According to a 2020 Microstrategy survey, 94% of enterprises report data and data analytics are crucial to their growth strategy. And yet, surprisingly, as much as 73% of the data that enterprises ...

The analysis of categorical data underpins inquiries across disciplines ranging from social sciences to genomics. At its core, categorical data analysis seeks to model relationships between variables ...

Up until the last article, we dealt with "numerical noise" such as missing values and outliers. This time, we are looking at ...

The focus of this tutorial is on “associations” between two categorical variables. The scenario we will examine is the following: Are managerial status and gender at the bank related? Naturally, we ...

A categorical variable is defined as one that can assume only a limited number of values. For example, a person's sex is a categorical variable that can assume one of two values. Variables with levels ...

The canonical correspondence analysis (CCA) is a multivariate direct gradient analysis method performing well in many fields, however, when it comes to approximating the unimodal response of species ...

Unlike metric data, categorical data does not consist of interval-scaled numerical values, which allows for arithmetic calculations. Categorical variables occur in nominal and ordinal scales, such as ...

A double sided worksheet with solutions. Students need to gather data on pet ownership from the class. This data is then organised into a table. The students then use the data to answer questions and ...