The presentation will walk you through descriptive and inferential statistic measures, including a simple scenario, key measures and applications of descriptive and inferential statistic's.

1. Measure's of
Descriptive Statistics and
Inferential Statistics
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2. Introduction
Descriptive statistics are numbers that
are used to summarize and describe
data. The word "data" refers to the
information that has been collected from an
experiment, a survey, a historical record,
etc.
Inferential statistics is a set of data taken
from the population to represent the
population. Inferential statistics uses a
random sample of data taken from a
population to describe and make inferences
about the population.
Item 1 Item 2 Item 3 Item 4 Item 5
25
20
15
10
5
0
Item1 Item2 Item3 Item4 Item5
50
40
30
20
10
0

3. Example
Raw data Descriptive statistics
Descriptive statistics
Inferential statistics

4. Measures of Descriptive Statistics
Frequency distribution
Measures of central tendency
Measures of position
Measures of variability
Graphical representation
Correlation co-efficient

5. Measures of Inferential Statistics
Parametric
Non-
Parametric
T-test
ANOVA
Kolmogrov-smirnov
Mann-whitney
Median
Spearman rank correlation
Chi-square

6. It gives information that describes the data in some
detailed manner.
Organize, analyze and present data in a meaningful
way.
To describe a situation.
Descriptive statistic are concerned with describing
the characteristics of frequency distribution.
Charts, graphs and tables.
Applications of Descriptive Statistic

7. To makes inferences about population using data
drawn from the population.
To compare data, text hypothesis and make
predictions (Estimation or parameters).
To explain the chance of occurrence of an event.
It attempts to reach the conclusion to learn about
the population.
Applications of inferential Statistic

8. Measure of Central Tendency
Measure of variability
Hypothesis Test, Confidence
Interval and Regression Analysis
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