What does the R-squared value indicate in a linear regression model?

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Question: What does the R-squared value indicate in a linear regression model?

Options:

Correct Answer: The proportion of variance explained by the model

Solution:

R-squared indicates the proportion of variance in the dependent variable that can be explained by the independent variables.

What does the R-squared value indicate in a linear regression model?

What does the R-squared value indicate in a linear regression model?

Step 1: Understand that in a linear regression model, we have two types of variables: dependent (the one we want to predict) and independent (the one we use for prediction).
Step 2: R-squared is a number that helps us understand how well our independent variables explain the changes in the dependent variable.
Step 3: R-squared values range from 0 to 1. A value of 0 means the independent variables do not explain any of the variance in the dependent variable.
Step 4: A value of 1 means the independent variables explain all the variance in the dependent variable.
Step 5: For example, if R-squared is 0.8, it means 80% of the changes in the dependent variable can be explained by the independent variables.

R-squared Value – R-squared measures how well the independent variables explain the variability of the dependent variable in a linear regression model.