Probability and Bayes' Theorem-Module overview
Course introduction
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Module 1 objectives, assignments, and supplementary materials
Probability and Bayes' Theorem-Probability
Background for Lesson 1
Lesson 1.1 Classical and frequentist probability
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Lesson 1.2 Bayesian probability and coherence
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Probability and Bayes' Theorem-Bayes' theorem
Lesson 2.1 Conditional probability
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Lesson 2.2 Bayes' theorem
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Supplementary material for Lesson 2
Probability and Bayes' Theorem-Review of distributions
Lesson 3.1 Bernoulli and binomial distributions
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Lesson 3.2 Uniform distribution
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Lesson 3.3 Exponential and normal distributions
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Supplementary material for Lesson 3
Statistical Inference-Module overview
Module 2 objectives, assignments, and supplementary materials
Statistical Inference-Frequentist inference
Background for Lesson 4
Lesson 4.1 Confidence intervals
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Lesson 4.2 Likelihood function and maximum likelihood
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Lesson 4.3 Computing the MLE
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Lesson 4.4 Computing the MLE: examples
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Supplementary material for Lesson 4
Introduction to R
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Plotting the likelihood in R
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Plotting the likelihood in Excel
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Statistical Inference-Bayesian inference
Background for Lesson 5
Lesson 5.1 Inference example: frequentist
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Lesson 5.2 Inference example: Bayesian
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Lesson 5.3 Continuous version of Bayes' theorem
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Lesson 5.4 Posterior intervals
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Supplementary material for Lesson 5
Priors and Models for Discrete Data-Module overview
Module 3 objectives, assignments, and supplementary materials
Priors and Models for Discrete Data-Priors
Lesson 6.1 Priors and prior predictive distributions
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Lesson 6.2 Prior predictive: binomial example
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Lesson 6.3 Posterior predictive distribution
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Priors and Models for Discrete Data-Bernoulli/binomial data
Lesson 7.1 Bernoulli/binomial likelihood with uniform prior
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Lesson 7.2 Conjugate priors
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Lesson 7.3 Posterior mean and effective sample size
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Data analysis example in R
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Data analysis example in Excel
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R and Excel code from example analysis
Priors and Models for Discrete Data-Poisson data
Lesson 8.1 Poisson data
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Models for Continuous Data-Module overview
Module 4 objectives, assignments, and supplementary materials
Models for Continuous Data-Exponential data
Lesson 9.1 Exponential data
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Models for Continuous Data-Normal data
Lesson 10.1 Normal likelihood with variance known
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Lesson 10.2 Normal likelihood with variance unknown
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Supplementary material for Lesson 10
Models for Continuous Data-Alternative priors
Lesson 11.1 Non-informative priors
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Lesson 11.2 Jeffreys prior
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Supplementary material for Lesson 11
Models for Continuous Data-Linear regression
Background for Lesson 12
Linear regression in R
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Linear regression in Excel (Analysis ToolPak)
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Linear regression in Excel (StatPlus by AnalystSoft)
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R and Excel code for regression
Models for Continuous Data-Course conclusion
Conclusion
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