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Summary Business Research period 3
International business HBO first year
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Business research Period 3 week 1What is meant by statistics? A statistic is a number used to communicate a piece of
information. For example; inflation, your school’s average grade, the price of a new car.
Statistics is the set of knowledge, and skills, used to organize summarize and analyse data.
With statistics decisions can be made.
Statistics; the science of collecting, organizing, presenting, analysing, and interpreting data
to assist in making more effective decisions.
Types of statistics
Descriptive statistics: methods of organizing summarizing and presenting data in an
informative way. Masses of unorganized data are of little value as is, however, descriptive
statistics can be used to organize data into a meaningful form.
Inferential statistics: the methods used to ESTIMATE a property of a population on the basis
on the sample.
- Population; the entire set of individuals or objects of the interest or the
measurements obtained from all individuals or objects of interest.
- Sample; a portion or part from the population of interest.
The process of sampling from a population with the objective of estimating properties of a
population is called inferential statistics.
It is widely applied to learn something about a population in business, agriculture, politics
and government.
Types of variables
There are two basic variables; qualitative (categorical) and quantitative (numeric).
If we gain qualitative information, most of the time we are looking for percentages. For
example, what percentage of the population has blue eyes?
Quantitative variables can be either continuous or discrete.
Discreet variables: can assume only certain values and there are gaps between values. For
example; number of rooms in a house, number of cars arriving at a park in an hour, and the
number of students for a particular course in a certain section (section A 50 students, section
B 30 students). Typically, discrete variables are counted. With gaps they mean that 3,456 is
not possible, it is 3 or 4.
Continuous variables: can assume any value within a specific range. For example, the air-
pressure, the weight of a shipment of tomatoes, ounces of raisins in a box, duration of your
flight. Continuous variables result from measuring.
Four levels of measurements:
, 1. Nominal-level data: For the nominal level of measurements, observations of a
qualitative variable are measured and recorded as labels or names. The labels or
names can only be classified and counted. There is no particular order. So with
nominal data we only write down the number of observations. For example, there
are 6 colors of M&M’s, 20 % is blue, that is the only thing you can say when using
nominal data.
2. Ordinal level data: for this level of measurement a qualitative variable or attribute is
either ranked or rated on a relative scale. For example, many businesses make
decisions about where to locate their business, of course concerning many factors. all
of these factors can be processed and a top 10 can be made. This is a typical example
of ordinal data since it is ranked in an order of best to worst. Another example is
rating your teachers from superior to inferior. With the frequency you can write
down the percentages.
3. Interval-level data; it includes all the characteristic of the ordinal level, but, in
addition, the difference or interval between values is meaningful.
The interval level of measurement is based on a scale with a known unit of
measurement. The Fahrenheit is an example of interval data. 20 degrees and 25
degrees have 5 degrees in between. This is the same amount of 5 degrees as the
change from 50 to 55. The 0 does not stand for the absence of the condition. So it is
just another point on the scale. We CANNOT make statements as; 20 degrees is twice
as warm as 10 degrees. The dress-size is another measure that’s an interval. We call
it interval because the intervals are the same. From 20 inches to 22 inches is the
same as going from 12 inches to 14 inches. And again, size 0 doesn’t stand for the
absence of a dress, so there is no zero point on the scale. Important to notice, the
ratio doesn’t make sense. When you divide 24 inches to 12 inches it isn’t 2 inch…
4. Ratio-level data: it has all the characteristics of the interval but the 0 and the ratio
between two numbers are both meaningful. Examples of ratio’s are; wages, units of
production, and money. 0 means the absence of something and 6 is two times as
many as three. Look at pg. 11 for nice diagram.
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