This page shows an example of a Poisson regression analysis with footnotes explaining the output. The data
collected were academic information on
316
students. The response variable is
days absent during the school year (**daysabs**), and we explore its relationship with
math standardized tests score (**mathnce**),
language standardized tests score (**langnce**)
and gender (**female**).

As assumed for a Poisson model, our response variable is a count variable, and each subject has the same length of observation time. Had the observation time for subjects varied, the Poisson model would need to be adjusted to account for the varying length of observation time per subject. This point is discussed later in the page. Also, the Poisson model, as compared to other count models (i.e., negative binomial or zero-inflated models), is assumed to be the appropriate model. In other words, we assume that the dependent variable is not over-dispersed and does not have an excessive number of zeros.

You can download the data set used in this example by clicking here .

data preg; set "C:\temp\poisson"; female = (gender = 1); run; proc genmod data = preg; model daysabs = mathnce langnce female / link=log dist=Poisson; run;The GENMOD Procedure Model Information Data Set WORK.PREG Distribution Poisson Link Function Log Dependent Variable DAYSABS number days absent Number of Observations Read 316 Number of Observations Used 316 Criteria For Assessing Goodness Of Fit Criterion DF Value Value/DF Deviance 312 2234.5462 7.1620 Scaled Deviance 312 2234.5462 7.1620 Pearson Chi-Square 312 2774.4139 8.8924 Scaled Pearson X2 312 2774.4139 8.8924 Log Likelihood 1482.2670 Algorithm converged. Analysis Of Parameter Estimates Standard Wald 95% Confidence Chi- Parameter DF Estimate Error Limits Square Pr > ChiSq Intercept 1 2.2867 0.0700 2.1496 2.4239 1068.59

### Model Information

Model Information Data Set^{a}WORK.PREG Distribution^{b}Poisson Link Function^{c}Log Dependent Variable^{d}DAYSABS number days absent Number of Observations Read^{e}316 Number of Observations Used^{e}316

a. **Data Set** – This is the SAS dataset on which the Poisson regression
was performed.

b.** Distribution** – This is the distribution of the dependent variable.
Poisson regression is a type of
generalized linear model. As such, we need to specify the distribution of
the dependent variable, **dist = Poisson**, as
well as the link function, superscript
c.

c.** Link Function** – This is the link function used for the Poisson
regression. By default, when we specify **dist = Poisson, **the log link
function is assumed (and does not need to be specified); however, for
pedagogical purposes, we include **link = log**. When we write our model out, log( μ ) = β_{0} + β_{1}x_{1} +
… + β_{p}x_{p}, where μ is the count we are
modeling, log( ) defines the link function (i.e., how we transform μ to
write it as a linear combination of the predictor variables).

d.** Dependent Variable **– This is the dependent variable used in the
Poisson regression.

e.** Number of Observations Read** and ** Number of Observations Used**
– This is the number of observations read and the number of observation used in the
Poisson
regression. The **Number of Observations Used** may be less than the **
Number of Observations Read** if there are missing values for any variables
in the equation. By default, SAS does a listwise deletion of incomplete cases.

### Criteria For Assessing Goodness Of Fit

Criteria For Assessing Goodness Of Fit Criterion^{f}DF^{g}Value^{g}Value/DF^{h}Deviance 312 2234.5462 7.1620 Scaled Deviance 312 2234.5462 7.1620 Pearson Chi-Square 312 2774.4139 8.8924 Scaled Pearson X2 312 2774.4139 8.8924 Log Likelihood 1482.2670 Algorithm converged^{i}.

Prior to discussing the **Criterion**, **DF**, **Value** and **
Value/DF**, we need to discuss the logic of this section. Attention is
placed on **Deviance** and **Scaled Deviance**; the argument naturally
extends to **Pearson Chi-Square**.

First, note that the **Deviance** has an approximate chi-square distribution with
*n-p*
degrees of freedom, where *n* is the number of observations and *p* is the
number of predictor variables (including the intercept), and the expected value of a chi-square random variable is equal
to the degrees of freedom. Then, if our model fits the data well, the ratio of the **
Deviance** to **DF**, **Value/DF**, should be about one. Large ratio
values may indicate model misspecification or an over-dispersed response variable; ratios less than one may
also indicate
model misspecification or an under-dispersed response variable. A consequence of
such dispersion issues is that standard errors are incorrectly estimated,
implying an invalid chi-square test statistic, superscript p. Importantly,
however, assuming our model is correctly specified, the Poisson regression estimates remain unbiased in the presence of over-disperion or under-dispersion.
Two “fixes” are either running the same model as a negative binomial
regression, or correcting the standard errors of the estimates. The standard error
correction corresponds to the approach for the scaled criterion. A naive
explanation when the **scale** option is specified (**scale = dscale**), the
**Scaled Deviance** is forced to equal one. By forcing **Value/DF** to one (dividing
**Value/DF** by itself), our model becomes “optimally” dispersed; however,
what actually happens is that the standard errors are adjusted ad hoc. The standard
errors are adjusted by a factor, the square root of **Value/DF**.

f. **Criterion** – Below are various measurements used to assess the
model fit.

**Deviance** – This is the deviance for the
model. The deviance is defined as two times the difference of the log
likelihood
for the maximum achievable model (i.e., each subject’s response serves as a unique
estimate of the Poisson parameter), and the log likelihood under the fitted
model.
The difference in the **Deviance** and degrees of freedom of two nested models
can be used in the likelihood ratio chi-square tests.

**Scaled Deviance** – This is the scaled deviance.
The scaled deviance is equal to the deviance since we did not
specify the **scale=dscale** option on the model statement.

**Pearson Chi-Square** – This is the Pearson chi-square
statistic. The Pearson chi-square is defined as the squared difference between
the observed and predicted values divided by the variance of the predicted value summed over
all observations in the model.

**Scaled Pearson X2** – This is the scaled Pearson
chi-square statistic.
The scaled Pearson X2 is equal to the Pearson chi-square since we
did not specify the **scale=pscale** option on the model statement.

**Log Likelihood** – This is the log likelihood of
the model. Instead of using the deviance, we can take two times the
difference between the log likelihood for nested models to perform a
chi-square test.

g. **DF** and **Value** – These are the degrees of freedom **DF**
and the respective **Value** for the **Criterion** measures. The **DF**
is equal to *n-p*, where *n* is the number of observation used and *p* is
the number of parameters estimated.

h. **Value/DF** – This is the ratio of **Value** to **DF** given
in superscript g. Refer to the discussion at the beginning of this section for an
interpretation/use of this value.

i. **Algorithm Convergered** – This is a note indicating that the algorithm for parameter estimates
has converged, implying that a solution was found.

### Analysis Of Parameter Estimates

Analysis Of Parameter Estimates Standard Wald 95% Confidence Chi- Parameter^{j}DF^{k}Estimate^{l}Error^{m}Limits^{n}Square^{o}Pr > ChiSq^{o}Intercept 1 2.2867 0.0700 2.1496 2.4239 1068.59

j. **Parameter** – Underneath are the predictor
variables and the **Scale** parameter.

k. **DF** – These are the degrees of freedom **DF** spent on each of
the respective parameter estimates. Note that the **DF** for the **Scale**
parameter is set to 0. The **DF** define the distribution used to test **
Chi-Square**, superscript o.

l. **Estimate** -These are the estimated Poisson regression coefficients for the model. Recall that the dependent variable is
a count variable, and Poisson regression models the log of the expected count
as a linear function of the predictor variables. We can interpret the Poisson
regression coefficient as follows: for a one unit change in the predictor variable, the
difference in the logs of expected counts is expected to change by the respective
regression coefficient, given the other predictor variables in the model are held
constant.

Also, note that each subject in our sample was followed for one school year.
If this was not the case (i.e., some subjects were followed for half a
year, some for a year and the rest for two years) and we were to neglect the
exposure time, our Poisson regression estimates would be biased, since our model
assumes all subjects had the same observation time. If this was an issue, we would
use the **offset** option, **offset= log_timevar**,
where

*log_timevar*corresponds to the logged version of the variable specifying length of time an individual was followed to adjust the Poisson regression estimates.

** Intercept** – This is the Poisson regression estimate
when all variables in the model are evaluated at zero. For males (the variable
**female** evaluated at zero) with zero **mathnce** and **langnce**
test scores, the log of the expected count for **daysabs** is 2.2867 units. Note that evaluating **
mathnce** and **langnce** at zero is out of the range of plausible test
scores. If the test scores were mean-centered, the intercept would have a
natural interpretation: the log of the expected count for males with average **
mathnce** and **langnce** test scores.

** mathnce** – This is the Poisson regression estimate for a one unit increase in
math standardized test score, given the other
variables are held constant in the model. If a student
were to increase her **mathnce** test score by one point, the difference in
the logs of expected counts would be expected to decrease by 0.0035 unit, while holding
the other variables in the model constant.

**langnce** – This is the Poisson regression estimate
for a one unit increase in language standardized test score, given the other
variables are held constant in the model. If a student
were to increase her **langnce** test score by one point, the difference in
the logs of expected counts would be expected to decrease by 0.0122 unit while holding
the other variables in the model constant.

**female** – This is the estimated Poisson
regression coefficient comparing females to males, given the other variables are
held constant in the model. The difference in the logs of expected counts is
expected to be 0.4010 unit higher for females compared to males, while holding
the other variables constant in the model.

**Scale** – This is the **Scale** value for the
Poisson
model. Since our model was not scaled (**Scaled Deviance** or **Scaled
Pearson X2**), the default scale for the Poisson model is set to one. This is
noted by the comment at the bottom of the output: NOTE: The scale parameter was held fixed.

m. **Standard Error** – These are the standard errors of the
individual regression coefficients. They
are used in both the **Wald 95% Confidence Limits**, superscript n, and the
**Chi-Square **test
statistic, superscript o.

n. **Wald 95% Confidence Limits** – This is the Wald Confidence Interval
(CI) of an individual Poisson regression coefficient, given the other predictors are in the
model. For a given predictor variable with a level of 95% confidence, we’d say
that we are 95% confident that upon repeated trials, 95% of the CI’s would
include the “true” population Poisson regression coefficient. It is calculated
as **Estimate** ± (z_{α/2})*(**Standard Error**), where z_{α/2}
is a critical value on the standard normal distribution. The CI is equivalent to the
**Chi-Square** 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 information on where the “true” parameter may lie
and the precision of the point estimate.

o. **Chi-Square** and **Pr > ChiSq – **These are the test statistics
and p-values, respectively, testing the null hypothesis that an individual
predictor’s regression coefficient is zero, given that the rest of the
predictors are in the model. The **Chi-Square** test statistic is the squared ratio of the **
Estimate **to the
**Standard Error** of the respective predictor. The **Chi-Square** value follows a standard
chi-square distribution with degrees of freedom given by **DF**, which is used to test against
the alternative hypothesis that the **Estimate **is not equal to zero. The probability that a particular **
Chi-Square** test statistic is as extreme as, or more
so, than what has been observed under the null hypothesis is defined by **Pr>ChiSq**.