Introduction to R; R examples: Binomial.

17 Março 2017, 11:30 • NICOLETTA ROSATI

Introduction to R: reading data, creating new numerical and qualitative variables, displaying a two-way table, creating a matrix, converting a qualitative variable into a factor, factor levels, estimating a glm model.
Estimation of a binomial GLM: modeling of proportions; weights; default dispersion parameter value; displaying and interpreting estimation results; individual parameter significance tests; tests based on the deviance; obtaining extra output; interpreting graphs.
Example - Worksheet 8: logistic regression for the Missing Persons Data. Discussion of Ex. 3 a-f from exam paper of 16/6/2016.

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Parametrisation. Examples

17 Março 2017, 08:30 • NICOLETTA ROSATI

Discussion of Ex. 7 from Exercise Sheet no. 1 (cont.). Note on parametrisation. Example. Discussion of Ex. 2 from exam paper of 16/6/2016.

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Inference. Exercise Sheet n. 1

15 Março 2017, 09:30 • NICOLETTA ROSATI

Pearson and deviance residuals. Estimation of dispersion parameter. Test F for nested models when the scale parameter is unknown. Comparison of non-nested models. Test of hypotheses on individual parameters. Confidence intervals. Discussion of Exercise 6 from Exercises Sheet no. 1.

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Deviance. Exercise Sheet n. 1

13 Março 2017, 08:00 • NICOLETTA ROSATI

Deviance and scaled deviance. Model fit and model comparison. Nested models. Comparison of nested models using the deviance difference: statistical test of significance. Examples. Discussion of Ex. 7 from Exercise Sheet no. 1.

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Generalised Linear Models.

10 Março 2017, 08:00 • NICOLETTA ROSATI

Introduction to Generalised Linear Models: link functions, canonical link function, linear predictor, variables, factors, interactions. Examples.

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