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  • Thursday, February 25, 2016
  • 3:40 PM–4:30 PM
  • North Hall 276

Laura Kapitula (Grand Valley State University)

Linear regression (method of least squares) can be used to model a continuous outcome variable and logistic regression can be used to model a binary outcome variable.  However, there are many situations that do not fall into one of those two categories. In many instances an outcome of interest may have a large number of zero outcomes and a group of nonzero outcomes that are discrete or highly skewed. For example, in modeling health care costs, some patients have zero costs, and the distribution of positive costs are often extremely right-skewed. When modeling charitable donations, many potential donors give nothing, and the majority of donations are relatively small with a few very large donors. In the analysis of count data, there are also times where there are more zeros than would be expected using standard methodology, or cases where the zeros might differ substantially than the non-zeros, such as number of cavities a patient has at a dentist appointment or number of children born to a mother. The two-stage regression model gives us a flexible and useful modeling framework that can be used for these type problems and it will be introduced and illustrated in this applied statistics talk.

Refreshments precede the talk at 3:30pm in North Hall 282.

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