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Each entry in the table contains the frequency or count of the occurrences of values within a particular group or interval, and in this way, the table summarizes the distribution of values in the sample. This is an example of a univariate (=single variable) frequency table. The frequency of each response to a survey question is depicted.
Another method of grouping the data is to use some qualitative characteristics instead of numerical intervals. For example, suppose in the above example, there are three types of students: 1) Below normal, if the response time is 5 to 14 seconds, 2) normal if it is between 15 and 24 seconds, and 3) above normal if it is 25 seconds or more, then the grouped data looks like:
Univariate is a term commonly used in statistics to describe a type of data which consists of observations on only a single characteristic or attribute. A simple example of univariate data would be the salaries of workers in industry. [1]
SPSS is a widely used program for statistical analysis in social science. [7] It is also used by market researchers, health researchers, survey companies, government, education researchers, industries, marketing organizations, data miners, [8] and others.
MCA is performed by applying the CA algorithm to either an indicator matrix (also called complete disjunctive table – CDT) or a Burt table formed from these variables. [citation needed] An indicator matrix is an individuals × variables matrix, where the rows represent individuals and the columns are dummy variables representing categories of the variables. [1]
where f m is the modal frequency, K is the number of categories and f i is the frequency of the i th group. This can be simplified to = where N is the total size of the sample. Freeman's index (or variation ratio) is [2] = This is related to M as follows:
The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4] The parameters used are:
Configural frequency analysis (CFA) is a method of exploratory data analysis, introduced by Gustav A. Lienert in 1969. [1] The goal of a configural frequency analysis is to detect patterns in the data that occur significantly more (such patterns are called Types) or significantly less often (such patterns are called Antitypes) than expected by chance.