Stats: Testing for an increasing trend
in a proportion (November 26, 2007). Someone asked me how to see if a sequence of four proportions is showing a
significant increase over time. The data represents the proportion of imaging
studies that are requested by a primary care physician (pcp), as opposed to
studies ordered by a specialist.
Stats: Interpretation
of an odds ratio (March 21, 2007). Someone sent me some data on crime. In
a sample of 2,957,239 people, 961 were criminals. 41 of the criminals were in
the first group (who numbered 20,109). The remaining 920 were in the larger
group (2,937,130). This person computed an odds ratio of 6.5 and wondered
what it meant.
Stats: Differences
between the Chi-square test, Fisher's Exact test, and logistic regression
(January 9, 2007). I received an email from India (isn't the Internet
wonderful?) that asked me to comment on the differences between a Chi-square
test, Fisher's Exact test, and logistic regression. Let's take each of these
in sequence.
Stats: Checking a Chi-square
test (February 13, 2006). Someone preparing a critique of a research
article wanted to check the accuracy of the statistics in that article. They
noted that in a group of 37 patients without the intervention, only one was
successful in avoiding a certain type of risky behavior. In a group with
counseling, 7 out of 44 avoided the risky behavior.
Stats: Continuous variables
in a logistic regression model (February 9, 2005). I got a question by
email that asked, in a rather indirect way, how to interpret the odds ratio
estimate for a continuous variable in a logistic regression model. It turns
out that the odds ratio represents a change in the estimated odds of the
outcome when the continuous variable increases by one unit.
Stats: Categorical variables in a logistic regression model (June 1, 2004).
On April 8, I had written a brief description of interactions in a
logistic regression model. This was a supplement to a discussion of the
concepts behind the logistic regression model. Another important topic in
that series of explanations is the interpretation of logistic regression
coefficients for categorical variables.
Stats: Interactions in logistic
regression (April 8, 2004). Someone asked me how to compute interactions
in binary logistic regression. You need to be careful, since interactions are
tricky to interpret.
Stats: The concepts behind the
logistic regression model (July 23, 2002). The logistic regression model
is a model that uses a binary (two possible values) outcome variable.
Examples of a binary variable are mortality (live/dead), and morbidity
(healthy/diseased). Sometimes you might take a continuous outcome and convert
it into a binary outcome. For example, you might be interested in the length
of stay in the hospital for mothers during an unremarkable delivery. A binary
outcome might compare mothers who were discharged within 48 hours versus
mothers discharged more than 48 hours.
Stats: SPSS dialog boxes for logistic
regression (July 22, 2002). This handout shows some of the dialog boxes
that you are likely to encounter if you use logistic regression models in
SPSS.
Stats: Fisher's Exact Test (August 23, 2000).
Dear Professor Mean: What is Fisher's Exact Test and when should I use it?
Stats: Guidelines for logistic regression
models (September 27, 1999). There are three steps in a typical logistic
regression model: 1. Fit a crude model; 2. Fit an adjusted model; 3. Examine
the predicted probabilities.
Theme and closely related categories:
Other resources:
This webpage was written on 2007-06-26 and was last modified on
2008-07-08.
