1. Qualitative, Nominal 2. Quantitative, Ratio 3. Qualitative, Ordinal 4. Quantitative, Nominal 5. Qualitative, Ordinal

1. -A BRANCH OF MATHEMATICS
THAT DEALS WITH THE
COLLECTION, ORGANIZATION,
ANALYSIS, AND
INTERPRETATION OF DATA.
STATISTICS

2. TWO DIVISIONS OF STATISTICS
DESCPRIPTIVE STATISTICS
- concerned with classification,
presentation and collection of summarizing
values to describe group characteristic of data.
-topics included in this study are measures
of central tendency, variability and average of
scores, skewness and kurtosis.
- summarizing and describing without
attempting to infer

3. Examples:
Class average of examination
Average salary
Average return of investment

4. INFERENTIAL STATISTICS
- methods dealing with making
inference,estimates or making predictions about the
large set of data using the information gathered.
Examples:
Determining whether the impact of the new ad of various
age groups is significant or not
Whether there is significant relationship between job
satisfaction and performance of employees.

5. POPULATION VS SAMPLE
POPULATION
- complete set of individuals, objects, places, events
and reactions having some common characteristics.
Example:
Ages of graduating students
IQ scores of employees
Number of houses
A SAMPLE
-a representative cross-section of elements drawn
from a population.

6. VARIABLE
- defined as a characteristic or attribute of
persons or objects, which can assume different
values for different persons or objects.
- refers to the property that can take on different
values or categories which cannot be predicted with
certainty.
Ex.
Undergraduate major, Smoking habit, Height,
Faculty ranks

7. CLASSIFICATION OF VARIABLES
1. According to functional relationship
a. Independent variable(predictor)
b. Dependent variable(criterion)
Example:
The Academic performance of students is dependent
on the IQ of students towards Statistics.

8. 2. According to continuity of values
a. Continuous variables
- variables that can take the form of
decimals.
Ex. Prices of commodities, weight, height,
average grades in school
b. Discrete variables
- variables that can not take the form of
decimals.
Ex. Number of students, number of houses

9. CLASSIFICATIONS OF DATA
QUALITATIVE DATA
- categorical data taking the form of
attributes or categories. They have labels or names
assigned to their respective categories.
Examples:
Sex - male, female
Year level - 1st yr, 2nd yr, 3rd yr, 4th yr
Course - BSCrim., BSED
Religion - INC, Born Again, Catholic

10. QUANTITATIVE DATA
- data that consist of numbers obtained from
counts or measurements like weights, heightsm ages,
temperatures, scores, IQ, prices, and other
measurable quantities.
Examples:
weight - 100 lbs, 215 kgs
height - 34 in., 5cm
ages - 5 y/o, 21 y/o

11. RAW VS ARRAYU
RAW
data in its original form
ARRAY
data arranged either from highest to lowest or
from lowest to highest.
Examples: Exam Grades
RAW : 18 22 17 18 25 30 35 21 10 11
ARRAY: 10 11 17 18 18 21 22 25 30 35

12. MEASUREMENT SCALES
-qualitative data may be converted to
quantitative data by the process called
measurement.
*numbers are used to code subjects or items so
that they can be treated statistically.
Example:
1 – very hot 3 – warm
2 – hot 4 – cold

13. (Classification of data)
3. According to the Level of Measurements
a. Nominal Scale
- numerical values are used to classify
objects, person or characteristic to identify groups to
which they belong
- numbers are used for
identification/classification purposes only.
- not arranged in ordering
scheme(unordered)

14. Solution
Examples
Cause of death
Gender
Transportation
Occupation
Types of court
Religion : 1 - Roman Catholic 2- INC
3 – Born again 4 - Protestant

15. b. Ordinal Scale
- categorical data having ordered sclae
- ranked or ordered in some low-to-
high manner
Examples:
Pain level : 1- none 2- mild 3- moderate 4- severe
Beauty contest Ranks
Educational attainment

16. c. Interval Scale
- having interval(of known sizes)
- based on unit or interval that is
accepted as common standard
- o(zero) does not imply the absence of
characteristic under consideration
Example: Temperature 0 degrees – cold!

17. d. Ratio Scale
- has true zero point
- it indicates the absence of the
characteristic under investigation
Examples:
height in meters
age in years

18. EXERCISES # 1 (1/4)
DETERMINE whether the given data is quantitative
or qualitative. Indicate too the level of measurement.
1. Type of Case(Civil, Criminal, etc)
2. Years in service as a teacher in Cronasia.
3. Design and layout of the room(poor,fair,good,very
good, excellent)
4. Court employee number
5. Court personnel are competent(strongly agree,
agree, disagree)