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December 13, 2025
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. describe
Contains data from auto.dta
obs: 74 1978 Automobile Data
vars: 4 11 Sep 1998 10:08
size: 1,998 (99.9% of memory free)
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1. make str18 %-18s Make and Model
2. mpg int %8.0g Mileage (mpg)
3. weight int %8.0gc Weight (lbs.)
4. foreign byte %8.0g origin Car type
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Sorted by: foreign
Note: dataset has changed since last saved
. inspect foreign
foreign: Car type Number of Observations
------------------ Non-
Total Integers Integers
| # Negative - - -
| # Zero 52 52 -
| # Positive 22 22 -
| # ----- ----- -----
| # # Total 74 74 -
| # # Missing -
+---------------------- -----
0 1 74
(2 unique values)
foreign is labeled and all values are documented in the label.
The variable foreign takes on two unique values, 0 and 1. The value 0 denotes a domestic car and 1 denotes a foreign car.
The model you wish to estimate is
Pr(foreign = 1) = F(B_0 + B_1 weight + B_2 mpg)
where F(z) = e^z/(1+e^z) is the cumulative logistic distribution.
To estimate this model, you type
. logit foreign weight mpg
Iteration 0: log likelihood = -45.03321
Iteration 1: log likelihood = -29.898968
Iteration 2: log likelihood = -27.495771
Iteration 3: log likelihood = -27.184006
Iteration 4: log likelihood = -27.175166
Iteration 5: log likelihood = -27.175156
Logit estimates Number of obs = 74
LR chi2(2) = 35.72
Prob > chi2 = 0.0000
Log likelihood = -27.175156 Pseudo R2 = 0.3966
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foreign | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------+--------------------------------------------------------------------
weight | -.0039067 .0010116 -3.862 0.000 -.0058894 -.001924
mpg | -.1685869 .0919174 -1.834 0.067 -.3487418 .011568
_cons | 13.70837 4.518707 3.034 0.002 4.851864 22.56487
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