Statisticians want to understand the world. The world is made of probabilities, we model probabilities with functions, and we model functions with parameters.
“Observe data and construct models, infer and refine hypotheses ”
Resources: https://github.com/unpingco/Python-for-Probability-Statistics-and-Machine-Learning-2E
- Put links into relevant notes
Portal for all statistics notes:
Logistic Regression: model how change and covariance influence the odds of an event
Proportional Hazard Model: time to an event
Hypothesis testing p values Confidence Interval
parametric vs non-parametric tests
R tidyverse: visualisation in R
Adaptive decision analysis: interrupting the experiment in the middle
Estimation Problems: using data to estimate model parameters
Likelihood ratio: Type I Error (False Positive) UMP test. used to maximise power. T-test is a consequence of this.