Practical Guide to Logistic Regression

In Changepower! 37 Secrets to Habit Change Success, author Meg Selig guides readers through a step-by-step process that will help them achieve any habit change goal. Whether the reader wants to break a hurtful habit like smoking or overeating, or build a healthy habit like exercising or speaking up, Changepower! provides a springboard for change.Selig helps habit-changers move beyond willpower and succeed with changepower - the synergy that comes from combining willpower with other resources, useful outside supports, and wise strategies. In Changepower!, she shows habit-changers how to beef up both their willpower and their changepower to achieve habit change success. The key is revving up motivation. Selig reveals the most powerful motivators for change - pain motivators, the Eight Great Motivators, and even not-so-noble motivators.Research has shown that most changes take place in stages rather than overnight. Selig provides a step-by-step plan for each stage, leaving plenty of room for flexibility depending on each person‘s needs. First-person stories, pithy quotes, and how-to exercises provide inspiration, humor, and encouragement as readers embark on their habit change journeys.

Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields, including medical and health outcomes research, business analytics and data science, ecology, fisheries, astronomy, transportation, insurance, economics, recreation, and sports. By harnessing the capabilities of the logistic model, analysts can better understand their data, make appropriate predictions and classifications, and determine the odds of one value of a predictor compared to another.Drawing on his many years of teaching logistic regression, using logistic-based models in research, and writing about the subject, Professor Hilbe focuses on the most important features of the logistic model. Serving as a guide between the author and readers, the book explains how to construct a logistic model, interpret coefficients and odds ratios, predict probabilities and their standard errors based on the model, and evaluate the model as to its fit. Using a variety of real data examples, mostly from health outcomes, the author offers a basic step-by-step guide to developing and interpreting observation and grouped logistic models as well as penalized and exact logistic regression. He also gives a step-by-step guide to modeling Bayesian logistic regression.R statistical software is used throughout the book to display the statistical models while SAS and Stata codes for all examples are included at the end of each chapter. The example code can be adapted to readers own analyses. All the code is available on the author‘s website.