Introduction to Statistical Modeling

Dr. Daniel T. Kaplan

Aim of Course:

To provide 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.

Who Should Take This Course:

Students who are planning to take Regression and other modeling courses at statistics.com. 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:

The course is structured as follows

The Instructor:

Dr. Daniel T. Kaplan is DeWitt Wallace professor in the Mathematics, Statistics, and Computer Science Department at Macalester College in Saint Paul, Minnesota where he teaches statistics, applied mathematics, and computational science. He is the author of Statistical Modeling: A Fresh Approach, as well Introduction to Scientific Computation and Programming, Understanding Nonlinear Dynamics, and Resampling Statistics in Matlab. He won the annual Excellence in Teaching award at Macalester in 2006 and has received curriculum development grants for this approach to statistical modeling from the Howard Hughes Medical Institute, the Keck Foundation, and the U.S. National Science Foundation.

Organization of the Course:

The course takes place over the internet, at statistics.com. 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 is scheduled to take place over 4 weeks, and 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 and work through exercises. Discussion among participants is encouraged. The instructor will provide answers and comments.

Certificates and Grades:

You may be interested only in learning the material presented, and not be concerned with grades or certificates. Or you may be enrolled in a statistics.com Program in Advanced Statistical Studies that requires demonstration of proficiency in the subject, in which case your work will be assessed for purposes of issuing a grade. Or you may require only a "Certificate of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). As you begin the class, you will be asked to specify your category.

Credit:

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a certificate will be issued by statistics.com, upon request.

Dates:

Jul. 16 - Aug. 20, 2010
Click here to be notified of future course offerings.

Participants gain access to the online materials on the first day of the course, and typically spend about 15 hours per week (at their convenience). You retain full access to course materials, including discussion board, for two weeks after the course closing date.

Level:

Introductory/Intermediate

Prerequisite:

The equivalent of Introduction to Statistics 1: Inference for a Single Variable, and Introduction to Statistics 2: Working with Bivariate Data (and, if necessary before these courses, Introduction to Statistics for Beginners or Survey of Statistics for Beginners).

For additional information about course prerequisites, click here.

Knowledge of statistics at the level of Intro Stats 2 or 3, e.g., understanding what a confidence interval is and what a p-value means. You should be comfortable interpreting linear formulas (y = ax + b) in terms of slope and rates of change, e.g., you have had at some point a calculus course even if you don't remember the details.

Course Text:

Statistical Modeling: A Fresh Approach by Daniel T. Kaplan. It can be ordered directly from the publisher here or from Amazon here.

Software:

You will need statistical software to fit models and perform other calculations on data and model results. As part of the course, there is a brief introduction to the powerful (but free!) statistical software package R. If you do not have a preferred statistical software program already, R would be a good choice for this course. Exercises and materials will be provided to help you become proficient in R with roughly 3 to 5 hours work.

If you are planning to use software other than R, you should be familiar with standard introductory-level computations, e.g., reading in a spreadsheet data file, plotting data, making tables of counts, etc. In other words, the sorts of computations that are encountered in an introductory course. You will also need to be able to perform computations relating to fitting and interpreting models. These are commonly available in statistics packages: Click here to learn what other you will be encountering in this course. Limited help will be available from teaching assistants for these operations in some programs other than R, but you will not have as much support as with R.

Software operations to expect in Introduction to Modeling:

1. Specifying the variables and terms to be included in linear models, fitting those models, and making a standard regression report.
2. Getting an analysis of variance or analysis of covariance report from a linear model. (This is related to, but not the same thing as performing one-way analysis of variance.)
3. Calculations on the models themselves, e.g., extraction of fitted values and residuals, calculation of R^2.

Registration:

Register Online - $499
Register Online (academic) - $399 (you must be affiliated with a college, university or high school)

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Note: Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

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