This book is designed to help the managers and researchers in solving statistical problems using SPSS and to help them understand how they can use various statistical tools for their own research problems. SPSS is a very powerful and user friendly computer package for data analyses. It can take data from most other file-types and generate tables, charts, plots, and descriptive statistics, and conduct complex statistical analyses. This book will help students, business managers, academics as well as practicing researchers to solve statistical problems using the latest version of SPSS (16.0). After providing a brief overview of SPSS and basic statistical concepts, the book covers: Descriptive statistics t-tests, chi-square tests, and ANOVACorrelation analysisMultiple and logistics regressionFactor analysis and testing scale reliabilityAdvanced data handling
Chapter 9: Logistic Regression
Logistic regression is used to predict a discrete outcome based on variables which may be discrete, continuous, or mixed. Thus when the dependent variable may have two or more than two discrete outcomes, logistic regression is a commonly used technique. Following are some common applications of logistic regression analysis in business and social sciences:
- Marketing: An international marketing manager of a big firm is interested in predicting whether foreign subsidiaries in a particular country will make profits or losses or will they just breakeven depending on the characteristics of the host country of the subsidiary's operations. The DV here is clearly discrete with three possible outcomes—loss, breakeven, or profit.
- Human Resource: The HR manager of a company wants to predict the absenteeism pattern of ...