The
course can be modified to cover some or all of the following topics depending
on participant needs.
Data
Analysis
1. Data
Collection: The Role of Sampling in Statistics
2.
Data Display: Graphical Techniques

Dotplots
and stemandleaf plots

Histograms

Cumulative
relative frequency

Scatterplots

Displaying
multivariate data
3.
Data Summarization: Descriptive sample statistics

The
sample mean

The
sample standard deviation

Modes,
quantiles, proportions, and boxplots
Probability
and Distributions
4.
Probability, Discrete and Continuous Populations

Probability

Discrete
random variables

Continuous
random variables

Population
parameters

Additional
topics in probability
5.
Some Useful Discrete and Continuous Distributions

The
binomial distribution

The
normal distribution

The
normal approximation to the binomial and checking for normality

The
distribution of the sample mean

The
Poisson, exponential, and hypergeometric distributions
Estimation
and Hypothesis Testing
6.
Estimation (One Sample)

General
remarks about estimation

Estimating
the true proportion in a population

Estimating
the mean

Estimating
the population median

Estimating
the population standard deviation
7.
Hypothesis Testing

Hypotheses,
test statistics and pvalues

The
decision rule and power

The
binomial test and acceptance sampling

Testing
hypotheses about the population mean and standard deviation

Testing
hypotheses about the population median
8.
Two Related Samples (Matched Pairs)
9.
Estimation and Hypotheses Testing with Two Independent Samples

Large
samples: inferences about the difference between two means

Difference
between two means: normal populations with equal variance
(twosample ttest)

Difference
between two means: normal populations with unequal variances (Satterthwaite's
test)

Difference
between two means: general populations

(WilcoxonMannWhitney
rank sum test)
Enumerative
Data
10.
Analysis of Enumerative Data
Correlation
and Regression
11.
Correlation
12.
Simple Linear Regression
13.
Multiple Linear Regression

Tests
of hypothesis in multiple regression

Methods
for selecting a regression model in the presence of several
independent variables

General
linear models with qualitative variables

General
linear models with interaction
Quality
Control
14.
Techniques for Monitoring Product Quality
Analysis
of Experimental Designs
15.
Analysis of Variance for OneFactor Experiments

An
overview of completely randomized designs

The
analysis of variance for the completely randomized design

Comparing
population means in a completely randomized design

A
comparison of means for general populations: The KruskalWallis
test
16.
Analysis of Variance for TwoFactor Experiments

The
analysis of variance for the randomized complete block design

Interaction
in twofactor experiments

Analysis
of twofactor experiments for general populations: The Friedman
test
17.
Other Useful Topics in Experimental Design

Analysis
of variance for threefactor experiments

The
analysis of covariance

Methods
for use with general populations
