Category: Logistic regression. The logistic regression model provides a framework for quantitative predictions of an outcome variable that is categorical, using one or more predictor variables. Articles are arranged by date with the most recent entries at the top. You can find the theme and closely related categories and other resources at the bottom of this page.

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.

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This webpage was written by Steve Simon on 2007-06-26, edited by Steve Simon, and was last modified on 2008-07-08. Send feedback to ssimon at cmh dot edu or click on the email link at the top of the page.