Quantitative or numerical data are numbers, and that way they 'impose' an order. The number and type of variables you have measured is A. B. four; two categorical and two quantitative. Typically it can be considered continuous because there are so many values for it, but people get paid (usually) in figures up to two decimal places and annual income is usually recorded in … Distribution tables { quantitative data (*) To summarize quantitative data in a table, the typical approach is to transform it into categorical data. You measure the age, marital status and earned income of an SRS of 1463 women. Ditto for ssn's and phone numbers. Ordinal level: You create brackets of income ranges: \$0–\$19,999, \$20,000–\$39,999, and \$40,000–\$59,999. (*) The quantitative data is divided into class intervals. For example, suppose you have a variable such as annual income that is measured in dollars, and we have three people who make \\$10,000, \\$15,000 and \\$20,000. For example, salary can be turned into a nominal variable by defining "high salary" as an annual salary of more than \$200,000, "moderate salary" as less than or equal to \$200,000 and more than \$75,000, and "low salary" as less than or equal to \$75,000. If we have income, the sum of any two incomes is another possible income. (*) The size, or relative size of each class interval is recorded in … The two main data types in business are nominal (categorical or qualitative data) and interval data (quantitative or continuous data). The second person makes \\$5,000 more than the first person and \\$5,000 less than the third person, and the size of these intervals is the same. You ask participants to select the bracket that represents their annual income. The bottom line is that one can make algebraic sense of numerical variables and that one can't make algebraic sense of categorical variables. However what sense does (zipcode1) + (zipcode2) have. Example of a variable at 2 levels of measurement You can measure the variable of income at an ordinal or ratio level. Actually there are three main types of data. Income Eligibility 1.1 Income Guidelines: Eligibility guidelines are based on the Federal Poverty Guidelines. But watch it! income (income eligibility) or the source of household income (categorical eligibility). C. four; one categorical and three quantitative. E. three; one categorical and two quantitative. Variables shown at the left of the preceding table can be converted to those farther to the right by using cutoff points. The first step towards selecting the right data analysis method today is understanding categorical data. Qualitative or categorical data have no logical order, and can't be translated into a numerical value. Variables can be classified as categorical or quantitative.Categorical variables are those that provide groupings that may have no logical order, or a logical order with inconsistent differences between groups (e.g., the difference between 1st place and 2 second place in a race is not equivalent to the difference between 3rd place and 4th place). ____ 5. 1. Eye colour is an example, because 'brown' is not higher or lower than 'blue'. D. three; two categorical and one quantitative. ____ 6. Examples are age, height, weight. 14563. Strictly speaking it's discrete, but it really depends on what you want to use it for. The distinction between categorical and quantitative variables is crucial for deciding which types of data analysis methods to use. Nominal data are just categories on variables such as customer names, and marital status and you cannot do any mathematical operations on this type of data.