Types of data
Different types of data influence how we approach analysis and interpret information. Categorising data as univariate, bivariate, categorical, or numerical can help determine the most appropriate and effective display methods. Data types form the foundation for interpreting patterns and making informed decisions in a variety of fields, such as research, business, science, and healthcare.
Use this page to revise the following concepts within types of data
Univariate and Bivariate Data
Univariate data are generated when each observation involves recording information about a single variable. The analysis of this type of data focuses on describing and summarising the distribution of a single variable, it does not explore relationships between variables or attempt to identify causes.
An example of univariate data can be the weight of tuna fish.
Bivariate data is generated when information about two variables is recorded for each subject. The analysis of this type of data focuses on understanding the relationship or association between these two variables.
An example of bivariate data can be a student’s test score and the time they spent studying for the test.
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Categorical and Numerical Data
Univariate data can be further classified as categorical or numerical.
Categorical data represents distinct groups or categories that can’t be measured numerically. Categorical data can be further defined as ordinal data (can be put in order) or nominal (can’t be put in order).
Numerical data involves values that are measurable or countable. Numerical data can be further defined as discrete data (can be counted) or continuous data (can be measured).
For each example listed below, classify it as either categorical or numerical by putting it in the appropriate column.