##### I used to teach following courses, click on respective course button for details

Biostatistics: Community Medicine Introduction to Biostatistics provides an introduction to selected important topics in biostatistical concepts and reasoning. This course represents an introduction to the field and provides a survey of data and data types. Specific topics include tools for describing central tendency and variability in data; methods for performing inference on population means and proportions via sample data; statistical hypothesis testing and its application to group comparisons; issues of power and sample size in study designs; and random sample and other study types. While there are some formulae and computational elements to the course, the emphasis is on interpretation and concepts.

# Biostatistics: Community Medicine Module

Introduction to Biostatistics provides an introduction to selected important topics in biostatistical concepts and reasoning. This course represents an introduction to the field and provides a survey of data and data types. Specific topics include tools for describing central tendency and variability in data; methods for performing inference on population means and proportions via sample data; statistical hypothesis testing and its application to group comparisons; issues of power and sample size in study designs; and random sample and other study types. While there are some formulae and computational elements to the course, the emphasis is on interpretation and concepts.

Assignments:  (Click here to submit Assignment)

Case Studies:

Data Sets:

Recommended Books:

Daniel, W. W., & Cross, C. L. (2014). Biostatistics: A foundation for analysis in the health sciences (10th ed.). Singapore: John wily & sons.

Field, A. P. (2013). Discovering statistics using IBM SPSS statistics (4th ed.) Sage Publishers

#### Advanced Applied Statistics 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.

# Advanced Applied Statistics

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, Tables and Graphs Do File

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

Advanced Regression Methods Do File

Lowess Smoothing
Robust Regression
Further rreg and qreg Applications
Nonlinear Regression – 1
Nonlinear Regression – 2
Box-Cox Regression
Multiple Imputation of Missing Values

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
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

Introduction to Programming Do File

Basic Concepts and Tools Matrix Programming

Assignments

Sample

[Assignment 1]

Assignment 2

[Assignment 3]

[Assignment 4]

# Case Studies

Employee Database

Customer Database

breasks 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

#### Categorical Data Analysis The main ideas of the course are to develop a critical approach to the analysis of categorical data often encountered in health sciences research. This process will include gaining some technical insight (mechanics of the statistical methodology behind the ideas) as well as applications of these methods in health-related data.

# Categorical Data Analysis

The main ideas of the course are to develop a critical approach to the analysis of categorical data often encountered in health sciences research. This process will include gaining some technical insight (mechanics of the statistical methodology behind the ideas) as well as applications of these methods in health-related data. Some of the main objectives of this course are:

• To develop an intuitive and critical approach to the analysis of frequency tables.
• To examine basic ideas and methods of generalized linear models (e.g. Logistic regression, Multinomial Logistic Regression, Ordinal Logistic Regression and Log Linear Models)
• To gain experience in categorical data analysis using statistical software packages (SPSS/Open Epi,etc.)

# Quick review:

Some of the basic sampling techniques, variable types, probability distributions relevant in our course (binomial, multinomial, Poisson, etc.), expectation, concept of likelihood, tests for one-way tables

Rate and Ratios Formulas

Introduction to generalized linear models: Logistic regression, interpretation of coefficients, model selection, diagnostics, goodness of fit. Introduction of multinomial regression, polytomous regression, poisson regression.

Loglinear models: for multi-way tables

Special topics:

# Books:

Agresti, Alan (2002) Categorical Data Analysis, Second Edition, Willey

Agresti, Alan (2007) An Introdiction to Categorical Data Analysis, Welley

Hosmer, D.W. and Lemeshow, S. (2000) Applied Logistic Regression, Second Edition, Wiley

#### Survival Analysis The main ideas of the course are to develop a critical approach to the analysis of survival data often encountered in health and actuarial sciences research. This process will include gaining some technical insight (mechanics of the statistical methodology behind the ideas) as well as applications of these methods in survival time related data. One of the main objective of this course isto gain experience in survival data analysis using statistical software packages (MS Excel,SPSS, STATA and Mathematica etc.)

# Survival Analysis – Models

The main ideas of the course are to develop a critical approach to the analysis of survival data often encountered in health and actuarial sciences research. This process will include gaining some technical insight (mechanics of the statistical methodology behind the ideas) as well as applications of these methods in survival time related data. One of the main objective of this course isto gain experience in survival data analysis using statistical software packages (MS Excel,SPSS, STATA and Mathematica etc.)

Introduction to Survival Analaysis

• Recognize or describe the type of problem addressed by a survival analysis.
• Define what is meant by censored data.
• Give reasons why data may be censored.
• Define survivor function.
• Define a hazard function.
• Describe the relationship between a survivor function and a hazard function.
• Identify the basic data layout for the computer; in particular, put a given set of survival data into this layout.
• Construction of actuarial life tables

Kaplan-Meier Survival Curves and the Log-Rank Test

• Compute Kaplan-Meier probabilities of survival, given survival time and failure status information on a sample of subjects.
• Interpret a graph of KM curves that compare two or more groups
• Draw conclusions as the whether or not two or more survival curves are the same based on

Log-Rank Test

• Computer results that provide a log-rank test.
• Draw conclusions as to whether or not two or more survival curves are the same based on computer results that provide a breslow test
• Decide whether the log-rank test or the breslow test is more appropriate for a given set of survival data.

The Cox Proportional Hazard Model and its Characteristics

• State the general form of the Cox proportional hazard model.
• State specific form of a Cox PH model appropriate for analysis, given a survival analysis scenario involving one or more explanatory variables.
• State or recognize the form and properties of the baseline hazard function in the Cox PH model.
• Give at least three reasons for the popularity of the Cox PH model.
• State the meaning of the PH assumption
• Given a computer printout involving one or more fitted Cox PH models;
• Compute any hazard ratio of interest
• Carry out and interpret a designated test of hypothesis
• Evaluate interaction and confounding involving one or more covariates.

Evaluating the Proportional Hazard Assumption

• State three general approaches for evaluating the PH assumption.
• Summarize how log-log survival curves may be used to assess the PH assumption.
• Summarize how observed versus expected plots may be used to assess the PH assumption.
• Summarize how GOF tests may be used to assess the PH assumption.
• Describe given survival data or computer output form a survival analysis that uses a Cox PH model, how to assess the PH assumption for one or more variables in the model using
• Graphical approach
• The GOF approach
• An extended Cox model with time dependent covariates

The Stratified Cox Procedure

• Explain a computer printout for a stratified Cox procedure.
• State the hazard form of a stratified Cox model for a given survival analysis scenario and /or a given set of computer results for such a model
• Evaluate the effect of a predictor of interest base on computer results form a stratified Cox procedure.
• For a given survival analysis scenario and/or a given set of computer results involving a stratified Cox model
• State the no-interaction assumption for the given model
• Describe and/or carry out a test of the no-interaction assumption
• Describe and/or carry out an analysis when the no-interaction assumption is not satisfied.

Extension of the Cox Proportional Hazard Model for the Time-Dependent Variables

• State the general form of the Cox model extended for the time dependent variables.
• State the specific form of an extended Cox model appropriate for the analysis, given a survival analysis scenario involving one or more time-dependent variables.
• State the formula for a designated hazard ratio of interest, given scenario describing a survival analysis using an extended Cox model.
• State the formula for an extended Cox model that provides a method for checking the PH assumption for one more of the time independent variables in the model, given a scenario describing a survival analysis.
• State the formula for the hazard ratio during different time interval categories specified by the heavy side functions.

Parametric Survival Analysis Exponential Distribution](https://www.dropbox.com/s/2gvdoq69e3vj4e6/Parametric%20Survival%20Analysis.pdf?dl=0)

• Weibul Distribution
• Gamma Fitting
• Exponetial Fitting

Case Studies: Breast Cancer Survival Heart Transplant

# Recomended Books:

• Kleinbaum, D. (2002). Survival Data Analysis: A self learning text, Second Edition, Springer Varlag
• Elisa T. Lee (1998) Introduction to Survival time Data, Second Edition, Wiley.

#### Statistical Inference This course deals with the proper use of statistical tools for the analysis of management data. The course covers basic and advanced level statistical techniques. The core objective is to enable students to apply suitable statistical techniques using various statistical packages like IBM SPSS Statistics, STATA, SAS, Minitab and JMP

# Statistical Inference

Course Outline

This course deals with the proper use of statistical tools for the analysis of management data. The course covers basic and advanced level statistical techniques. The core objective is to enable students to apply suitable statistical technique to accomplish the underlying objectives.

#Assignments

Assignment 1

Assignment 2

Assignment 3

Assignment 4

# Case Studies

Employee Database

Customer Database

breasks Database

Credit Cards

Comparing Groups

Bank Loan

MAINTENANCE

# Project

Employee Satisfaction Survey

# Books

• David. S. Moore and George P. McCabe (2003), The Practice of Statistics for Business & Economics Freeman Publishers.
• Landau, Sabine. (2004) “A handbook of statistical analyses using SPSS”, Chapman & Hall/CRC Press LLC
• Vijay G. (2002), Statistical Analysis with Excel, VJ Books.
• Wayne L. Winston (2007), Data Analysis and Business Modeling, Microsoft Press

#### Statistics for Managers Statistics is a field study concerned with gathering, transforming and analyzing data for the purpose of making intelligent statements and drawing appropriate decisions. This course has been designed for Business students so that they can apply their knowledge of Statistics in many practical situations. The purpose of this course is to provide you with a strong background in statistical principles. Our emphasis will be on learning how to be an intelligent “consumer” of statistics, rather than on how to become a statistician.

# Statistics for Managers

Statistics is a field study concerned with gathering, transforming and analyzing data for the purpose of making intelligent statements and drawing appropriate decisions. This course has been designed for Business students so that they can apply their knowledge of Statistics in many practical situations. The purpose of this course is to provide you with a strong background in statistical principles. Our emphasis will be on learning how to be an intelligent “consumer” of statistics, rather than on how to become a statistician.

Topiscs to be covered:

• Understand how statistics is used in business
• Understand the different measurement scales
• Organize numerical data
• Develop tables and charts for numerical data
• Develop tables and charts for categorical data
• Understand the principles of proper graphical presentation
• Describe the properties of central tendency, variation, and shape of data
• Construct and interpret a box-and-whisker plot
• Calculate descriptive summary measures from a population
• Application through SPSS
• Understand basic probability concepts
• Understand the basic principles of hypothesis testing
• Use of hypothesis testing to test a mean
• Use of hypothesis testing for comparing the difference between the means
• Use of hypothesis testing for related samples
• Use of one-way analysis of variance to test for differences among the means of several groups
• Pearsons Correlation
• Simple Linear Regression
• Multiple Linear Regression
• Chi-Square test of Association

###Assignments

Assignment 1

Assignment 2

Assignment 3

# Case Studies

Employee Database

Customer Database

breasks Database

Credit Cards

Comparing Groups

Bank Loan

# Project

Employee Satisfaction Survey

# Reference Books

• Even you can learn Statistics, 2nd Edition, by David M. Levine and David F. Stephan
• Statistics for Managers, 4th Edition by Levine, Stephan and Berenson.
• Middleton, Decision Analysis Using Microsoft Excel, April 2007
• Middleton, Data Analysis Using Microsoft Excel: Updated for Office XP
• Introduction to Practice of Business Statistics, 6th Edition, by David Moore
• Statistics for Business and Economics, 7th Edition, by McClave, Benson and Sincich

#### Quantitative Techniques 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.

# Quantitative Techniques

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.

#Assignments

Assignment 1

Assignment 2

Assignment 3

Assignment 4

# Case Studies

Employee Database

Customer Database

breasks Database

Credit Cards

Comparing Groups

Bank Loan

MAINTENANCE

Job Satisfaction

# Project

Employee Satisfaction Survey