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This course provides an elementary introduction to probability and statistics with applications.
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English
English [CC]
FREE
- Learn basic syntax that can apply to any language.
- Learn what is a programming language and the basic concepts for beginners.
- Understand what is Javascript in it's truest form.
- Know the basic syntax of Javascript.
- Know some hidden quirks in Javascript.
Description
Topics include: basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression.
The Spring 2014 version of this subject employed the residential MITx system, which enables on-campus subjects to provide MIT students with learning and assessment tools such as online problem sets, lecture videos, reading questions, pre-lecture questions, problem set assistance, tutorial videos, exam review content, and even online exams.
Course content
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- Introduction, counting and sets Unlimited
- Probability basics Unlimited
- Conditional probability, Bayes’ theorem Unlimited
- Discrete random variables, expectation Unlimited
- Variance, continuous random variables Unlimited
- Gallery of continuous variables, histograms Unlimited
- Expectation, variance, law of large numbers and central limit theorem Unlimited
- Joint distributions: Independence, covariance and correlation Unlimited
- Review for exam 1 Unlimited
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- Introduction to statistics; maximum likelihood estimates Unlimited
- Bayesian updating with known discrete priors Unlimited
- Bayesian updating: Probabilistic prediction; odds Unlimited
- Bayesian updating: Continuous prior, discrete data Unlimited
- Beta distributions: Continuous data Unlimited
- Conjugate priors; choosing priors Unlimited
- Probability intervals Unlimited
- Frequentist methods; NHST Unlimited
- NHST II: Significance level, power, t-tests Unlimited
- NHST III: Gallery of tests Unlimited
- Comparison of Bayesian and frequentist inference Unlimited
- Review for exam 2 Unlimited
Instructor
Massachusetts Institute of Technology
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