2. At the end of the presentation, you
should be able to:
• Give the two integral parts of data
management.
• Offer reasons why data management is
important.
• Enumerate ten fields of work where statistics is
useful.
• Cite 3 usefulness of data management.
• Make decisions by analyzing, interpreting data
or information
3. Data Management or
Statistics
• The science of collecting, organizing,
presenting, analyzing and interpreting
numerical data.
• Refer to the mere tabulation of numeric
information in report of stock, market
transaction or to the body of techniques
used in processing or analyzing data.
4. Types of Statistics
• DESCRIPTIVE STATISTICS
- concerned with collecting, organizing, presenting
and analyzing numerical data. The statistician tries
to describe or summarize a situation.
2. INFERENTIAL STATISTICS
(STATISTICAL INFERENCE OR
INDUCTIVE STATISTICS)
- concerned with analyzing the organized data
leading to prediction or inferences.
5. Variables
• The characteristic that is being
studied.
• It varies across individuals or subjects.
• It includes age, race, gender,
intelligence, personality type,
attitudes, political or religious
affiliation, height, weight, marital
status, eye color, etc.
6. Two Types of Variables
• Qualitative
• Quantitative
a. Discrete Variable
b. Continuous Variable
7. It represents differences in quality, character
or kind but not in amount.
Example: Sex, birthplace or geographic
location, religious preference, marital status,
eye color, brand of computer purchased, etc.
• Qualitative Variables
8. 2. Quantitative Variables
It is numerical in nature and can be ordered
or rank.
Example: Weight, height, age, test scores,
speed, body temperature, grades, etc.
It also can be categorized as discrete or
continuous.
9. Discrete Variable
- variable whose values can be counted using
integral values.
Example: Number of enrollees, drop-outs,
deaths, number of students in a classroom,
number of computers functioning, number of
mathematics subjects and number of calls
received.
10. Continuous Variable
- variable that can assume any numerical
value over an interval or intervals. It can
yield decimals or fractions.
Example: Height, weight, temperature,
time.
11. Let's Try!
• The quantity of fat in a sausage
• The mark out of 50 for a geography test
• The weight of a 17 year old student
• The volume of water in a cup of coffee
• The number of toad in a lake
• The speed of a bicycle
• The age of a person
• The height of the Eifel tower
• Shoe size
• Time taken to run a race
12. Dependent and
Independent Variable
Dependent Variable - the variable whose value is
being predicted
Independent Variable - the predictor
Example 1: to predict the amount of sunlight on
the growth of a certain plant.
Example 2: to evaluate the effect of using
computer to the performance of the students.
13. DATA
• A collection of observations on one or
more variables.
• Factual information such as
measurements or statistics used as a
basis for reasoning, discussion or
calculation.
• The raw material which the statistician
works. It can be found through surveys,
experiments, numerical records and
other modes of research.
14. Primary and
Secondary Data
Primary Data - refers to the information gathered
directly from the original source or which are based on
direct or first hand experience. (e.g. - surveys,
interviews, observations, registrations,
autobiographies, etc.)
Independent Variable - refers to the information which
are taken from published or unpublished data which are
previously gathered by other individuals or agencies.
(e.g. - books, magazines, internet. newspapers, etc.)
15. You have completed your postgraduate study with
flying colours and published a couple of papers to
disseminate your research results. Your papers have
been cited widely in the research literature by
others who have built upon your findings. However,
three years later a researcher has accused you of
having falsified the data.
Example:
16. • Do you think you would be able to prove
that you had done the work as
described? If so, how?
2. What would you need to prove that you
have not falsified the data?
Questions
17. The documentation of data analysis and
transformation as well as the
storing of data and research results are
integral part of data
management and could help you to
prove your work. Without data
management, not only a solid basis
ensuring replicability for your
research results may be missing, but
your data can also be subject to
data loss more easily.
18. • Weather Forecast
• Emergency Preparedness
• Predicting Disease
• Medical Studies
• Genetics
• Political Campaigns
• Insurance
• Consumer Goods
• Quality Testing
• Stock Market
Everyday Reasons
Why Statistics is
Important
19. Usefulness of Statistics
• useful in making conclusions and/or
predictions of the events of the world
• used to describe the natural order and
occurrences of the universe
• used to organize patterns and regularities
as well as irregularities
