# Residual Plots - PowerPoint PPT Presentation

1 / 7

Residual Plots. 15.3. Residuals. The difference between actual data points and ones predicted by the regression line (line of best fit). If the residuals are small, the model is good. Predicted Value. Residual. Observed Value. Finding Residuals. From the graph.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

Residual Plots

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

## Residual Plots

15.3

### Residuals

• The difference between actual data points and ones predicted by the regression line (line of best fit).

• If the residuals are small, the model is good.

Predicted Value

Residual

Observed Value

### Finding Residuals

From the graph

From the equation (predicted outcomes)

### Residual Plots

• Graph the x-value and the residual value to see how good our regression line models the data.

• Look for 3 things for the model to be appropriate

• Equal number of points above the x-axis and below

• No obvious patterns (linear, exponential)

• Small values for the residuals (y-axis)

### What do these Residual Plots tell us?

• All of these show residual plots for bad models. What is the biggest problem with each of the graphs?

The top two graphs show residuals that are too large (too high on the y-axis)

The bottom two graphs show patterns which mean we shouldn’t use a linear model

### Residual Plot

• Does the Residual Plot indicate that our model (regression line) is a good predictor for this data? Explain.

• YES

• equal number of points above & below the x-axis

• no pattern

• all residuals ≤