TECH3 Applied statistics
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Module 2: Probability, random variables, probability distributions and simulations.
Bernoulli distribution
Module 1: Summarizing and visualizing data
Introduction
Textbook
Statistical Thinking
Working with data
Summarizing data
Summary statistics
Data visualization principles
Plotting tools
Idealised representations
Gapminder example of good visualization
Python
Exercises
Module 2: Probability, random variables, probability distributions and simulations.
Introduction
Textbook
What is probability?
Basic concepts in probability
Basic probability rules
Conditional probability
Independent events
Bayes rule
Discrete random variables and distributions
Expectation and variance of discrete random variables
Bernoulli distribution
Binomial distribution
Python
Exercises
Module 3: Estimation, sampling distributions and resampling
Introduction
Textbook
Population vs sample
What is a model?
Sampling error and distribution
Central Limit Theorem
What is an estimator?
Maximum likelihood
Random number generation
Monte Carlo simulation
Bootstrap
Python
Exercises
Module 4: Designing studies, hypothesis testing, and quantifying effects
Introduction
Textbook
Hypothesis testing
Hypothesis testing in Python
Power analysis
Videos
Python
Exercises
Module 5: Measuring relationships and fitting models
Introduction
Textbook
Modeling categorical relationships
Contingency tables
Simpson’s paradox
Modeling continuous relationships: Correlation
Correlation and causation
Linear regression
Assessing the model
Prediction models
Logistic regression
Python
Exercises
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Bernoulli distribution
Controll questions
Module 2: Probability, random variables, probability distributions and simulations.
Bernoulli distribution
Bernoulli distribution
Slides for
“The Bernoulli distribution”
Controll questions
What is a Bernoulli trial?
How is the Bernoulli distribution defined, and what role does probability p play in it?
Expectation and variance of discrete random variables
Binomial distribution