Exam 18: Discriminant and Logit

Discriminant functions are linear combinations of the predictor or independent variables, which will best discriminate between the categories of the criterion or dependent variable (groups).

The procedure that is used to estimate the parameters of the binary logit model is called the maximum likelihood method.

D is the in the model D = b₀ + b₁X₁ + b₂X₂ + b₃X₃ + ... + b_kX_k.

In discriminant analysis, we can obtain some idea of the relative importance of the variables by .

The logit model falls somewhere between regression and discriminant analysis in application.

The first step in discriminant analysis is to formulate the problem by identifying the objectives, the criterion variable, and the independent variables.

In discriminant analysis, the value of the coefficient for a particular predictor depends on the other predictors included in the discriminant function.

When conducting discriminant analysis, the analysis sample is that part of the total sample used to check the results of the estimation sample.

If you are using the direct method or stepwise discriminant analysis, you are at the step of conducting discriminant analysis.

When the dependent variable is binary and there is one independent variable that is nonmetric, in addition to two- group discriminant analysis, one can also use OLS regression, logit, and probit models for estimation.

In discriminant analysis, rejecting the null hypothesis means there is significant discrimination between groups.

Discriminant analysis is available in EXCEL.

In discriminant analysis, there is no multicollinearity in the predictor variables. Therefore, there is no ambiguous measure of the relative importance of the predictors in discriminating between groups.

Discriminant/logit analysis is similar to regression and ANOVA in which of the areas stated below (Table 18.1 in the text)?

Discriminant/logit analysis is similar to regression and ANOVA in which of the areas stated below (Table 18.1 in the text)?

The is a test of significance of the logistic regression coefficient based on the asymptotic normality property of maximum likelihood estimates.

As one of the statistics associated with discriminant analysis, the _ (unstandardized) are the multipliers of variables when the variables are in the original units of measurement.

The null hypothesis in discriminant analysis, that in the population, the means of all discriminant functions in all groups are not equal, can be statistically tested.

The hit ratio is the number of variables found to be significant by the discriminant analysis.

A characteristic profile is an aid to interpreting discriminant analysis results by describing each group in terms of the group means for the predictor variables..