| 2. Probability |
2.1 Random Experiment. Sample space. Events.
2.2 Probability: concept, properties and methods of determination.
2.3 Conditional Probability. Independence of events.
2.4 Fundamental theorems: Product rule, total probabilities and Bayes' rule. |
| 4. Intervals of confidence |
4.1 Estimator: concept and properties.
4.2 The sample mean, sample variance and sample proportion.
4.3 Intervals of confidence for the mean, variance and proportion.
4.4 Calculation of the size of the sample.
4.5 Intervals of confidence for the difference of two means and two proportions. |
| 6. Introduction to regression models |
6.1 Linear association measures: covariance and linear correlation coefficient.
6.2 The simple linear regression model.
6.3 Least squares and the fitted model.
6.4 Properties of the least squares estimators and inference.
6.5 Analyses of variance and sample coefficient of determination.
6.6 Model checking.
6.7 Prediction.
6.8 Multiple linear regression model.
6.9 Methods for model selection. |
