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13: Statistics and Probability Background
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- 13.1: Basic statistics- mean, median, average, standard deviation, z-scores, and p-value
- 13.2: SPC- Basic Control Charts- Theory and Construction, Sample Size, X-Bar, R charts, S charts
- 13.3: Six Sigma- What is it and what does it mean?
- 13.4: Bayes Rule, Conditional Probability and Independence
- 13.5: Bayesian Network Theory
- Bayesian network theory can be thought of as a fusion of incidence diagrams and Bayes’ theorem. A Bayesian network, or belief network, shows conditional probability and causality relationships between variables. The probability of an event occurring given that another event has already occurred is called a conditional probability.
- 13.6: Learning and analyzing Bayesian networks with Genie
- 13.7: Occasionally Dishonest Casino? Markov Chains and Hidden Markov Models
- 13.8: Continuous Distributions- normal and exponential
- 13.9: Discrete Distributions - Hypergeometric, Binomial, and Poisson
- 13.10: Multinomial Distributions
- Typical events generating continuous outcomes may follow a normal, exponential, or geometric distribution. Discrete outcomes can only on take prescribed values; for instance, a dice roll can only generate an integer between 1 to 6. Discrete outcomes are typically distributed either binomially or multinomially. It is with multinomial distribution that this section is concerned.
- 13.11: Comparisons of two means
- 13.12: Factor analysis and ANOVA
- 13.13: Correlation and Mutual Information
- 13.14: Random sampling from a stationary Gaussian process
