 
Applied Statistics
Support Site
A Free Service for
those Interested in Applied Statistics. This site is always under
construction.
Prepared by
Craig A. Stevens, PMP, CC and his
Students
Links to
Resources on
this Website

Research Links

Stats Defined

Descriptive Statistics

Sampling Methods

Using Excel

Discrete Stats

Probability

Continuous Stats

Regression

Hypothesize Test

Confidence Intervals

ANOVA Tables

Least Squares Point Estimates

Test of Variability

Nonparametric

NonParametric Based on Ranks

Nonparametric Probability

Test of
Means

Sign Test

Business Intelligence and Data Mining

Six Sigma
Other Links

Link to Six Sigma

Basic
Statistics

Six Sigma Online Training  Six Sigma Online Certification
Six Sigma Training by Six Sigma Online, Black Belt Certification, Green Belt
Training, DFSS Design for 6 Sigma Courses & Certification "Best Six Sigma
Training on the Web" Payment Plans Available!!

http://math.about.com/library/weekly/aa020502a.htm

http://mathworld.wolfram.com

http://www.fourmilab.ch/rpkp/experiments/analysis/chiCalc.html

http://www.robertniles.com/stats/tests.shtml

http://members.aol.com/johnp71/javastat.html#WhichAnalysis

http://www.plantbio.ohiou.edu/epb/instruct/quantmet/lectures/pdf/lec7.pdf

http://home.ubalt.edu/ntsbarsh/excel/excel.htm#rFtest

http://members.aol.com/johnp71/javasta2.html#General

Links
From Dr. James L. Schmidhammer University of Tennessee Knoxville
General Statistics
StatSoft
Electronic Textbook
Linear Regression
Topics on Linear Regression from UCLA Web Site
StatSoft
Electronic Textbook
Logistic Regression
Topics on Logistic Regression from UCLA Web Site
Introduction to Logistic Regression (Patel)
Logistic Regression Analysis (Dayton)
StatSoft
Electronic Textbook
Decision Trees
Decision Trees for Predictive Modeling
Tree Structured Data Analysis
StatSoft
Electronic Textbook
Neural Networks
Neural Networks and Statistical Models
StatSoft
Electronic Textbook
Model Assessment
Tutorial on Receiver Operating Characteristic (ROC) curves
The Great American Humorist, Mark
Twain, Once said, "I have come loaded with Statistics."
www.thinkexist.com. Found by
LaSaundra PattonMatlock, (UoPhx, MBA Stats, 2005.)

"In this
class, out of all the other classes that I have taken, I really learned the
most. I appreciate you and your teaching style. You have a great sense of humor
and I wish you well ..."
Lavon Grissom (UoPhx MBA Stats,
2007)

This is a site to support the TNU MHR
3040 and UoPhx QNT 530,
531, 554, and Mgt 510) workshops.
Links to research data http://www.westbrookstevens.com/links_to_others.htm
What is Statistics? – A Study of how to best (a)
Collect Data, (b) Describe and Summarize Data, and (c) Draw Practical
Conclusions Based on Data. A way to:
 test theories and practices in some cases related to doing work
 determine the characteristics of a population by using a sample
 Use data to make inferences under uncertainty
Big Picture  Science of amassing data, taking a portion of it, and seeing
what that portion tells us about the whole
Little Picture  actual statistics themselves statistics with little “s”
is any number that represents something else
What is
Statistics?
By Verta
SessionWebb,
Kourtney
Tharpe,
TerriJane
Hammerle,
Augustine
Ozobu
(UoPhx
2008)
The word
"statistics"
comes in
various
definitions,
but not
one
could be
regarded
as
complete
and
absolute.
It has
been
frequently
referred
to
either
as the
quantitative
or
numerical
information
itself
or the
methods
of
dealing
with the
information.
To
facilitate
understanding,
many
statisticians
prefer
the term
"statistical
data"
for
quantitative
information
and the
methods
of
dealing
with the
information
the
"statistical
methods."
Statistics
is a
collection
of
procedures
and
principle
for
gaining
and
processing
information
in order
to make
decisions
when
faced
with
uncertainty.
(Davis,
Simon &
Utts,
2002).
This
paper
will
concentrate
the on
the
practical
application
of the
following
statistical
concepts
such as
statistical
data,
standard
deviation,
mean,
mode,
and
median
concepts,
and
sampling
techniques.
Statistical
Methods
Statistical
methods
are
usually
divided
into
five
basic
steps:
1.
Collection.
Quantitative
information
supplies
facts
for
solving
problems
involving
numbers.
After
the
identification
of the
problem,
certain
relevant
facts
that can
be
expressed
numerically
should
be
gathered.
Statistical
data can
be
classified,
according
to
source,
as
primary
and
secondary
data.
The
latter
is the
most
convenient
and
economic
way of
obtaining
information
from
published
materials.
When
published
data are
not
available
for a
particular
study,
primary
data may
become
necessary.
Collecting
primary
data by
survey
is
usually
a
costly,
tedious,
and
timeconsuming
process.
2.
Organization.
Secondary
statistical
data are
usually
in
organized
form.
However,
those
that are
collected
from a
survey
need
organization.
The
first
step in
organizing
data is
editing.
This is
done to
correct
omissions,
inconsistencies,
irrelevant
answers,
and
erroneous
computations.
The next
step is
classifying,
which is
to
decide
the
proper
classifications
in which
the
edited
items
will be
grouped.
This is
a very
important
step
since
the
succeeding
steps
are
affected
by given
classifications.
The last
step is
tabulation.
In this
step,
similar
items
are
counted
and
recorded
according
to
proper
classifications.
3.
Presentation.
In order
to
facilitate
statistical
analysis,
data are
presented
in
textual
form,
table,
or
graph.
Textual
presentation
is
convenient
only for
presenting
a few
items.
When a
large
mass of
data is
involved,
this
becomes
inefficient
and
burdensome,
since
detailed
explanations
and
properties
of data
may have
to be
repeated
many
times.
Users
usually
prefer
Tables
if they
can be
effectively
constructed.
A graph
is a
pictorial
representation
of data.
It
usually
gives
the user
of data
only an
approximate
value of
the
facts.
4.
Analysis.
This is
about
the
analysis
of a
population
or
universe
based on
a sample
study.
There
are
numerous
methods
of
analyzing
statistical
data.
Some are
simple
observation,
while
others
necessitate
the use
of
sophisticated
and
highly
mathematical
tools.
5.
Interpretation.
After
the
completion
of the
analysis,
the
findings
must be
interpreted.
Correct
interpretation
will
lead to
a valid
conclusion
of the
study.
Reference:
Davis,
D.,
Simon,
M., &
Utts, J.
(2002).
Statistics
and
Research
Methods
for
Managerial
Decisions.
Mason:
SouthWestern
College
Publishing.

All of the material here has been developed from the
following texts. For more information please buy and read the latest versions
of these texts.
 W. J. Conover, Practical Nonparametric Statistics 2nd ed, 1980, John
Wiley and Sons, Inc.
 Bowerman, Bruce L., Richard T. O'Connell, Business Statistics in
Practice, 3rd edition, 2003, McGrawHill Companies, Inc.
 Cooper and Schindler. (2003). Business Research Methods, Eighth Edition.
New York: McGrawHill.Lind and Mason. (2003).
 Lind. (2004). Statistical techniques in business & economics (11th ed). New York: McGrawHill.
 Lind. (2005). Statistical techniques in business & economics
(12th ed). New York: McGrawHill.
 More to come...
Step One: Understand What is Needed:
 Define and Write Out Goals  Under
line Information Being Requested, You may place in terms of Ho: and H1: (What are
you trying to decide or test?)
 Understand Statistical
Definitions, symbols, and Methods  Look up the terms you do not understand.

Find Data  The following link will take you to possible research
links. http://www.westbrookstevens.com/links_to_others.htm
Step Two: Decide on Method
 Descriptive: Start
as simply as possible. Simple Descriptive
Statistics are methods of organizing, summarizing, and presenting data
in informative ways (includes simple statistical calculations such as
Mean, Mode, etc and pure graphical methods)
 Inferential: Inferential Statistics allows you
to make a decision, estimate values, predict outcomes, or generalize about a
population, based on a sample.
 To Determine
the Probability of something happening (for classes MHR
3040, QNT 554,
and QNT
530).
 When estimating population parameters, one may use Confidence
Intervals. (for classes MHR
3040, QNT 554, and
QNT 531)
 Looking to provide evidence for making a decision, you may Test
a Hypothesis. (for classes MHR
3040, QNT 554, and
QNT 531)
 To Test or
Compare Two of More Sets of Data
 Regression Models
 Nonparametric
(See Statistical
Definitions)
 Design of Experiments (DOE)
Here is are a couple of links to find the appropriate method.
 http://www.graphpad.com/www/Book/Choose.htm

http://www.ats.ucla.edu/stat/stata/whatstat/default.htm

KEY
TO SELECTING CORRECT TEST
Step Three: Use Method: (Follow
the links to the left to find methods and steps in using methods.)
Step Four: Testing Ho:
Step Five: Testing Residuals:
Plotting is a way of detecting correlation between residuals. A tendency to
have runs of positive and negative residuals indicates positive correlation,
implying the independence has been violated. Randomization is important in
obtaining independence. If errors are normally and independently distributed
with mean zero and constant but unknown variance s^2, then ANOVA procedure is
exact test of Ho.
 Calculate eij = Yij  Average Yi.
 Place in the order as listed in data. k = 1,2,…N (3) Calculate Pk =
(K1/2)/N
Example Problems: You may
find example problems next to the subject when appropriate. This site is
always under construction, so it may take a while to put the information in the
right places.  
