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   Courses> Modeling With R 

Modeling With R 

Begins April 4th 2014

Aim of the Course: This 3-week course will show you how to use R to create models for use in classification and prediction. You will be introduced to advanced graphing methods as needed. Modeling techniques include OLS, LAD, and EIV regression, quantile regression, and decision trees (CART).  Model validation is emphasized.

Who Should Take This Course:
Anyone familiar with R who encounters statistics in their work and wishes to program their own procedures in a convenient, widely-used, open source (free) language. 

Instructor:
Dr. Phillip Good, former Calloway Professor of Computer Science at the University of Georgia (Fort Valley) and graduate of the program in mathematical statistics at UC Berkeley, is the author of Introduction to Statistics via Resampling Methods and R (2nd ed, Wiley, 2013), Common Errors in Statistics (and How to Avoid Them) (4th edition, Wiley, 2012 with James Hardin), Resampling Methods (Birkhauser, 2nd ed, 2005), Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer, 3rd ed, 2004), Manager's Guide to Design and Conduct of Clinical Trials (Wiley, 2nd ed. 2005), The A-Z of Error-Free Research Using R (CRC, 2012), and Applying Statistics in the Courtroom (CRC, 2001). He has given tutorials at the Joint Statistical Meetings (U.S.) and Deming Conference, lectured in Australia, Belgium, Bulgaria, France, Holland, Ireland, Slovenia, and Spain, and was twice a traveling lecturer for the American Statistical Association. This is his seventh (8th) year of providing on-line interactive courses. 

Prerequisite: Prerequisite: You should have familiarity with basic statistical concepts or the equivalent.  You should have some familiarity with R as in our course Introduction to R.  (And will get a $50 discount if you sign up for "Introduction" as well as "Modeling with R".)

Organization of the Course: The course takes place over the Internet.  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 an alias and access to a private bulletin board that serves as a forum for discussion of ideas, problem solving, and interaction with the instructor. The course is scheduled to take place over three weeks. Estimated weekly time requirements for this course - an hour and half for the lecture, an hour and a half for preparation, and another three hours for homework and review.  At the beginning of each week, participants receive the relevant material, in addition to answers to exercises from the previous session. During the week, participants are expected to go over the course materials and work through exercises. Discussion among participants is encouraged. The instructor will provide answers and comments.

Optional Text: Participants may wish to purchase and make use of The A-Z of Error-Free Research Using R (CRC, 2012; Chapter 12-14). Also available as an ebook.

  1. Session I: Linear Regression and Advanced Graphics

    Ordinary Least Squares

    Interpretation of Output

    Plotting residuals, Plots with multiple lines, Side-by-side plots

    Stepwise Regression

  2. Session 2: Alternatives to OLS Regression

    Deming Regression

    LAD

    Quantile Regression

  3. Session 3: Decision Trees

    Construction, Pruning, Prediction

    Validation Methods

        Trees vs. Regression

        Which Modeling Technique?

One week is allowed after Session 3 to give participants the opportunity to clarify any questions arising from this or previous sessions.

Full cost of course is $229.  Early-bird discount may apply. Students, faculty and research workers at academic institutions are eligible for a discount of $50.  Just send an email to courses@statcourse.com from your academic email account to receive a discount coupon.

Immediately after your payment is credited, you will receive an email giving you a password, sign up instructions, and the web address (URL) of the course material.  Note that you will not be able to access this address until the start date of the course.  

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Refund Policy: Up until one-week prior to the start of a course, a refund of the fee paid less $35 will be provided on request.  Although there are no refunds after that date, a course participant may enroll without charge the next time the course is offered.

 
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