. 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) ------------------------------------------------------------------------------- 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 ------------------------------------------------------------------------------- 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 ------------------------------------------------------------------------------ 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 ------------------------------------------------------------------------------