STAT 101
Statistics is the science of collecting, organizing,
presenting, analyzing, and interpreting data to assist in
making more effective decisions.
Data is the values that variables can assume.
The collection of values of data forms a data set, and
each value in the data set is called
data value or datum.
Variable is a characteristic or attribute that can assume
different values.
Example
• daily temperature
• each person’s weight in a classroom
• gender
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Ordinal data classifies data value into categories that
can be ranked. (However, some difference between other
groups cannot be distinguished.)
Example. "How did you rate the facilities of a
university:" Superior, Good, Average or etc.
The data can be classified according to levels of
measurements. These are as follows:
Interval data are data values that can be ranked, the
precise difference between units exists; Data
classifications are ordered according to the amount of
characteristics they possess.
Example. Interval scale of measurement in women’s
shoe sizes.
Ratio data possesses all the characteristics of interval
data, but in addition, the 0 point is meaningful and the
ratio between two numbers is meaningful.
Example. Wages, weight, distance, and height
Independent variables are type of variables that can be
manipulated by the researcher.
Dependent variables are the resultant variables.
Example
To see the cause and effect relationship between the
grade and attendance of students.
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Qualitative variables are variables which involve
attributes or categories.
Example
• occupation
• gender
Given the data set, determine the following:
• what are the variables
• qualitative, quantitative variables.
• identify the discrete and continuous variables.
• level of measurements for each variable.
Quantitative variables are variables which involve
numerical values that can be order.
Example
• age
• weight
• height
TYPES OF STATISTICS
Descriptive statistics refers to the methods of
organizing, summarizing, and presenting data in an
informative way.
Example
The US government reports the population of the US
was:
• 179, 323, 000 in 1960
• 203, 302, 000 in 1970
• 226, 542, 000 in 1980
• 248, 709, 000 in 1990
• 265, 000, 000 in 2000
• 308, 400, 000 in 2010
Discrete variables are variables which can be counted.
Example
The number of students in Room 208.
Continuous variables are variables which can assume
an infinite number of values, and
can be obtained by measuring.
Example
The weight and height of each student in Room 208.
The data can be classified according to levels of
measurements. These are as follows:
Nominal data classifies data value into mutually
exclusively categories (non-overlapping) in which there
is no order or ranking.
Example. Gender, Name and Colors.
Inferential statistics or statistical inference refers to
the methods used to estimate a property of a population
on the basis of a sample.
Example
A recent survey showed 46% of the senior high schools
can solving problems involving fractions.
Population is the entire set of individuals or objects of
interest or the measurements obtained from all
individuals or objects of interest.
Example
• All the students enrolled at Batangas State University.
• All countries of the world.
Sample is a portion, or part of the population of interest.
Example
• 50 student athletes enrolled in Batangas State
University.
• All the countries in Southeast Asia.
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