 Course Outline

It is applied course in statistics that is designed to provide you with the concepts and methods of statistical analysis for decision making under uncertainties. This course is a combination of lectures and computer-based practice, joining theory firmly with practice. It introduces techniques for summarizing and presenting data, estimation, confidence intervals, hypothesis testing, modeling relationships and some multivariate techniques. The lectures focuses more on understanding of key concepts and statistical thinking, and less on formulas and calculations, which can now be done on statistical software.

Summary Statistics for Measurement Variables Exploratory Data Analysis Normality Tests and Transformations Frequency Tables and Two-Way Cross-Tabulations Multiple Tables and Multi-Way Cross-Tabulations Tables of Means, Medians and Other Summary Statistics Histograms Box Plots Scatterplots and Overlays Line Plots and Connected-Line Plots Other Twoway Plot Types Bar Charts and Pie Charts Symmetry and Quantile Plots

###ANOVA and Other Comparison Methods Do File Do File

One-Sample Tests
Two-Sample Tests One-Way Analysis of Variance (ANOVA) Two- and N-Way Analysis of Variance Factor Variables and Analysis of Covariance (ANCOVA) Predicted Values and Error-Bar Charts

Rate and Ratios Stratified Rate and Ratios Contingency Table Analysis of 2 X 2 Tables Contingency Table Analysis of 2 X 2 X K Tables Contingency Table Analysis of R X C Tables Contingency Tables Analysis of ordinal variables Contingency Tables Analysis of Matched Pair samples

###Linear Regression Analysis Do File crime Data elemapi2 Data Do File

Simple Regression
Correlation
Multiple Regression
Hypothesis Tests
Dummy Variables
Interaction Effects
Robust Estimates of Variance
Predicted Values and Residuals
Diagnosing Multicollinearity and Heteroskedasticity
Confidence Bands in Simple Regression
Diagnostic Graphs

Lowess Smoothing
Robust Regression
Further rreg and qreg Applications
Nonlinear Regression - 1
Nonlinear Regression - 2
Box�Cox Regression
Multiple Imputation of Missing Values
Structural Equation Modeling
Do File

###Logistic Regression

Using Logistic Regression
Marginal or Conditional Effects Plots
Diagnostic Statistics and Plots
Logistic Regression with Ordered-Category y
Multinomial Logistic Regression
Ordinal Logistic Regression

###Survival and Event-Count Models

Survival-Time Data
Count-Time Data
Kaplan�Meier Survivor Functions
Cox Proportional Hazard Models
Exponential and Weibull Regression
Poisson Regression

###Principal Component, Factor and Cluster Analysis

Principal Component Analysis and Principal Component Factoring
Rotation Factor Scores Principal Factoring Maximum-Likelihood Factoring Cluster Analysis � 1 Cluster Analysis � 2 Using Factor Scores in Regression Measurement and Structural Equation Models

###Time Series Analysis

Smoothing Further Time Plot Examples Recent Climate Change Lags, Lead and Differences Correlograms ARIMA Models ARMAX Models

###Multilevel and Mixed-Effects Modeling

Regression with Random Intercepts Random Intercepts and Slopes Multiple Random Slopes Nested Levels Repeated Measurements Cross-Sectional Time Series Mixed-Effects Logit Regression

###Survey Data

Declare Survey Data Design Weights Poststratification Weights Survey-Weighted Tables and Graphs

Basic Concepts and Tools Matrix Programming

#Assignments

Sample

[Assignment 1]

Assignment 2

[Assignment 3]

[Assignment 4]

# Case Studies

Employee Database

Customer Database

Credit Cards

Comparing Groups

Bank Loan

Datasets

Granite2011_6

Electricity

Student2

[Project]

# Books

• Sophia Rabe-Hesketh & Brian Everitt (2004), Handbook of Statistical Analysis using Stata, Chapman & Hall/CRC Press LLC

• Landau, Sabine. (2004) "A handbook of statistical analyses using SPSS", Chapman & Hall/CRC Press LLC

• Yosef Cohen and Jeremiah Y. Cohen (2008), "Statistics and Data With R, An applied approach through examples" Wiley