Objectives: The objectives of this course are designed for experienced SAS users who are seeking for positions similar to the following: 1.biostatistician, data analyst or programmer position in biomedical and health care fields, such as hospitals, pharmaceutical companies, health care research units, etc. 2. programming analyst in bank industry. 3. modeling specialist in telecommunication industry.

Topics: This course content covers two major parts: basic theory and projects. The basic theory covers ten major topics on statistics and modeling. The projects parts contain three real world projects from three industries: health care, bank and telecommunication. The health care project introduces how to use SAS to deal with the research questions in biomedical and health studies. The bank project covers the data processing and modeling in bank industry. The telecommunication project introduces the predictive modeling for business risk.

Goal: Upon the completion of this course, you will be acknowledge of the industry real world stories on SAS and data analysis/modeling, and independently solve the problems.

Requirements: basic SAS programming skills and understanding of basic Statistical concepts

Theory

1. Introduction
. Sample and Population
. Distribution
. Statistical Inference
. SAS tool

2. Descriptive Statistics
. Describing data
. Frequency distribution
. Creating summary data with proc means and proc univariate
. Plotting data
. SAS tools

3. Analyzing Categorical Data
. Chi-square test
. Exact tests
. Mantel-Haenszel test
. McNemar’s test
. Odds ratios
. Relative Risk
. SAS tools

4. T-tests and Nonparametric Comparisons
. T-test: Testing differences between two means
. Paired T-tests
. One-tailed versus Two-tailed tests
. Nonparametric methods
. SAS tools

5. Analysis of Variance
. Introduction
. One-way analysis of Variance
. Computing contrast
. N-way Factorial Designs
. Interpreting significant interactions
. SAS tools

6. General Linear Regression
. Introduction
. Examining data
. Transforming variables
. Simple linear regression
. Multiple covariates regression
. SAS tools

7. Logistic Regression
. Introduction
. Dichotomous response
. Polytomous response
. Exact methods in Logistic Regression
. SAS tools

8. Poisson Regression
. Introduction
. Methodology for Poisson Regression
. Simple Poisson Counts Example
. SAS tools

9. Model Selection
. Theory
. Variables selection
. Dealing the missing values
. SAS tools

Projects I: Health Care

Project1. Investigating whether or not a drug is doing a better job than placebo in treating a group of patients with heart disease
Objective: To learn how to analyze categorical data, understand the difference between Odd Ratio (OR) and Relative Risk (RR)

Project2. Using a graph to show the relationship between amount and strength of papers
Objective: To learn how to produce a graph using SAS and use graph to detect some preliminary relationships among variables being analyzed

Project3. Comparing a new drug with an approved drug and placebo for treating high-blood pressure patients in a clinical trial
Objective: To learn how to test differences between two means using different methods such as T-test, paired T-tests
and nonparametric methods, also learn how to randomly assign subjects to different study groups

Project4. Comparing the ability of three groups of subjects to remember briefly-presented chess positions
Objective: To learn how to analyze the variance and design an experiment

Project5. Investigating how a tumor distributed in USA, is there any age or sex difference in this disease?
Objective: To learn how to analyze population data, some epidemiology notations and Poisson regression

Project6. Analyzing on data of low birth weight
Objective: To learn how to prepare a data, conduct a logistic regression, and write a summary for the results

Project7. Monitoring the CD4+ counts in HIV patients to help their treatments
Objective: You will be very confident on how to explore, transfer and clean a dataset, conduct a model selection
and report your final model to team members

Project II: Banking

1) Data Warehouse: SQL codes
a. Overview Data Warehouse
b. Discussion of tables to be used in the course
c. Discussion various SQL
d. Program SQL to query

2) SAS on Various Platforms

a. Discussion SAS on PC, Unix, Mainframe
b. Expose to JCL
c. Hands on programming with SAS
d. SQL vs SAS programming

3) Executing Statistical Model in Production
a. Discussion on the post Model Procedure
b. Discussion on Unit Test
c. Hands on Unit Test
d. Discussion on Validation Test
e. Discussion on System Test

4) Introduction to SAS Macro

a. Discussion the importance of Macro
b. Hands on programming with Macro

Project III: Modeling

1) Background Introduction

a. Industry description and task features
b. Knowledge and skills requirement
c. Resume and interview tips

2) Project Introduction

a. Project description
b. Terminology and concepts
c. Modeling approach and theory

3) Data Processing

a. Data source and format
b. Data processing language
c. Data processing tools
d. SAS tools for data processing
e. Tips on data processing

4) Modeling

a. Modeling problem description
b. Mathematical/Statistical models applied
c. Available software for modeling
d. SAS tools for modeling
e. Tips on playing with numbers