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Weighted number of observations in each group is equal to the unweighted numberĬ. We are using the default weight of 1 for each observation in the dataset, so the Number of observations falling into each of the three groups. Observations into the three groups within job. Group Statistics – This table presents the distribution of
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In this example, all of the observations inī. Number (“N”) and percent of cases falling into each category (valid or one of SPSS might exclude an observation from the analysis are listed here, and the Analysis Case Processing Summary – This table summarizes theĪnalysis dataset in terms of valid and excluded cases. This will provide us withĬlassification statistics in our output. In job to the predicted groupings generated by the discriminant analysis.įor this, we use the statistics subcommand. We will be interested in comparing the actual groupings In this example, we have selected three predictors: outdoor, socialĪnd conservative. The discriminating variables, or predictors, in the variables subcommand. In parenthesis the minimum and maximum values seen in job. Subcommand that we are interested in the variable job, and we list In this example, we specify in the groups Performs canonical linear discriminant analysis which is the classical form ofĭiscriminant analysis. Will also look at the frequency of each job group. Uncorrelated variables are likely preferable in this respect. Very highly correlated, then they will be contributing shared information to theĪnalysis. These correlations will give us some indication of how much unique informationĮach predictor will contribute to the analysis. Next, we can look at the correlations between these three predictors. Observations in one job group from observations in another job These differences will hopefully allow us to use these predictors to distinguish Tables=outdoor social conservative by jobįrom this output, we can see that some of the means of outdoor, socialĪnd conservative differ noticeably from group to group in job. Let’s look at summary statistics of these three continuous variables for each job category. We are interested in how job relates to outdoor, social and conservative. To start, we can examine the overall means of theĬontinuous variables. Some options for visualizing what occurs in discriminant analysis can be found in theĭiscriminant Analysis Data Analysis Example. Will be discussing the degree to which the continuous variables can be used toĭiscriminate between the groups. Well the continuous variables separate the categories in the classification. We can predict a classification based on the continuous variables or assess how Specifically, we would like to know how manyĭimensions we would need to express this relationship. We are interested in the relationship between the three continuous variablesĪnd our categorical variable. Levels: 1) customer service, 2) mechanic and 3) dispatcher. Three continuous, numeric variables ( outdoor, social andĬonservative) and one categorical variable ( job) with three , with 244 observations on four variables. The data used in this example are from a data file, This page shows an example of a discriminant analysis in SPSS with footnotesĮxplaining the output.