This page shows an example of probit regression analysis with footnotes
explaining the output in Stata. The data in this example were gathered on
undergraduates applying to graduate school and includes undergraduate GPAs, the
reputation of the school of the undergraduate (a topnotch indicator), the
students’ GRE score, and whether or not the student was admitted to graduate
school. Using this dataset, we can predict admission to graduate school
using undergraduate GPA, GRE scores, and the reputation of the school of the
undergraduate.  Our outcome variable is binary, and we will use a probit
model.  Thus, our model will calculate a predicted probability of admission
based on our predictors. The probit model does so using the cumulative distribution
function of the standard
normal.

First, let us examine the dataset and our response variable. Our binary
outcome variable must be coded with zeros and ones, so we will check this before
proceeding.

use https://stats.idre.ucla.edu/stat/stata/dae/logit.dta, clear

summarize

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       admit |       400       .3175    .4660867          0          1
         gre |       400       587.7    115.5165        220        800
    topnotch |       400       .1625    .3693709          0          1
         gpa |       400      3.3899    .3805668       2.26          4

tabulate admit

      admit |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        273       68.25       68.25
          1 |        127       31.75      100.00
------------+-----------------------------------
      Total |        400      100.00

To run the model in Stata, we first give the response variable (admit),
followed by our predictors (gre, topnotch and gpa).

probit admit gre topnotch gpa

Iteration 0:   log likelihood = -249.98826
Iteration 1:   log likelihood = -238.97735
Iteration 2:   log likelihood = -238.94339
Iteration 3:   log likelihood = -238.94339

Probit regression                                 Number of obs   =        400
                                                  LR chi2(3)      =      22.09
                                                  Prob > chi2     =     0.0001
Log likelihood = -238.94339                       Pseudo R2       =     0.0442

------------------------------------------------------------------------------
       admit |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         gre |   .0015244   .0006382     2.39   0.017     .0002736    .0027752
    topnotch |   .2730334   .1795984     1.52   0.128     -.078973    .6250398
         gpa |   .4009853   .1931077     2.08   0.038     .0225012    .7794694
       _cons |  -2.797884   .6475363    -4.32   0.000    -4.067032   -1.528736
------------------------------------------------------------------------------

Iteration History^a

Iteration 0:   log likelihood = -249.98826
Iteration 1:   log likelihood = -238.97735
Iteration 2:   log likelihood = -238.94339
Iteration 3:   log likelihood = -238.94339

a. Iteration History – This is a listing of the log likelihoods at
each iteration for the probit model. Remember that probit regression uses maximum likelihood
estimation, which is an iterative procedure. The first iteration (called
Iteration 0) is the log likelihood of the “null” or “empty” model; that is, a
model with no predictors.  At the next iteration (called Iteration 1), the
specified predictors are included in the model. In
this example, the predictors are gre, topnotch and gre.  At each iteration, the log likelihood
increases because the goal is to maximize the log likelihood. When the
difference between successive iterations is very small, the model is said to
have “converged” and the iterating stops. For more
information on this process for binary outcomes, see

Regression Models for Categorical and Limited Dependent Variables by J.
Scott Long (page 52-61).

Model Summary

Probit regression                                 Number of obsc   =        400
                                                  LR chi2(3)d      =      22.09
                                                  Prob > chi2e     =     0.0001
Log likelihood = -238.94339b                      Pseudo R2f       =     0.0442

b. Log likelihood – This is the log likelihood of the fitted model. It
is used in the Likelihood Ratio Chi-Square test of whether all predictors’
regression coefficients in the model are simultaneously zero.

c. Number of obs – This is the number of observations in the dataset
for which all of the response and predictor variables are non-missing.

d. LR chi2(3) – This is the Likelihood Ratio (LR) Chi-Square test that at least one of the predictors’ regression
coefficient is not equal to zero. The number in the parentheses indicates the
degrees of freedom of the Chi-Square distribution used to test the LR Chi-Square
statistic and is defined by the number of predictors in the model (3).

e. Prob > chi2 – This is the probability of getting a LR test
statistic as extreme as, or more so, than the observed statistic under the null
hypothesis; the null hypothesis is that all of the regression coefficients
are simultaneously equal to zero. In other words, this is the
probability of obtaining this chi-square statistic (22.09) or one more extreme if there is in fact
no effect of the predictor variables. This p-value is compared to a specified
alpha level, our willingness to accept a type I error, which is typically set at
0.05 or 0.01. The small p-value from the LR test, 0.0001, would lead us to
conclude that at least one of the regression coefficients in the model is not
equal to zero. The parameter of the chi-square distribution used to test the
null hypothesis is defined by the degrees of freedom in the prior line,
chi2(3).

f. Pseudo R2 – This is McFadden’s pseudo R-squared. Probit
regression does not have an equivalent to the R-squared that is found in OLS
regression; however, many people have tried to come up with one.  There are a
wide variety of pseudo-R-square statistics.  Because this statistic does not
mean what R-square means in OLS regression (the proportion of variance of the
response variable explained by the predictors), we suggest interpreting this
statistic with great caution. For more information on pseudo R-squareds, see
What are Pseudo R-Squareds?.

Parameter Estimates

------------------------------------------------------------------------------
       admitg|     Coef.h   Std. Err.i      zj  P>|z|k    [95% Conf. Interval]l
-------------+----------------------------------------------------------------
         gre |   .0015244   .0006382     2.39   0.017     .0002736    .0027752
    topnotch |   .2730334   .1795984     1.52   0.128     -.078973    .6250398
         gpa |   .4009853   .1931077     2.08   0.038     .0225012    .7794694
       _cons |  -2.797884   .6475363    -4.32   0.000    -4.067032   -1.528736
------------------------------------------------------------------------------

g. admit – This is the binary response variable predicted by the
model.

h. Coef. – These are the regression coefficients. The predicted
probability of admission can be calculated using these coefficients.  For a
given record, the predicted probability of admission is

where F is the cumulative distribution function of the
standard normal. However, interpretation of the coefficients in probit
regression is not as straightforward as the interpretations of coefficients in
linear regression or logit regression.  The increase in probability
attributed to a one-unit increase in a given predictor is dependent both on the
values of the other predictors and the starting value of the given predictors.
For example, if we hold gre and topnotch constant at zero, the one
unit increase in gpa from 2 to 3 has a different effect than the one unit
increase from 3 to 4 (note that the probabilities do not change by a common
difference or common factor):

and the effects of these one unit increases are different if we
hold gre and topnotch constant at their respective means instead
of zero:

However, there are limited ways in which we can interpret the
individual regression coefficients.  A positive coefficient means that an increase in the
predictor leads to an increase in the predicted probability.  A negative
coefficient means that an increase in the predictor leads to a decrease in the
predicted probability.

    gre – The coefficient of gre is 0.0015244.
This means that an increase in GRE score increases the predicted probability of
admission.

topnotch – The coefficient of topnotch is
0.2730334. This means attending a top notch institution as an undergraduate
increases the predicted probability of admission.

gpa – The coefficient of gpa is 0.4009853.
This means that an increase in GPA increases the predicted probability of
admission.

_cons – The constant term is -2.797884.  This
means that if all of the predictors (gre, topnotch and gpa) are evaluated at
zero, the predicted probability of admission is F(-2.797884) = 0.002571929. So,
as expected, the predicted probability of a student with a GRE score of zero and
a GPA of zero from a non-topnotch school has an extremely low predicted
probability of admission.

To generate values from F in Stata, use the
normal function. For example,

display normal(0) 

will display .5, indicating that
F(0) = .5 (i.e., half of the area under the standard normal distribution
curve falls to the left of zero). The first student in our dataset has a GRE
score of 380, a GPA of 3.61, and a topnotch indicator value of 0. We could
multiply these values by their corresponding coefficients,

display -2.797884 +(.0015244*380) + (.2730334*0) + (.4009853*3.61)

to determine that the
predicted probability of admittance is F(-0.77105507). To find this
value, we type

display normal(-0.77105507)

and arrive at a predicted probability of 0.22033715.

i. Std. Err. – These are the standard errors of the individual
regression coefficients. They are used in both the calculation of the z test statistic, superscript
j, and the
confidence interval of the regression coefficient, superscript l.

j. z – The test statistic z is the ratio of the Coef. to the Std. Err. of the respective predictor. The z value follows a standard normal distribution which is used to test against a two-sided alternative hypothesis that the
Coef. is not equal to zero.

k. P>|z| – This is the probability the z test statistic (or a more extreme test statistic) would be observed under the null hypothesis
that a particular predictor’s regression coefficient is zero, given that the
rest of the predictors are in the model. For a given alpha level, P>|z| determines whether or not the null hypothesis
can be rejected. If P>|z|
is less than alpha, then the null hypothesis can be rejected and the parameter
estimate is considered statistically significant at that alpha level.

gre –  The z test
statistic for the predictor gre is (0.0015244/0.0006382) = 2.39 with an
associated p-value of 0.017. If we set our alpha level to 0.05, we would
reject the null hypothesis and conclude that the regression coefficient for
gre has been
found to be statistically different from zero given topnotch and gpa
are in the
model.

topnotch –
The z test
statistic for the predictor topnotch is (0.2730334/0.1795984) = 1.52 with an
associated p-value of 0.128. If we set our alpha level to 0.05, we would
fail to reject the null hypothesis and conclude that the regression coefficient for
topnotch has not been
found to be statistically different from zero given gre and gpa
are in the
model.

gpa –
The z test
statistic for the predictor gpa is (0.4009853/0.1931077) = 2.08 with an
associated p-value of 0.038. If we set our alpha level to 0.05, we would
reject the null hypothesis and conclude that the regression coefficient for
gpa has been
found to be statistically different from zero given gre and topnotch
are in the
model.

_cons -The z test
statistic for the intercept, _cons, is (-2.797884/0.6475363) = -4.32 with an
associated p-value of < 0.001. With an alpha level of 0.05, we would reject the
null hypothesis and conclude that _cons has been found to be
statistically different from zero given gre, topnotch and gpa are in
the model and evaluated at zero.

l. [95% Conf. Interval] – This is the Confidence Interval (CI) for an
individual coefficient given that the other predictors are in the model. For a
given predictor with a level of 95% confidence, we’d say that we are 95%
confident that the “true” coefficient lies between the lower and upper limit of
the interval.  It is calculated as the Coef. (z_α/2)*(Std.Err.),
where z_α/2 is a critical value on the standard normal distribution.
The CI is equivalent to the z test statistic: if the CI includes zero,
we’d fail to reject the null hypothesis that a particular regression coefficient
is zero given the other predictors are in the model. An advantage of a CI is
that it is illustrative; it provides a range where  the “true” parameter may
lie.