Statistics : Basic Terminology

A population :

is a set of units ( usually people, objects, transactions, or events) that we are interested in studying.

For example; population may include :

(a) all employed workers in the Republic of Indonesia for the year 2011.

(b) all registered voters in Indonesia administered by Komisi Pemilihan Umum.

(c) everyone who have purchased all blackberry gadget in Indonesia up to now.

(d) all crude palm oils export number in metric-ton by Indonesia’s CPO producers within the year 2010.

In studying a population, we focus on one or more characteristics or properties of the units in the populations. We call such characteristics variables. For example, we may interested in the variables ages, gender, income, education of the people currently unemployed in the Indonesia for the year 2010.

 

The field of statistics deals with measurements – some quantitative and others qualitative.

The measurements are the actual numerical values of variables. ( qualiative variables could be describe by numbers, although such a description might be arbritrary; for example North = 1, South = 2, East = 3, West = 4 ; or Yes = 1 , and No = 0 ).

The 4 (four)  generally used scales of measurements are listed here from weakest to strongest.

Nominal scales :

In the nominal scale of measurement, numbers are used simply as labels for groups or classses. If our data set consists of blue, green, and red items, we may designate blue as 1, green as 2, and red as 3. In this case, the numbers 1, 2,  and 3 stand only for the category to which a data points belongs. ” nominal ” stands for “name” of category. The nominal scale of measurement is used for qualitative rather than quantitative data: blue, green, red ; male, female ; an so on.

Ordinal scale :

In the ordinal scale of measurement, data element may be ordered according to their relative size or quality. Four products ranked by a cunsumer may be ranked as 1, 2, 3 , and 4 , where 4 is the best  and 1 is the worst. In this scale of measurement we do not know how much better one product is than others, onlu that is is better.

Interval scale :

In the interval scale of measurement, the value of zero is assigned arbitrarily and therefore we cannot take ratios of two measurements. But we can take ratios of intervals. A good example is how we measure time of day, which is an interval scale. We cannot say 10:00 AM is twice as long as 05: AM . But we can say that the interval between 0:00 AM (midnight) and 10:00 AM which is a duration of 10 hours, is twice as long as the interval between 00:00 AM (midnight) and 05:00 AM which is a duration of 5 hours. This is because 00:00 AM does not mean absence of any time.

Ration scale :

If two measurements are in ratio scale, then we can take ratios of those measurements. The zero in this scales is an absolute zero. For example ; money is measured in a ration scale. A sum of $ 100 is twice as large as $ 50. A sum of $ 0 means absence of any money and is thus absolute zero. We have already seen that measurement of duration (but not time of day) is in a ratio scale. In general, the interval between two interval scale measurement will be in ratio scale. Others example of the ratio scale are the measurement of weight, volume, area, or length.

 

The population consists of the set of all measurement in which the investigator is interested. The population is also called the universe.

A sample : is a subset of measurements selected from the population. Sampling from the population is often done randomly, such that every possible sample of n elements will have an equal chance of being selected. A sample selected in this way ia called a simple random sample. A random sample allows chance to determine its elements.

 

Problems :

1-1 : A survey by an electric company contains questions on the following:

(1) Age of household head.

(2) Sex of household head.

(3) Number of people in household.

(4) Use of electric air conditioner – AC  (yes or no).

(5) Number of large appliances used daily.

(6) Average number of hours AC  is on.

(7) Average number of hours AC is off.

(8) Average number of AC usage a day.

(9) Household income.

(10) Average monthly electric bill.

QUESTION ! : Describe the variables implicit in these 10 items as quantitative or qualitative , and describe the scales of measurement !

 

 

 

 

 

 

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