The easiest form of data is called categorical, or qualitative, for example data on variables “country” (e.g., the data observation might be Germany) or “question response” (e.g., believe that climate change is manmade) or “exam grade” (e.g., B). Categorical variables and data can be either nominal or ordinal. The question about climate change, with possible responses “natural”, “manmade” or “don’t know/can’t answer” is a nominal categorical variable, as is the variable “country” - there is no ordering in the categories of these variables. By contrast, exam grade is an ordinal categorical variable, since its categories are ordered: A is better than a B, B is better than a C, and so on.

Other examples of nominal categorical variables are gender, region of residence, field of study, type of transport, type of housing, etc.

Other examples of ordinal categorical variables are income group (if incomes have been categorized), an attitude question in a survey where possible responses are strongly agree/agree/disagree/strongly disagree (these categories have an order), social class (with classes usually in an inherent order), terrorist threat levels (in the UK these are low/moderate/substantial/severe/critical), etc.

The other main type of data (see Fig. 1.4) is called continuous, or quantitative, for example data on variables “blood pressure”, “age” and “income”. These are observations of variables on continuous scales, usually rounded in some convenient way. For example, although age is a continuous time variable, and we are getting older all the time by seconds, minutes and hours, someone’s age is almost always rounded to the number of years completed. There is a subtle difference between interval-scale and ratio-scale continuous data, which is worth mentioning here. Age is an interval-scale variable: to compare two children of ages 10 and 12, we would compute the interval difference, i.e. 2 years. We would not say the 12-year old is 20% older than the 10-year old. But comparing prices or incomes, for example, we would tend to compute percentage differences, making them ratio-scale variables. A good example is the inflation rate, comparing the prices of a basket of products over time, not as a difference but as a percentage. As a general rule, most data on monetary values and those coming from physical measurements (e.g., lira, gold price, centimeters, kilograms) are ratio-scale variables.

As also understood from the information given, the correct answer is E.