2. Probability |
2.1 random Experiment. Sample space. Events.
2.2 Probability: concept, properties and methods of determination.
2.3 Probability conditioned. Independence of events.
2.4 fundamental Theorems: of the product, total probabilities and Bayes. |
4. Intervals of confidence |
4.1 Estimator: concept and properties.
4.2 The average, variance and proportion samples.
4.3 Intervals of confidence for the average, variance and proportion.
4.4 Calculation of the size of the sample.
4.5 Intervals of confidence for the difference of averages and proportions. |
5. Contrasts of hypothesis |
5.1 Definition and classical methodology of a contrast: types of hypothesis,errors associated to the contrast, level of significance, region of rejection. Power.
5.2 Critical Level or p-value.
5.3 Contrasts for the comparison of averages and variances of dosdistribuciones normal.
5.4 Contrast chi-square of independence.
5.5 Contrasts of normality. |
6. Introduction to the models of regression |
6.1 Measurement of the linear association: covariance and coefficient of linear correlation.
6.2 Formulation of the model of simple linear regression.
6.3 Estimate of the parameters.
6.4 Intervals of confidence and contrasts of hypothesis.
6.5 Analyses of the variance and coefficient of determination. Goodness of adjust.
6.6 Validation of the structural hypotheses.
6.7 Prediction.
6.8 linear Model general.
6.9 Strategies of regression and comparison of models. Selection of optimum models. |