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
|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|
|Likelihood Theory I & II||00:20:00|
|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|
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