Р. П. Мильруд Доктор педагогических наук, профессор кафедры иностранных языков

11. The department has exceeded its __________ level of spending. 
12. The survey got more than 1,000 ________________ . 
13. Sixty-four per cent of ______ reported side effects from the drug. 
 
PRACTICE ACTIVITIES 
 
10. Analyze information on data types and fill in the spaces in Figure 6.9. below. Illustrate each type of data 
with your own examples.
 
TYPES OF QUANTITATIVE DATA
 
According to many statistics textbooks quantitative data are classified into data types using a hierarchy of meas-
urement, often in ascending order of numerical precision. These different levels of numerical measurement dictate the 
range of techniques available to you for the presentation, summary and analysis of your data. 
Quantitative data can be divided into two distinct groups: categorical and quantifiable. 
Categorical data refer to data whose values cannot be measured numerically but can be either classified into sets 
(categories) according to the characteristics in which you are interested or placed in rank order. They can be further 
subdivided into descriptive and ranked. A car manufacturer might categorise the cars it produces as hatchback, saloon 
and estate. These are known as descriptive (or nominal) data as it is impossible to measure the category numerically or 
rank it. For virtually all analyses the categories should be unambiguous and not overlap. Although these data are purely 
descriptive, you can count them to establish which category has the most and whether cases are spread evenly between 
categories. Ranked (or ordinal) data are more precise. In such instances you know the definite position of each case 
within your data set, although the actual numerical measures on which the position is based are not recorded. 
Quantifiable data are those whose values you actually measure numerically as quantities. This means that quanti-
fiable data are more precise than categorical as you can assign each data value a position on a numerical scale. Within 
this group there is, again, a subdivision: continuous and discrete. Continuous data are those whose values can theoreti-
cally take any value provided that you can measure them accurately enough. Data such as furnace temperature, delivery 
distance and length of service are therefore continuous data. Discrete data can, by contrast, be measured precisely. Each 
case takes one of a finite number of values from a scale that measures changes in discrete units. These data are often 
whole numbers (integers) such as number of mobile phones manufactured or customers served. 

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