Introduction to Statistical Modeling

taught by Danny Kaplan

Brief Description:

This course provides a solid introduction to the ideas and techniques of statistical modeling.

Instructor(s):
Level: Introductory / Intermediate

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.

Dates:
January 04, 2013 to February 01, 2013
modelingintro Click here to be reminded of future sessions of this course.

Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

Register Online -$499
Register Online -$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.

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. Multiple course registrations may be entitled to tuition discounts; read more.

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Introduction to Statistical Modeling

taught by Danny 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.

This course is a core requirement or elective in the following Program(s) in Analytics and Statistical Studies (PASS):

• Biostatistics - Epidemiology

• Business Analytics

• Environmental Science

• Social Science

Prerequisite(s):

If you are unclear as to whether you have mastered the requirements, try these placement tests here.

Knowledge of statistics at the level of Introduction to Statistics 1 and 2, 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.
Course Program:

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

HOMEWORK:

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.

Organization of the Course:

This course takes place over the internet 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.

Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

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:

There are two approaches for software in this course:

(1)  Use the R statistical software program, which is free.  Its use is illustrated in the course, and exercises and materials will be provided to help you become proficient in R with roughly 3 to 5 hours additional work. Warning: The first week of the course has a comparatively heavy workload of regular course material, so if you need to learn R, be sure to appropriately budget your time.

(2)  If you are familiar with, or have a strong preference for, a standard statistical software package, you can use the linear modeling capabilities of that package (supplemented, if you wish, by a spreadsheet).  Click here for information on standard statistical software packages.  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., as well as computations relating to fitting and interpreting models. 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.

Register Now

Yes, I want to register for:

Introduction to Statistical Modeling

taught by Danny Kaplan

Instructor(s):
Dates:
January 04, 2013 to February 01, 2013
Course Fee: $499
Academic Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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