Short Courses
Short Courses are taught in a single, two-hour session.
Below are the syllabi of the Short Courses currently offered.
Introduction to data analysis using the SAS software package
I. The Data Step
A. Reading ASCII Data
1. Column Style
2. Free Style
3. Formatted
B. Reading from non-ASCII Data
1. Excel
2. dBase
C. Creating new variables from existing variables
D. Creating new data sets from old data sets
1. Set
2. Merge
II. The Procedure Step
A. Proc Sort (data sorting)
B. Proc print (printing your data)
C. Proc means (descriptive statistics)
D. Proc univariated (descriptive statistics)
E. Proc freq (frequencies)
F. Proc corr (correlations)
G. Proc plot (graphics)
H. Proc reg (regression)
I. Proc GLM (ANOVA)
J. Proc TTest (T-Test)
Introduction to data analysis using the SPSS software package
I. Entering Data
A. Defining variables and entering data directly
B. Importing from a database
1. Excel
2. dBase
II. Data
A. Sorting
B. Selecting
C. Merging
III. Transform
A. Computing new variables from old
B. Recoding variables
IV. Analyze
A. Descriptive statistics
B. Compare means
C. Correlation
D. Regression
V. Graphs
A. Scatter plots
Regression analysis using the SAS software package
I. What is linear regression?
A. Model Structure
B. Model Assumptions
1. Linear in the parameters
2. X's are known, non-random, constants
3. Errors
a. homoscedasticity
b. uncorrelated
c. mean=0
d. normal
II. Preliminary steps before fitting the model
A. Choosing a pool of potential independent variables for consideration in the model
B. Scatter plots
III. Fitting the model using PROC REG
A. Testing model fit and assumptions using residual plots
1. Scatter about zero
2. normal probability plots
3. partial regression plots (PARTIAL)
B. ANOVA table
1. Over-all F-test
2. Mean square error (MSE)
3. R-squared
C. Least squares parameter estimates with standard errors
1. Significance tests
a. Individual tests
b. Family-wise tests (Bonferroni)
2. Implications of multicollinearity
a. Variance Inflation Factor (VIF)
b. Polynomial models
c. Ridge regression
i. trade off between biased parameter estimates and smaller variance
D. Predicted values (P)
1. As conditional mean with confidence limits (CLM)
2. As new value estimate with confidence limits (CLI)
E. Outlier detection
1. Identifying X outliers - Hat Matrix Leverage Values
2. Identifying Y outliers - Studentized Deleted Residuals
3. Identifying influential cases (INFLUENCE)
a. DFFITS
b. DFBETAS
c. Cook's Distance (R)
F. Automated model selection procedures
1. SELECTION=RSQUARE
2. SELECTION=STEPWISE (SLE) (SLS)
3. SELECTION=FORWARD
4. SELECTION=BACKWARD
Regression analysis using the SPSS software package
I. What is linear regression?
A. Model Structure
B. Model Assumptions
1. Linear in the parameters
2. X's are known, non-random, constants
3. Errors
a. homoscedasticity
b. uncorrelated
c. mean=0
d. normal
II. Preliminary steps before fitting the model
A. Choosing a pool of potential independent variables for consideration in the model
B. Scatter Plots (Graphs...Scatter)
III. Fitting the model (Analyze ...Regression...Linear)
A. Method
1. Enter
2. Stepwise
3. Backward
4. Forward
B. ANOVA table
1. Over-all F-Test
2. Mean square error (MSE)
3. R-squared
C. Statistics
1. Estimates of parameters
a. Significance tests
i. individual
ii. family wise (Bonferroni)
b. SE
2. Confidence intervals for parameters
3. Descriptives
4. Collinearity
a. tolerance
b. variance inflation factor (VIF)
c. eigenvalues
d. condition index
e. variance proportions
D. Save
1. Predicted values
a. SE of mean predicted values
b. Prediction intervals
i. mean
ii. individual
2. Residuals
a. unstandarized
b. standardized
c. studentized
d. deleted
e. studentized deleted
3. Distances
a. Cook's D
b. Leverage value
4. Influence statistics
a. DfBetas
b. DfFit
c. Covariance ratio
E. Descriptive statistics
1. Residual plots
2. Partial regression plots