Introduction to Statistical Modeling

# Introduction to Statistical Modelingtaught by Daniel Kaplan

Aim of Course:

To provide a solid introduction to the ideas and techniques of statistical modeling. Once you have completed this online course, "Introduction to Statistical Modeling" 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.

Course Program:

## WEEK 1: What is a Statistical Model?

• Explanatory and response variables
• Model terms
• Fitting models to data
• Introduction to R software

## WEEK 2: The Logic Behind Models

• 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

## WEEK 3: Randomness and Models

• Geometry of multiple explanatory variables
• Random walks and random directions
• Confidence intervals
• Collinearity

## WEEK 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.

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

# Introduction to Statistical Modeling

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.
Level:
Introductory / Intermediate
Prerequisite:
You should be familiar with introductory statistics.  Try these self tests to check your knowledge.You should also be comfortable interpreting linear formulas (y = ax + b) in terms of slope and rates of change.
Organization of the 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.

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.

Time Requirement:

Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. Certificate - 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. CEUs and/or proof of completion - 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,  CEU's and a record of course completion will be issued by The Institute, upon request.
4. Other options - Statistics.com Specializations, INFORMS CAP recognition, and academic (college) credit are available for some Statistics.com courses
Course Text:

Statistical Modeling: A Fresh Approach, Third Edition by Daniel T. Kaplan. This new edition is due to be published December 2016.

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.
Instructor(s):

Dates:

April 28, 2017 to May 26, 2017 October 27, 2017 to November 24, 2017 April 27, 2018 to May 25, 2018

# Introduction to Statistical Modeling

Instructor(s):

Dates:
April 28, 2017 to May 26, 2017 October 27, 2017 to November 24, 2017 April 27, 2018 to May 25, 2018

Course Fee: \$549

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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