Data-set for Cases of Sexually Transmitted Diseases: Jos University Teaching Hospital, Plateau State,Nigeria

Published: 07-02-2020| Version 1 | DOI: 10.17632/s25947r7md.1
Hillary Ujah Ali,
Desmond Bisandu,
Chizoba Nwadinobi Akanihu


This procedure is designed to summarize several columns of quantitative data. It will calculate various statistics, including correlations, covariances, and partial correlations. Also included in the procedure are a number of multivariate graphs, which give interesting views into the data. Use the Tabular Options and Graphical Options buttons on the analysis toolbar to access these different procedures. After this procedure, you may wish to select another procedure to build a statistical model for your data. Depending on your goal, one of several procedures may be appropriate. Following is a list of goals with an indication of which procedure would be appropriate: GOAL: build a model for predicting one variable given values of one of more other variables. PROCEDURE: Relate - Multiple Factors - Multiple Regression GOAL: group rows of data with similar characteristics. PROCEDURE: Describe - Multivariate Methods - Cluster Analysis GOAL: develop a method for predicting which of several groups new rows belong to. PROCEDURE: Relate - Classification Methods - Discriminant Analysis GOAL: reduce the number of columns to a small set of meaningful measures. PROCEDURE: Describe - Multivariate Methods - Factor Analysis GOAL: determine which combinations of the columns determine most of the variability in your data. PROCEDURE: Describe - Multivariate Methods - Principal Components GOAL: find combinations of the columns which are strongly related to each other. PROCEDURE: Describe - Multivariate Methods - Canonical Correlations