Gender, handedness, favorite color, and religion are examples of variables measured on a nominal scale. The data fall into categories, but the numbers placed on the categories have meaning. In some cases, we see that ordinal data Is analyzed using univariate statistics, bivariate statistics, regression analysis, etc. Work with real data & analytics that will help you reduce form abandonment rates. Data types are an important aspect of statistical analysis, which needs to be understood to correctly apply statistical methods to your data. (Statisticians also call numerical data quantitative data.)

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Numerical data can be further broken into two types: discrete and continuous.

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