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Applied Statistics 3-Day On-Site Course

Course Description and Outline

Course description

This practical seminar introduces the logic and procedures for statistical estimation, hypothesis testing, and model fitting in a variety of settings. Numerous examples help demonstrate fundamental concepts of statistical reasoning and research design.

Emphasis on:

Practical applications
Designing good research
Drawing causal conclusions
Difference between statistical significance and practical importance

Analytical techniques for:

One- and two-sample settings
Classification models (i.e., ANOVA)
Correlational studies
Prediction methods (i.e., regression)

Numerous examples demonstrate popular computer programs:

SAS
SPSS
MINITAB
JMP
EXCEL

Course materials for each participant:

CD containing Dr. Schulman's book, Statistics in Plain English - written explicitly for this course
Course notes - exact copies of transparencies
Booklet of examples - demonstrating use of popular computer programs

Brief outline

1. Probability Distributions
2. Estimation
3. One-Sample Tests
4. Twp-Sample Tests
5. Analysis of Variance
6. Correlation
7. Simple Linear Regression
8. Multiple Linear Regression

Complete outline

This outline is for the typical 3-day Applied Statistics seminar. Variations/additions/deletions can be made as appropriate.

1. Probability Distributions

probability concepts
random variables
mean, variance, and standard deviation
normal distribution
standard normal
standardizing normal distributions
distribution of sample mean

2. Estimation

estimates of center and spread
coefficient of variation
standard error
estimation using computer programs

3. One-Sample Tests

hypothesis testing logic
Z and t tests on a single mean
t distribution
one-sided and two-sided tests
type I & type II errors
power
sample size effects
p-values
one-sample tests using computer programs

4. Two-Sample Tests

pooled and approximate t tests for comparing two means
testing for equality of variances
inferring causality
two-sample tests using computer programs

5. Analysis of Variance

experimental framework and hypotheses
graphical representation
partitions of sums of squares and degrees of freedom
F distribution
coefficient of determination
multiple range procedures
analysis of variance using computer programs

6. Correlation

categorical data
chi-square test for association
chi-square distribution
Pearson's correlation
scale invariance
outliers
causality
chi-square test and correlation using computer programs

7. Simple Linear Regression

statistical model
graphical representation
least squares estimation
residual plot
tests on intercept and slope
analysis of variance approach
R-square and adjusted R-square
outlier and influence detection
simple linear regression using computer programs

8. Multiple Linear Regression

general linear model
least squares estimation
testing the full model
multicollinearity
collinearity diagnostics
partial tests
model evaluation criteria
stepwise regression routines
cross validation
multiple linear regression using computer programs