## How do you choose the best regression model?

Statistical Methods for Finding the Best Regression Model

- Adjusted R-squared and Predicted R-squared: Generally, you choose the models that have higher adjusted and predicted R-squared values.
- P-values for the predictors: In regression, low p-values indicate terms that are statistically significant.

**Is the LSRL the line of best fit?**

The Least Squares Regression Line is the line that makes the vertical distance from the data points to the regression line as small as possible. It’s called a “least squares” because the best line of fit is one that minimizes the variance (the sum of squares of the errors).

### What is the least squares line of best fit?

The least squares method is a statistical procedure to find the best fit for a set of data points by minimizing the sum of the offsets or residuals of points from the plotted curve. Least squares regression is used to predict the behavior of dependent variables.

**How do you find the slope of the line of best fit?**

The line’s slope equals the difference between points’ y-coordinates divided by the difference between their x-coordinates. Select any two points on the line of best fit. These points may or may not be actual scatter points on the graph. Subtract the first point’s y-coordinate from the second point’s y-coordinate.

## What is the difference between regression line and line of best fit?

Linear regression consists of finding the best-fitting straight line through the points. The best-fitting line is called a regression line. The black diagonal line in Figure 2 is the regression line and consists of the predicted score on Y for each possible value of X.

**Why is the regression line called the Line of Best Fit?**

The regression line is sometimes called the “line of best fit” because it is the line that fits best when drawn through the points. It is a line that minimizes the distance of the actual scores from the predicted scores.

### Why do we call a regression line a trend line?

The regression line minimizes the total sum-squared Y-error. A “trend line” may be the line that gives the smallest errors when the “error” is defined as the 2-dimensional distance from the data points to the line, NOT the y-distance from the data points to the line.

**What does a regression line tell you?**

A regression line is a straight line that de- scribes how a response variable y changes as an explanatory variable x changes. We often use a regression line to predict the value of y for a given value of x. The text gives a review of the algebra and geometry of lines on pages 117 and 118.