| Lecture Topics
(approximate; subject to minor changes) |
| 0. Introduction | 1 | General introduction: admin, summary, quality |
| 1. Probability and Probability Models
| 2 | Probability: basics, conditional probability, law of total probability, Bayes' theorem |
| | 3 | Independence; reliability; independent trials, geometric distribution |
| | 4 | Bernoulli trials: binomial distribution, negative binomial distribution |
| | 5 | Sampling without replacement, hypergeometric distribution; acceptance sampling |
| | 6 | Markov chains and simple applications |
| | 7 | Poisson process and applications |
| 2. Random variables, Distributions and Applications
| 8 | Random variables: cdf, pmf and pdf |
| | 9 | Random variables: quantiles, expectation; measures of location and spread |
| | 10 | Mean and variance, approximations; exponential distribution, gamma distribution |
| | 11 | Failure models, Weibull distribution; uniform distribution; normal distribution |
| | 12 | Normal distribution and applications. Extreme Value distribution. |
| 3. Descriptive Statistics and Exploratory Data Analysis
| 13 | Descriptive statistics; dotplot, order statistics, boxplot, measures of location and spread |
| | 14 | Descriptive statistics: sample distribution, sample cdf and QQ plots |
| | 15 | QQplot applications; Probability plot; generalisations |
| 4. Estimation and Confidence Intervals
| 16 | Estimation. Introduction. Point and interval estimation. Methods of estimation. |
| | 17 | Maximum likelihood estimation. Confidence intervals. t-distribution, c2 distribution. |
| | 18 | Confidence intervals and Prediction intervals. Likelihood-based confidence interval. |
| 5. Hypothesis Testing and Control Charts
| 19 | Hypothesis testing: introduction and terminology |
| | 20 | Hypothesis testing: examples, discrete case, more examples |
| | 21 | Likelihood ratio test. Sequential testing. Control charts introduction |
| | 22 | Control charts for location, for variation; for attributes, for non-conformities |
| 6. Inference for Normal Populations
| 23 | Inference for normal populations review. Comparative inference) |
| | 24 | Comparison of two normal populations. F-distribution. Comparing variances |
| | 25 | Comparison of two normal populations: comparing means. |
| | 26 | Comparison of k normal populations, one-way anova |
| | 27 | Two-way anova, additive model: introduction and examples |
| | 28 | Two-way anova, m observations/cell: model with interaction |
| 7. Design and Analysis of Experiments
| 29 | Design and analysis of experiments: principles, simple designs |
| | 30 | Factorial experiments: introduction, case-study, examples |
| | 31 | Factorial experiments: blocking, partial replication |
| 8. Linear Regression and Prediction
| 32 | Regression: introduction, estimation |
| | 33 | Regression: inference, confidence intervals, prediction intervals and hypothesis testing |
| | 34 | Correlation: estimation and inference. Multiple linear regression: introduction |
| | 35 | Multiple linear regression: examples and applications |
| 9. Revision
| 36 | Revision: exams old and new. |
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