Category Archive for: Discriminant and logit Analysis

Estimating the Binary Logit Model

As discussed in , the linear regression model is fit by the ordinary least squares (OLS) procedure. In OLS regression, the parameters are estimated so as to minimize the sum of squared errors of prediction. The error terms in regression can take on any values and are assumed to follow a normal distribution when conducting statistical tests. In…

Read More →

Interpret the Results

The interpretation of the results is aided by an examination of the standardized discriminant function coefficients, the structure correlations, and certain plots. The standardized coefficients indicate a large coefficient for income on function I, whereas function 2 has relatively larger coefficients for travel, vacation, and age. A similar conclusion is reached by an examination of the structure matrix To…

Read More →

Satisfied Salespeople Stay

A recent survey asked business people about the concern of hiring and maintaining employees during the current harsh economic climate. It was reported that 85 percent of respondents were concerned about recruiting employees and 81 percent said they were concerned about retaining employees. When the economy is uncertain, as in 2008-2009. turnover is rapid. Generally speaking, if an…

Read More →

Conducting Discriminant Analysis

The steps involved in conducting discriminant analysis consist of formulation, estimation, determination of significance, interpretation, and validation (see Figure 18.2). These steps are discussed and illustrated within the context of two-group discriminant analysis. Discriminant analysis with more than two groups is discussed later in this chapter Formulate the Problem The first step in discriminant analysis is to formulate…

Read More →

Discriminant and logit Analysis

Rebate Redeemers A study of 294 consumers was undertaken to determine the correlates of rebate proneness, or the characteristics of consumers who respond favorably to rebate promotions. The predictor variables were four factors related to household shopping attitudes and behaviors, and selected demographic characteristics (sex, age, and income). The dependent variable was the respondent’s degree of rebate proneness,…

Read More →

Back to Top