Statistics 112
Session One
Professor Esfandiari
Winter 2007
I
The objective of this lecture is to
A.
Make you familiar with the following terms:
Data: The information gathered from data and experiments
1.
Statistics: The art and science of learning from data
2.
Main aspects of statistics including
a)
Design: Planning how to collect data to answer question of interest
b)
Description: Summarizing data that are obtained
c)
Inference: Making decisions and descriptions based on data
3.
Sample: Subset of the population on which we collect data
4.
Population: Total subjects we are interested in
5.
Subject: The entities that we measure in a study
6.
Descriptive statistics: Using graphical and numerical summaries to
describe a sample
7.
Inferential statistics: Using the data from the sample to make inference
about the
8.
Population
9.
statistic: Numerical summary for the population
10. Sample: Numerical summary for the sample
B. Importance of random sampling in choosing a representative sample form the
population and designing experiments
C. The role of computers and a sample data file
Some basic definitions
Population
: Population includes all the potential elements that could be part of a
study. Population could be finite; i.e. we know how many elements are included in
the population. Population could be infinite ; we do not know how many elements are
included in the population.
Examples of finite populations
: All the high school students in the United States.
All of the students who major in political science in state universities in the United
States.
Examples of infinite populations:
The number of pebbles in a certain beach. The
number of trees in a certain jungle, etc.
Samples
are a fraction of the population and we already discussed methods of
choosing unbiased and representative samples.
Parameters
are used to describe a population.
Statistics
are used to describe a
sample.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Descriptive statistics
relates to describing the sample
. So, calculating the mean,
median, mode, variance, standard deviation, and correlation for a sample of size n are
examples of descriptive statistics.
Inferential statistics
is about generalizing the results from the sample to the
population.
For example, when we make a statement such as; "We are 95% confident
that this drug lowers blood pressure between 10 to 20 points", we are not making this
statement about a sample of size n. We are implying that these results are true for the
population of individuals who have high blood pressure. Thus, we are generalizing
the findings from the sample of size n to the population.
Symbols used to show parameters and statistics
Index
Symbol used to show
statistic in the ample
Symbols used to show
parameter in the population
Mean
X
μ
Percentage
P^ (hat shows estimate)
P
Variance
S^2
^2
σ
Standard deviation
S
σ
Correlation
r
μ
For clarification of
part A, the following problems will be discussed
•
To explain concepts 12, you will read and analyze scenarios 13
on pages 56.
This is the end of the preview.
Sign up
to
access the rest of the document.
 Spring '08
 Esfandini
 Statistics, Standard Deviation, Mean

Click to edit the document details