Statistical Treatment Data

1. STATISTICAL TREATMENT
OF DATA
What is statistical treatment of
data?
Statistical treatment of data is when
you apply some form of statistical
method to a data set to transform it
from a group of meaningless numbers
into meaningful output.

2. Types of statistical analysis:
1. Descriptive Statistics
Fundamentally, it deals with organizing and summarizing data using
numbers and graphs. It makes easy the massive quantities of data for
intelligible interpretation even without forming conclusions beyond
the analysis or responding to any hypotheses.
2. Inferential Statistics
The inferential statistical analysis basically is used when the
inspection of each unit from the population is not achievable, hence,
it extrapolates, the information obtained, to the complete population.

3. 3. Predictive Analysis
Predictive analysis is implemented to make a prediction of
future events, or what is likely to take place next, based on
current and past facts and figures.
4. Prescriptive Analysis
The prescriptive analysis examines the data to find out what
should be done, it is widely used in business analysis for
identifying the best possible action for a situation.

4. 5. Causal Analysis
In general, causal analysis assists in
understanding and determining the
reasons behind “why” things occur, or why
things are as such, as they appear.

5. Statistical treatment of data involves the use of statistical
methods such as:
MEAN PERCENTAGE
MEDIAN STANDARD DEVIATION
FREQUENCIES VARIANCE
REGRESSION DISTRIBUTION RANGE

6. • mean – denotes the equal distribution of values
for a given data set.
•median – a statistical measure that determines
the middle value of a dataset listed in ascending
order.
• frequencies - a measure of the number of
occurrences of a particular score in a given set of
data.

7. •regression – is a powerful statistical method that
allows you to examine the relationship between two or
more variables of interest.
• percentage – is a display of data that specifies the
percentage of observation that exist for each data
point or grouping of data points.

8. •standard deviation – is the measure of
dispersion of a set of data from its mean.
• variance - a measure of variability.
- tells the degree of spread in your
data set.
•distribution range – is the spread of your data
from the lowest to the highest value in the
distribution

9. EXAMPLE OF MEAN, MEDIAN , VARIANCE, STANDARD DEVIATION AND DISTRIBUTION RANGE
STUDENTS EXAM SCORE Xi Xi - X (Xi - X)2
JUSHUA 85 85 7 49
ANDY 76 76 -2 4
ERIC 73 73 -5 25
PRINCESS 80 80 2 4
MARA 72 72 -6 36
CRISTINE 81 81 3 9
MARK 79 79 1 1
546 128
X = = 78
MEAN( X) = 78
MEDIAN = 79 Middle value

10. V =
= 21.33
S. D =
= 4.67 RANGE= 85 - 72 = 13

11. EXAMPLE OF FREQUENCIES AND PERCENTAGE
BLOOD TYPE FREQUENCY PERCENTAGE(%)
A 5 25%
B 5 25%
O 6 30%
B 4 20%
A, O ,A ,B ,B, AB ,B, B, O, A, O, O,O, AB, B, AB, AB, A, O ,A
TO GET THE PERCENTAGE (%)
here is the fOrmula;
% = x 100
% = = 0.25 x 100 = 25%