# Stata: Predicted Scores and Residuals

## Predicted Scores in Stata

As we discussed in class, the predicted value of the outcome variable can be created using the regression model. For example, we can use the `auto` dataset from Stata to look at the relationship between miles per gallon and weight across various cars. We estimate the follow equation

$mpg' = b_0 + b_1weight$

``````sysuse auto
regress mpg weight
Source |       SS       df       MS              Number of obs =      74
-------------+------------------------------           F(  1,    72) =  134.62
Model |   1591.9902     1   1591.9902           Prob > F      =  0.0000
Residual |  851.469256    72  11.8259619           R-squared     =  0.6515
Total |  2443.45946    73  33.4720474           Root MSE      =  3.4389
------------------------------------------------------------------------------
mpg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
weight |  -.0060087   .0005179   -11.60   0.000    -.0070411   -.0049763
_cons |   39.44028   1.614003    24.44   0.000     36.22283    42.65774
------------------------------------------------------------------------------
``````

Thus, we see a negative relationship between `weight` and `mpg`. For every 1 unit increase in `weight`, `mpg` goes down by -.006. We can obtain the predicted scores for the observations in our dataset with the following command:

``````predict mpg_pred
``````

This creates a new variable called `mpg_pred` with the predicted `mpg` for all the `weight` values in our dataset. Here’s 20 of the actual mpg values and 20 of the predicted values.

``````list mpg mpg_pred in 1/20
+----------------+
| mpg   mpg_pred |
|----------------|
1. |  22   21.83483 |
2. |  17   19.31118 |
3. |  22   23.57735 |
4. |  20   19.91205 |
5. |  15   14.92484 |
|----------------|
6. |  18    17.3884 |
7. |  26   26.04091 |
8. |  20   19.73179 |
9. |  16   16.12658 |
10. |  19   19.01075 |
|----------------|
11. |  14   13.42267 |
12. |  14    16.0064 |
13. |  21   13.66302 |
14. |  29   26.76196 |
15. |  16   17.26823 |
|----------------|
16. |  22   20.33266 |
17. |  22   20.09231 |
18. |  24    22.9164 |
19. |  19   18.83049 |
20. |  30   26.70187 |
+----------------+
``````

## Residuals in Stata

Recall the a residual in regression is defined as the difference between the actual value of $Y$ and the predicted value of $Y$ (or $Y'$):

$Y - Y'$

Thus, to compute residuals we can just subtract `mpg_pred` from `mpg`. Stata will do this for us using the predict command:

``````predict mpg_res, residuals
``````

Here’s 20 of the actual mpg values, 20 of the predicted values, and 20 of the residuals.

`````` list mpg mpg_pred mpg_res in 1/20
+----------------------------+
| mpg   mpg_pred     mpg_res |
|----------------------------|
1. |  22   21.83483    .1651688 |
2. |  17   19.31118   -2.311183 |
3. |  22   23.57735    -1.57735 |
4. |  20   19.91205    .0879486 |
5. |  15   14.92484    .0751587 |
|----------------------------|
6. |  18    17.3884    .6115971 |
7. |  26   26.04091   -.0409119 |
8. |  20   19.73179    .2682092 |
9. |  16   16.12658   -.1265787 |
10. |  19   19.01075   -.0107484 |
|----------------------------|
11. |  14   13.42267    .5773304 |
12. |  14    16.0064   -2.006405 |
13. |  21   13.66302    7.336983 |
14. |  29   26.76196    2.238046 |
15. |  16   17.26823   -1.268229 |
|----------------------------|
16. |  22   20.33266    1.667341 |
17. |  22   20.09231    1.907688 |
18. |  24    22.9164    1.083605 |
19. |  19   18.83049    .1695122 |
20. |  30   26.70187    3.298132 |
+----------------------------+
``````

We can see that the residuals are intact the difference between the first two columns.

Given that the residuals are the part of the `mpg` that is unrelated to `weight`, `mpg_res` should be uncorrelated with `weight`. Let’s check:

``````. corr weight mpg_res
(obs=74)
|   weight  mpg_res
-------------+------------------
weight |   1.0000
mpg_res |   0.0000   1.0000
``````

Magic!