Friday, 2 May 2014

Introduction to Statistical Modeling

If you've had some statistics and feel that mathematical notation and formulas are not your friend, consider Dr. Vittorio Addona's online course Introduction to Statistical Modeling at

This course provides a solid introduction to the ideas and techniques of statistical modeling. Once you have completed this course, you will be able to construct and interpret linear statistical models involving multiple variables and co-variates, you will understand the implications of including or excluding explanatory variables, you will be able to conduct and interpret analysis of variance (ANOVA) and of covariance (ANCOVA), and you will have a solid theoretical foundation for understanding linear regression and experimental design. For more details please visit at

Who Should Take This Course:
Students who are planning to take regression and other modeling courses at Analysts or educators who need to work with multiple variables but are not comfortable with the standard formula- and linear-algebra based approach generally taken.

Course Program:
Course outline: The course is structured as follows

SESSION 1: What is a Statistical Model?
  • Explanatory and response variables
  • Model terms
  • Reading model formulas
  • Fitting models to data
  • Introduction to R software

SESSION 2: The Logic Behind Models
  • Statistical adjustment
  • Introduction to the geometry of model fitting:
    • case space vs variable space
    • variables as vectors
    • simple projection and least squares
    • the model triangle
  • Correlation as a measure of alignment

SESSION 3: Randomness and Models
  • Geometry of multiple explanatory variables
  • Random walks and random directions
  • Confidence intervals
  • Collinearity

SESSION 4: Inference and Models
  • The F statistic
  • Decomposing variance into parts
  • Ambiguities introduced by collinearity
  • The virtues of orthogonality

The homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and guided data modeling problems using software.

In addition to assigned readings, this course also has supplemental readings available online, and example software codes.

The Instructor, Dr. Vittorio Addona, teaches statistics at Macalaster College in Minnesota.  His research falls into three broad categories: survival analysis, sports statistics, and election audits.  His doctoral work was in survival analysis, a statistical field often employed in medical studies of time-to-event data. Vittorio teaches across the statistics curriculum: introductory modeling, probability, mathematical statistics, and an applied survival analysis course.

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

We, the Center for eLearning and Training (C-eLT), Pune, partner with and offer these courses to Indian participants at special prices payable in INR.

For India Registration and pricing, please visit us at

Call: 020 6680 0300 / 322


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