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. Other measures of time are interval-scale variables (the word “interval” gives you a clue to that!), for example hours of sleep (on Sundays I sleep an hour longer - I would not say I sleep 14% longer) and time to run 100 meters (e.g., at the 2009 World Championships in Berlin, Usain Bolt shaved more than a tenth of a second off his record, clocking 9.58 seconds - we wouldn’t say he reduced the time from 9.69 seconds by 1.1%).

As also understood from the information given “Age”, “Hours of Sleep” and “Time to Run 100 meters” are interval scale variables, so the correct answer is C. “The Inflation Rate” and “Gold Price” are ratio-scale variables.