What is the goal of linear regression?
Machine Learning Regression: Linear Regression

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Computers
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Professional Development
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Medium
afizan azman
Used 32+ times
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15 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
To analyze time series data
To predict categorical outcomes
To calculate the mean of the variables
To model the relationship between variables using a linear equation.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Explain the difference between simple linear regression and multiple linear regression.
Simple linear regression is more accurate than multiple linear regression.
In simple linear regression, there are two or more independent variables, whereas in multiple linear regression, there is only one independent variable.
In simple linear regression, there is only one independent variable, whereas in multiple linear regression, there are two or more independent variables.
Simple linear regression is used for categorical data, while multiple linear regression is used for continuous data.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the formula for a simple linear regression model?
y = ax + b
y = mx - b
y = cx + m
y = mx + c
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How is the best-fit line determined in linear regression?
By minimizing the sum of squared differences between observed values and predicted values using the method of least squares.
By flipping a coin
By choosing the highest value
By asking a magic eight ball
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 100.
R-squared is a value between -1 and 1.
R-squared is a value between 0 and 1.
R-squared is a value between 0 and 2.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of the cost function in linear regression?
To calculate the accuracy of the model
To measure the performance of the model by calculating the difference between predicted and actual values.
To determine the number of features in the dataset
To select the learning rate for gradient descent
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What are the assumptions of linear regression?
Linearity, Independence, Homoscedasticity, Skewness of residuals
Linearity, Dependence, Heteroscedasticity, Normality of residuals
Non-linearity, Independence, Homoscedasticity, Normality of residuals
Linearity, Independence, Homoscedasticity, Normality of residuals
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