This course will teach you the derivation of maximum likelihood estimates and their properties.
This course will teach you the derivation of maximum likelihood estimates and their properties.
Maximum likelihood is a popular method of estimating population parameters from a sample. This course covers the derivation of maximum likelihood estimates (MLE) and their properties. It’s primary purpose is to provide a useful conceptual foundation for those contemplating taking statistical modeling courses, it is not to provide facility with MLE as a practical tool.
This is a two-week course.
Students who complete this course will study the derivation of maximum likelihood estimates, and their properties. You will understand the role that MLE plays in statistical models, and be able to assess both the advantages and disadvantages of using a maximum likelihood estimate in a particular situation.
Note: The primary purpose of this course is to provide a conceptual understanding of MLE as a building block in statistical modeling. It is not to provide facility with MLE as a practical tool.
Maximum likelihood estimation is used in many of the methods taught in Statistics.com’s intermediate and advanced courses, such as Survival Analysis, Categorical Data Analysis and Generalized Linear Models, to name a few. Students who need to understand the theory behind those methods should take this course first.
Mr. Kuber Deokar holds a Masters degree in Statistics from University of Pune, India, where he also taught undergraduate statistics. Mr. Deokar holds the position of Instructional Operations Supervisor at Statistics.com. He is responsible for coordination of Statistics.com online courses, and ensures seamless interactions between the management team, course instructors, teaching assistants, and students. He also serves as the senior teaching assistant and shares instructional responsibilities for several courses, and handles consultancy assignments, working from our office in Pune, India.
Basics of Estimation, What is a ML Estimator?
Properties and Applications of ML Estimators and Bonus Readings
No instruction.
No instruction.
Nov 24, 2023 to Dec 8, 2023
Apr 12, 2024 to Apr 26, 2024
Nov 22, 2024 to Dec 6, 2024
No classes scheduled at this time.
Familiarity with Calculus is required.
The courses listed below are prerequisites for enrollment in this course:
I need to know R to perform my job as I am a product manager for a software company that interacts with R. I am now able to understand R scripts and hopefully contribute some of my own. The instructor's videos were great. Just hearing his voice made it more personal. This was my first ever web based course and I really enjoyed it although I had to work hard.
This course takes place online at The Institute for 2 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.
Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.
Course materials will be provided by the instructor.
This course does not have a software requirement.
Access to one or more of these software packages- SAS, R, S-plus, Stata, Minitab, StatXact, LISREL, will enhance your experience with this course.
Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?”
Enrollment
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.
Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.
Group Rates
Contact us to get information on group rates.
Discounts
Academic affiliation? In most courses you are eligible for a discount at checkout.
New to Statistics.com? Click here for a special introductory discount code.
Invoice or Purchase Order
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.
This course is eligible for the following credit and recognition options:
No Credit
You may take this course without pursuing credit or a record of completion.
Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.
CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.
At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:
Chrome
Firefox
Safari
There is no additional information for this course.
