Statistical Computing II

Course Description

The aim of this course is to introduce students to essential concepts and tools required for the management and analysis of data using modern and open source statistical software. Data manipulation, descriptive analyses, importing and exporting data, R objects and R functions, and interpretation are introduced using R statistical software. The course will develop basic skills in the use of an open source statistical package R through classroom demonstrations and independent lab assignments. The course will emphasize data definition, descriptive and inferential statistics and graphical presentation.

The course also aims to teach students to use the SAS statistical programming language and to apply this knowledge appropriately in a variety of settings. Student achievement in the course will rely heavily on performing computational tasks, data management, editing data, running basic statistical procedures, and producing reports using SAS.

This is a data analysis course that shows how to use the statistical packages SAS and R to help solve both simple and complex real-life data problems including one-, two-, and k-sample statistical problems. Basic concepts include data preparation, modification, analysis, and interpretation of results. 

Course objectives

The aim of this course is to provide students with the knowledge and skills required to undertake moderate to high level data manipulation and management in preparation for statistical analysis of data. Specific objectives are for students to:

  •  Gain experience in data manipulation and management using two major statistical software packages (R and SAS)
  • Learn how to display and summarize data using statistical software
  • Become familiar with basic statistical data analysis using statistical software packages (R and SAS)
  • Acquire fundamental programming skills for efficient use of software packages
  •  communicate the findings of a statistical analysis in a clear, concise, and scientific manner


Current Topics

Objectives of the Course:-

  • to do research on various Statistics and related fields topics and thoroughly explore all sides of the issues
  • to consider a variety of opinions and perspectives on controversial and complex topics
  • to have defend a position, extensive class participation and discussion
  • to add relevance to past and current studies as well as to promote global awareness and create an educated citizenry

    • Learning Outcomes: This course supports the achievement of the following outcomes:

    • Ability to apply knowledge of advanced statistical theories/methods/techniques, software skills and research methods to the analysis of statistical data, to design/fit different models and solving different problems
    • Ability to communicate clearly and use the appropriate medium, including written, oral, and electronic methods.
    • Ability to learn new subjects that are required to solve problems in statistics and different related fields without being dependent on a classroom environment.
    • Ability to maintain life-long learning and continue to be motivated to learn new subjects or be admitted to an excellent Ph.D. program.


    Multivariate Statistical Methods

    Multivariate Statistical techniques are important tools of analysis in all fields of management: Finance, Production, Accounting, Marketing, and Personnel Management. Besides, they play key roles in the fundamental disciplines of Social Science: Economics, Psychology, and Sociology. This course is designed to provide students with a working knowledge of the basic concepts underlying the most important multivariate techniques, with an overview of actual applications in various fields, and with experience in actually using such techniques on a problem of their choosing. The course will address; Introduction, Bivariate, and Multivariate Distributions, Inference about mean vector, Comparisons of Several Multivariate Means, Multivariate Multiple Linear Regression Model, Principal Components Analysis, Factor Analysis, Discriminate Analysis, Cluster Analysis, Canonical Correlation Analysis


    Current Topics

    Seminar in Current Affairs is an advanced study/research and discussion-based course that will provide students with an opportunity to explore broad and ever-changing topics of current interests in Statistics and related fields. The course will in part consist of an intensive study of selected papers from the current literature. The participants will be expected to prepare bibliographies and present oral and/or written reports.


    Applied Regression Analysis


    The course covers the basics of simple linear regression: parameter estimation and model fitting; model checking (R-square, residual plot an PP- plot); prediction; inference about parameters; linear correlation and inference about correlation coefficient; The multiple linear regression: model assumptions, model fitting; R-square; partial correlation coefficients;model diagnostics, partitioning sum of squares, ANOVA table construction, test of hypoth esis, prediction, dummy variables; effects of departures from model assumptions; model building strategy, polynomial regressions.


    Survival Data analysis

    Introduction to Survival data, Non parametric inference, Comparison of survival curves, Survival models, Parametric inference, Binomial and Poisson models for discrete data, Proportional Hazard model, Markov Models, Rank tests with censored data, Survival data with competing risks.