A Primer on Interpreting Regression Models

We perceive a need for more complete interpretation of regression models published in the wildlife literature to minimize the appearance of poor models and to maximize the extraction of information from good models. Accordingly, we offer this primer on interpretation of parameters in single‐ and multi‐variable regression models. Using examples from the wildlife literature, we illustrate how to interpret linear zero‐intercept, simple linear, semi‐log, log‐log, and polynomial models based on intercepts, coefficients, and shapes of relationships. We show how intercepts and coefficients have biological and management interpretations. We examine multiple linear regression models and show how to use the signs (+, ‐) of coefficients to assess the merit and meaning of a derived model. We discuss 3 methods of viewing the output of 3‐dimensional models ( y , x 1 , x 2 ) in 2‐dimensional space (sheet of paper) and illustrate graphical model interpretation with a 4‐dimensional logistic regression model. Statistical significance or Akaike best‐ness does not prevent the appearance of implausible regression models. We recommend that members of the peer review process be sensitive to full interpretation of regression models to forestall bad models and maximize information retrieval from good models