Revised on November 9, 2011
Chapter 1
Descriptive and Inferential Statistics
Statistics
is the science of
•
Collecting
•
Classifying
•
Analyzing
•
Generalizing
a
set of
data
obtained for a special purpose.
Why study statistics?
Statistics provides methods for
•
Design:
planning how to gather data for research studies (Chapter 2)
•
Description:
Summarizing the data collected in ways that are easy to understand
(Chapter 3)
•
Inference:
Making decisions about the population using information in the sample.
(Chapters 5 – 16)
•
Probabilistic Reasoning:
All statistical techniques are based on probabilistic reasoning
(Chapter 4)
Data
is a collection of observations
o
On one or more characteristic(s) of interest [
random variable
(s)]
o
On population units (giving population data) or
o
On selected sample units (giving sample data).
We
collect data
by
o
Sample surveys or
o
Experiments or
o
Censuses
STA 6126 Chapters 1 & 2, page
1
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Once data is collected, the first step is to use
Descriptive Statistics
or
Exploratory Data
Analysis (EDA) [
first proposed by J. W. Tukey (1977) of Princeton University] for summarizing
the data using tables, graphs and numerical summaries.
Statistical Inference
is our main goal in sample surveys and experiments. Statistical inference is
the process of making decision about one or more
population
parameter(s)
using one or more
sample statistic(s).
Observe that for census data there is no need to use inferential statistics.
[Why?]
A parameter
is a numerical summary of the
population data
. Most frequently used ones
are the population mean (μ), the population standard deviation (σ), and the population
variance (σ
2
) and population proportion (
π
).
In general population parameters are shown
by lower case Greek letters.
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 Summer '09
 Statistics, Inferential Statistics, Exploratory Data, population units

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