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This course provides students with the basic tools for analyzing experimental data, properly interpreting statistical reports in the literature, and reasoning under uncertain situations. Topics organized around three key theories: Probability, statistical, and the linear model. It also covers axioms of probability, discrete and continuous probability models, Bayesian methods as well as hypothesis testing, elementary design of experiments principles and goodness-of-fit.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: MIT

Course Curriculum

Conditional Probability, Bayes’ Rule and Independence 00:20:00
Discrete Probability Models 00:25:00
Continuous Probability Models I & II 00:25:00
Joint Distributions and Independent Random Variables 00:20:00
Conditional Distributions and Functions of Jointly Distributed Random Variables I & II 00:25:00
Moment Generating Functions I & II 00:25:00
The Law of Large Numbers and the Central Limit Theorem 00:20:00
Method-of-Moments Estimation 00:20:00
Likelihood Theory I & II 00:20:00
Bayesian Methods 00:20:00
Corrigienda 00:05:00
Bootstrap and Monte Carlo Methods 00:20:00
Hypothesis Testing I & II 00:30:00
Simple Regression Model I, II & III 00:20:00
Analysis of Variance 00:15:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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