The **avplot** command plots an added-variable plot of the dependent variable and one independent variable from a multiple regression. An added-variable plot is sometimes also known as a partial-regression leverage plot, a partial regression plot, or an adjusted partial residual plot.

When you have a simple linear regression (one independent variable only), you are able to graph both variables (dependent and independent) with the regression line all in one graph. This gives a reasonable indication of the relationship between dependent and independent variables.

For a multiple regression this is more complicated. The relationship between the dependent variable and independent variable *A* will impact the relationship between the dependent variable and independent variable *B*. This makes it more difficult to observe the exact relationship between variables. The **avplot** command generates a graph that shows the relationship between the dependent variable and independent variable *A* while holding all other variables constant. It is an indication of the true relationship between variables in the model.

The slope of an added-variable plot indicates the amount of influence an independent variable is having on the dependent variable. These plots are also useful for identifying outliers. The **avplot** command will plot a single independent variable of your choosing. It is worth noting that you can specify an independent variable that is not yet in the model using **avplot**, so you can see whether it would be a good addition to your model. The **avplots** command will plot all added-variable plots (one for each independent variable in the model) in one graph image.

For this example I am using the Stata auto example dataset. I will run a multiple linear regression and then use the **avplots** command to look at the relationships between each independent variable and the dependent variable. In the command pane I type the following:

```
sysuse auto, clear
regress weight length displacement trunk turn
avplots
```

This produces the following composite graph:

From these plots I can determine that the independent variable *length* has the strongest linear relationship with the dependent variable *weight*. The independent variable *displacement* also appears to have a linear relationship with *weight*, but it is not as strong. Both independent variables *trunk* and *turn* do not appear to have any linear relationship with *weight* in this model.

Let’s take a closer look at the **avplot** for *displacement*, as the linear relationship we see here is questionable. In the command pane:

```
avplot displacement
```

This generates the same *weight* v *displacement* plot shown in the composite above, but it is bigger and easier to evaluate visually.

There are some issues that you can see with this graph. The linear line-of-best-fit appears to rely heavily on only a few plot points in the right side of the graph to get its linear fit. There are many more plot points on the left side of the graph but these appear more scattered and unrelated. I’d say that there are outliers here that are influencing the graph too much for the relationship to be considered properly linear.

From these **avplots** I can determine that only *weight* and *length* have a good linear relationship in this regression.

There is one additional use for the **avplot** command. It can be used on other variables in your dataset that you did not include in your model, to evaluate whether that variable would be a good addition to your model. Let’s see whether the *mpg* variable would benefit our model. In the command pane:

```
avplot mpg
```

This generates the following graph:

There appears to be a small linear relationship here. This relationship is a better fit than either *trunk* or *turn*, so perhaps *mpg* would be a good substitute for those two variables.

The **avplot** and **avplots** commands are a useful way of visually evaluating the relationships in multiple linear regression models. They can give a better indication of the relationships between variables than just using the **regress** p-values. A p-value will tell you whether or not a relationship is statistically significant, but it cannot tell you what that relationship looks like. The **avplot** for the *mpg* variable above is a good example.

If the *mpg* variable is added to the regression above and both *trunk* and *turn* are removed, the p-value given for *mpg* is 0.031 - which is statistically significant. However, we can see from the **avplot** above that although the relationship is statistically significant, it is also only having a small practical effect on the dependent variable *weight*. If the *mpg* is consistently impacting the weight of the vehicle, but the impact is only 1 ounce of weight per 5 mile change in mpg (miles per gallon), then mpg is not having any practical effect on the weight of cars when they weigh between 1700 and 4900 pounds.

Something that is statistically significant is not always practically significant. The **avplot** and **avplots** commands can help to illustrate the practical effectiveness of relationships.