Introduction of statistics and probability

1.Recall the different concepts in statistic;
2.Classify, organize and gathering data;
3.Understand the different scales of data measurement;
4.Calculate measures of central tendency, position and
variability;
5.Differentiate Permutation from combination; and
6.Understand the basic probability concept.

Describe and differentiate the following
measure of central tendency?
1. range
2. Mean
3. Median
4. Mode

is the science that deals with the collection,
organization and presentation, analysis and
interpretation of all kinds of data pertinent to the
study being considered, so that the meaningful
conclusion can be drawn.

 Aids in decision making by providing comparison of data,
 explains action that has taken place,
 justify a claim or assertion,
 predicts future outcome and estimates un known quantities.
 Summarizes data for public use

 In Biological and medical sciences, it helps researchers discover relationship worthy
of further attention.
Ex. A doctor can use statistics to determine to what extent is an increase in blood pressure dependent upon
age
 In social sciences, it guides researchers and helps them support theories and models
that cannot stand on rationale alone.
Ex. Empirical studies are using statistics to obtain socio-economic profile of the middle class to
form new socio-political theories

 In business, a company can use statistics to forecast sales, design
products, and produce goods more efficiently.
Ex. A pharmaceutical company can apply statistical procedures to find out if the new formula is
indeed more effective than the one being used
 In Engineering, it can be used to test properties of various materials.
Ex. A quality controller can use statistics to estimate the average lifetime of the products
produced by their current equipment.

is a group of statistical measurements that aims to
provide the basic characteristics of a data set through
tables and graphs and other descriptive measures such
as measure of central tendency, measures of position
and measures of variation.

aims to infer or to make interpretations by making a
concluding statement about the population based on
the results derived from the data set. Measures
commonly used inferential statistics include analysis of
variance, t-test, chi square test, correlation and
regression analysis.

DESCRIPTIVE INFERENTIAL
A bowler wants to find his bowling average for
the past 12 months
A bowler wants to estimate his chance of winning
a game based on his current season averages and
the average of his opponents.
A housewife wants to determine the average
weekly amount she spent on groceries in the past
3 months
A housewife would like to predict based on last
year’s grocery bills, the average weekly amount
she will spend on groceries for this year.
A politician wants to know the exact number of
votes he receives in the last election
A politician would like to estimate based on
opinion polls, his chance for winning in the
upcoming election.

1.Collection data
2.Organization and presentation of data
3.Analysis of data
4.Interpretation of data

is a body of information or observation being
considered by the researcher.
when data is processed, which is the basis for decision
making is produced.

statistical and is typically
structured in nature – meaning it is
more rigid and defined. This type
of data is measured using
numbers and values, which makes
it a more suitable candidate for
data analysis.
Qualitative data is non-statistical and is
typically unstructured or semi-structured
in nature. This data isn’t necessarily
measured using hard numbers used to
develop graphs and charts. Instead, it is
categorized based on properties,
attributes, labels, and other identifiers.

QUANTITATIVE QUALITATIVE
qualitative is open for exploration, quantitative
data is much more concise and close-ended. It can
be used to ask the questions “how much” or “how
many,” followed by conclusive information.
Qualitative data can be used to ask the question “why.” It
is investigative and is often open-ended until further
research is conducted. Generating this data from
qualitative research is used for theorizations,
interpretations, developing hypotheses, and initial
understandings.
Quantitative data can be generated through:
Tests
Experiments
Surveys
Market reports
Metrics
Qualitative data can be generated through:
Texts and documents
Audio and video recordings
Images and symbols
Interview transcripts and focus groups
Observations and notes

Primary data is the type of data that is
collected by researchers directly from
main sources.
secondary data made available to
researchers from existing sources are
formerly primary data which was
collected for research in the past. The
availability of secondary data is highly
dependent on the primary researcher's
decision to share their data publicly or
not.

When the data has not been placed in
any categories and no
aggregation/summarization has taken
placed on the data then it is known as
ungrouped data.
When raw data have been grouped in
different classes then it is said to be
grouped data..

are countable in a finite amount of time.
For example, you can count the change in
your pocket. You can count the money in
your bank account. You could also count
the amount of money in everyone’s bank
accounts.
a refers to the unfixed number of
possible measurements between
two realistic points.

describes a variable with categories that do not have a natural order or ranking. You can
code nominal variables with numbers if you want, but the order is arbitrary and any
calculations, such as computing a mean, median, or standard deviation, would be
meaningless
is a scale (of measurement) that uses labels to classify cases (measurements) into
ordered classes. Note that an ordinal scale implies that the classes must be put into an
order such that each case in one class is considered greater than (or less than) every
case in another class.

is one where there is order and the difference between two values is meaningful.
Examples of interval variables include: temperature (Farenheit), temperature (Celcius),
pH, SAT score (200-800), credit score (300-850).
a quantitative scale where there is a true zero and equal intervals between neighboring
points. Unlike on an interval scale, a zero on a ratio scale means there is a total
absence of the variable you are measuring. Length, area, and population are examples
of ratio scales.

Introduction of statistics and probability

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