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To build models of complex socio-technical systems for improved system design and decision-making, you must have data and systems knowledge. If you want to solve problems related to the functions of random variables, Poisson processes, Markov processes etc. this [course_title] is for you to help where you can also be able to know classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques and so on.


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

Introduction and overview 00:15:00
3-door problem 00:05:00
Analyzing a probability problem 00:05:00
Broken stick problem 00:05:00
Pedestrian crossing problem 00:05:00
Random incidence: A major source of selection bias 00:15:00
Random incidence and more 00:10:00
Spatial models 00:15:00
Markov processes and their application to queueing, part 1 00:10:00
Markov processes and their application to queueing, part 2 00:15:00
Queueing and transitions: Sampling from distributions, Gauss 00:05:00
Derived distributions to statistics 00:15:00
The Queue Inference Engine and the psychology of queueing 00:10:00
Beyond the physics of queueing 00:15:00
The Weibull distribution and parameter estimation 00:15:00
Hypothesis testing 00:15:00
Descriptive statistics and statistical graphics 00:46:00
Regression 00:30:00
Analysis of variance, with discussion of Bayesian and frequentist statistics 00:15:00
Multiple regression 00:15:00
Design of experiments, part 1 00:15:00
Design of experiments, part 2 00:15:00
Design of computer experiments 00:15:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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