Probability Distributions

taught by Madhav Kulkarni

Brief Description:

This course cover statistical probability distributions. Participants will learn how to identify which distribution(s) reasonably fit given data, and to evaluate the fit.

Instructor(s):
Level: intermediate

Who Should Take This Course:

Anyone who models data or statistical processes.

Dates:
June 22, 2012 to July 20, 2012
distributions Click here to be reminded of future sessions of this course.

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

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Probability Distributions

taught by Madhav Kulkarni

Aim of Course:

This course cover statistical probability distributions, such as the Bernoulli distribution, uniform distribution, hypergeometric distribution, Poisson distribution, Normal distribution, exponential distribution, Gamma distribution, Weibull distribution, Student's t distribution, chi-square distribution, F-distribution. Participants will learn how to identify which distribution(s) reasonably fit given data, and to evaluate the fit.

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

• Engineering Statistics

• Using R

Prerequisite(s):

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

Some familiarity with calculus (see statistics.com's brief Calculus Review course) is helpful for a complete facility with the various distributions.

Course Program:

SESSION 1: Introduction to Random Experiment and Random Variables (Discrete and Continuous)

• Probability distribution
• Properties and applicability
• Bernouli and discrete uniform distribution

SESSION 2: Introduction of Distibutions

• Binominal
• Hypergeometric
• Poisson and geometric random variables
• Identification and application of these distributions

SESSION 3: Introduction of Distributions

• Normal
• Exponential
• Gamma
• Weibull
• Identification and application of these distributions

SESSION 4: Derived Distributions

• Introduction and applicability
• Student's distribution
• Chi-square distribution and F-distribution
• Importance and applications
• Identification and appropriate distribution

HOMEWORK:

Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and end of course project.

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:

Required text for this course is Introduction to Discrete Probability and Probability Distributions, First Edition by Madhav B. Kulkarni and Surendra B. Ghatpande, published 2007 by SIPE Academy, Publishers and Consultants, Nashik.  The text will be made available as a .pdf during the first lesson.

Software:

Any general statistical package can perform most of the operations called for in this course, which fall into two main categories: (1) generation, tabling and graphing of random variables from specified distributions, (2) calculating probabilities, assessing fit, and performing statistical tests. Also note that Excel can be used for much of the above, particularly when coupled with Resampling Stats for Excel. Finally, R functions are provided as part of the course; these can be used for most of the course work if you have some basic familiarity with R. Click here for download information for these and other software packages that offer free or nominal cost versions that may be used in statistics.com courses.

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Probability Distributions

taught by Madhav Kulkarni

Instructor(s):
Dates:
June 22, 2012 to July 20, 2012
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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