Business Statistics: Categorical or Quantitative Data?

Data and Statistics
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Data and Statistics
Introduction
Business statistics, like many areas of study, utilize data for decision making. As opined by Black (2019), business analysts interpret data, thus providing useful information for a wide variety of applications such as those that forecast future values or predict trends for companies. Different types of data are collected for analysis and interpretation from various sources such as business surveys, internet searches, empirical research, government documents, retail scanners, among others. Such data may be analyzed and presented to decision-makers either in the categorical or quantitative form. It is noteworthy that categorical and quantitative data have unique attributes that ought to be considered before choosing between the two types of data. Therefore, this paper discusses the different characteristics of categorical and quantitative data, thus picking the better option to present information for decision-makers in a company.
The distinction between Categorical and Quantitative Data
To begin with, categorical data or variables are those who take category or label values to place individuals into different groups. It follows that every observation can only be placed into one category, and all the categories used must be mutually exclusive. General features of categorical data include its division into nominal (named) data and ordinal (scale) data, its qualitativeness since it is descriptive, and the occasional use of numerical values as labels. Moreover, categorical data may be analyzed through mode, and median distributions where the nominal data is only explained through mode distributions, and ordinal data can be analyzed through both mode and median distributions. Additionally, ordinal data may be analyzed based on univariate and bivariate statistics, as well as regression applications, classification methods, and linear trends. In the foregoing, categorical data is useful for interpreting qualitative data sets, which could have otherwise been difficult to analyze. Accordingly, categorization simplifies large volumes of qualitative data and improves its visualiz...