Crosstabs is an SPSS procedure that cross-tabulates two variables, thus displaying their relationship in tabular form. In contrast to Frequencies, which summarizes information about one variable, Crosstabs generates information about bivariate relationships.

Crosstabs creates a table that contains a cell for every combination of categories in the two variables.Inside each cell is the number of cases that fit that particular combination of responses.

SPSS can also report the row, column, and total percentages for each cell of the table.

Because Crosstabs creates a row for each value in one variable and a column for each value in the other, the procedure is not suitable for continuous variables that assume many values. Crosstabs is designed for discrete variables--usually those measured on nominal or ordinal scales.

Crosstabs creates a table that contains a cell for every combination of categories in the two variables.Inside each cell is the number of cases that fit that particular combination of responses.

SPSS can also report the row, column, and total percentages for each cell of the table.

Because Crosstabs creates a row for each value in one variable and a column for each value in the other, the procedure is not suitable for continuous variables that assume many values. Crosstabs is designed for discrete variables--usually those measured on nominal or ordinal scales.

Discriminant function analysis is used to determine which continuous variables

discriminate between two or more naturally occurring groups.

Discriminant function analysis is multivariate analysis of variance (MANOVA)

reversed. In MANOVA, the independent variables are the groups and the

dependent variables are the predictors. In DA, the independent variables are the

predictors and the dependent variables are the groups. As previously

mentioned, DA is usually used to predict membership in naturally occurring

groups. It answers the question: can a combination of variables be used to

predict group membership? Usually, several variables are included in a study to

see which ones contribute to the discrimination between groups

A single interval variable might discriminate between groups in an almost perfect fashion, not at all, or somewhere in between. For example, if one wished to differentiate adult males and females, one could collect information on how many bras the person owned, score on the last statistics test, and height. In the case of the number of bras, the discrimination would be very good, but not perfect (some women don't own any bras, some men do). In the case of the score on the last statistics test, little discrimination would be possible because males and females generally score about the same. In the case of height, some discrimination between adult males and females would be possible, but it would be far from perfect.

In general, the larger the difference between the means of the two groups relative to the within groups variability, the better the discrimination between the groups. The following program allows the student to explore data sets with different degrees of discrimination ability

Posted By : -

Raghavendra Ramchandra Nitturkar (13030)

Operations Grp 1

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