Linear Regression Model
Definition
A linear regression model is a statistical approach used to model and analyze relationships between two variables, where one variable (dependent variable) can be predicted based on another variable (independent variable). It assumes that there exists a linear relationship between these variables.
Analogy
Think of linear regression as fitting puzzle pieces together. You have two sets of puzzle pieces representing your independent and dependent variables, and you're trying to find how they fit together in a straight line.
Related terms
Slope: In linear regression, slope refers to how steep or flat the line connecting data points is. It represents the change in dependent variable for each unit increase in independent variable.
Residuals: Residuals are differences between observed values and predicted values obtained from a linear regression model. They indicate how well or poorly our model fits the data points.
Coefficient of Determination (R-squared): R-squared measures the proportion of variation in the dependent variable that can be explained by the independent variable(s). It ranges from 0 to 1, where higher values indicate a better fit.
"Linear Regression Model" appears in:
Study guides (2)
Practice Questions (5)
- How can we justify a claim about a linear regression model using a confidence interval?
- What is the interpretation of a 90% confidence interval for the slope of a linear regression model?
- If a confidence interval for the slope of a linear regression model is (-1.8, -0.2), what does this imply about the correlation between the variables?
- What is the distribution of the slope estimate when the assumptions of a linear regression model are satisfied and the null hypothesis is true?
- What do negative residuals indicate in the linear regression model?
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