A comprehensive introduction to probability, as a language and set of tools for understanding statistics, science, risk, and randomness. Basics: sample spaces and events, conditional probability, and Bayes Theorem. Univariate distributions: density s, expectation and variance, Normal, Binomial, Negative Binomial, Poisson, exponential distributions. Bivariate distributions.
PhD in Marine Engineering
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| content serial | Description |
|---|---|
| 1 | An introduction to Statistics and statistical analysis on data observation. |
| 2 | Statistical measurements |
| 3 | Testing of hypothesis, linear regression, Multiple regression, Analysis of variance. |
| 4 | Elementary Probability- Probability theorems. |
| 5 | Conditional probability Independent and dependent events |
| 6 | Total probability rule – Baye’s Theorem and enumeration methods |
| 7 | Discrete probability distribution – probability mass |
| 8 | Continuous probability distribution –probability density |
| 9 | Mathematical expectation, mean and variance |
| 10 | Special discrete distribution: Bernoulli, Binomial, Hypergeometric and Poisson distributions |
| 11 | Special continuous distribution: Uniform and exponential distribution |
| 12 | Special continuous distribution: normal distribution |
| 13 | Discrete joint probability distribution |
| 14 | Continuous joint probability distribution |
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