## Stata Markdown

Let us read the fuel efficiency data that ships with Stata

```. sysuse auto, clear
(1978 Automobile Data)
```

To study how fuel efficiency depends on weight it is useful to transform the dependent variable from "miles per gallon" to "gallons per 100 miles"

```. gen gphm = 100/mpg
```

We then obtain a fairly linear relationship

```. twoway scatter gphm weight || lfit gphm weight,  ///
>     ytitle(Gallons per 100 Miles) legend(off)

. graph export auto.png, width(500) replace
(file auto.png written in PNG format)
```

The regression equation estimated by OLS is

```. regress gphm weight

Source │       SS           df       MS      Number of obs   =        74
─────────────┼──────────────────────────────────   F(1, 72)        =    194.71
Model │  87.2964969         1  87.2964969   Prob > F        =    0.0000
Residual │  32.2797639        72  .448330054   R-squared       =    0.7300
─────────────┼──────────────────────────────────   Adj R-squared   =    0.7263
Total │  119.576261        73  1.63803097   Root MSE        =    .66957

─────────────┬────────────────────────────────────────────────────────────────
gphm │      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
─────────────┼────────────────────────────────────────────────────────────────
weight │    .001407   .0001008    13.95   0.000      .001206    .0016081
_cons │   .7707669   .3142571     2.45   0.017     .1443069    1.397227
─────────────┴────────────────────────────────────────────────────────────────
```

Thus, a car that weighs 1,000 pounds more than another requires on average an extra 1.4 gallons to travel 100 miles.

That's all for now!