Linear Regression
Definition
Linear regression is a statistical method used to model the relationship between two variables by fitting a linear equation to observed data. It helps us understand how changes in one variable are associated with changes in another variable.
Analogy
Think of linear regression as finding the best-fitting line through a scatterplot of points. Just like drawing a straight line that passes through most of the points, linear regression finds the line that best represents the overall trend in the data.
Related terms
Y-intercept: The y-intercept is the point where the regression line crosses or intersects with the y-axis. It represents the predicted value of the dependent variable when all independent variables are set to zero.
R^2 (R-squared): R^2 is a measure of how well the regression line fits the data. It represents the proportion of variation in the dependent variable that can be explained by changes in the independent variable(s).
Slope: The slope is another term used in linear regression and refers to how steep or flat the regression line is. It indicates how much change we expect in our dependent variable for each unit change in our independent variable(s).
"Linear Regression" appears in:
Additional resources (1)
Practice Questions (8)
- When should you use a Linear Regression T Test for Slopes?
- What is the goal of linear regression?
- Which variable is considered the dependent variable in linear regression?
- What is the purpose of finding the line of best fit in linear regression?
- Which statistical technique is used to model the linear relationship between variables in linear regression?
- What is the goal of linear regression in terms of residuals?
- Why do we use the least squares criterion in linear regression?
- In linear regression, what is the purpose of calculating the sum of squared residuals?
Resources
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