Best-fit linear regression model

Definition

A best-fit linear regression model estimates the relationship between a dependent variable and one or more independent variables using a straight line. It minimizes the sum of the squared differences between observed and predicted values to provide the most accurate predictions possible.

5 Must Know Facts For Your Next Test

1. The best-fit linear regression line is determined using the least squares method.
2. In finance, it can be used to predict stock prices based on historical data.
3. The slope of the line represents the rate of change in the dependent variable for every unit change in the independent variable.
4. The coefficient of determination (R^2) indicates how well the model explains variability in the dependent variable.
5. Outliers can significantly affect the accuracy and reliability of a best-fit linear regression model.

Review Questions

• What method is used to determine the best-fit linear regression line?
• How can a best-fit linear regression model be useful in predicting financial outcomes?
• What does a high R^2 value indicate about a best-fit linear regression model?

Related terms

Least Squares Method: A mathematical procedure used to find the best-fitting line by minimizing the sum of squared differences between observed and predicted values.

Coefficient of Determination (R^2): A statistical measure that indicates how well data points fit a statistical model, ranging from 0 to 1.

Independent Variable: A variable that is manipulated or controlled in order to observe its effect on a dependent variable.