Normality of residuals
Linearity, Independence, Heteroscedasticity, Non-normality of residuals
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