Statistical modelling: Theory and practice
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1 Statistical modelling: Theory and practice Introduction Gilles Guillot August 27, 2013 Gilles Guillot Stat. modelling August 27, / 6
2 Schedule 13 weeks weekly time slot: Tuesday 13:00-17:00 lecture approx. 2 hours + 2 hours exercises or 4 hours on an assignment Gilles Guillot (gigu@dtu.dk) Stat. modelling August 27, / 6
3 Evaluation Two project reports Report 1 due by week 9 Report 2 due by week 14 Oral exam combining general questions on lecture topics specific questions on project Gilles Guillot (gigu@dtu.dk) Stat. modelling August 27, / 6
4 Course overview Thorough presentation of the most important topic in statistics: The Linear Model Solid base inferential methods: Likelihood and Bayesian methods Application-oriented: the R program Window on more specialized statistical methods: times series, stochastic simulation, survival data. Gilles Guillot (gigu@dtu.dk) Stat. modelling August 27, / 6
5
6 Inference principles: Likelihood theory
7 Inference principles: Likelihood theory Introduction to the R program
8 Inference principles: Likelihood theory Introduction to the R program Simple and multiple regression
9 Inference principles: Likelihood theory Introduction to the R program Simple and multiple regression Analysis of Variance (ANOVA)
10 Inference principles: Likelihood theory Introduction to the R program Simple and multiple regression Analysis of Variance (ANOVA) Analysis of Covariance (ANCOVA)
11 Inference principles: Likelihood theory Introduction to the R program Simple and multiple regression Analysis of Variance (ANOVA) Analysis of Covariance (ANCOVA) Inference principles: Bayesian
12 Inference principles: Likelihood theory Introduction to the R program Simple and multiple regression Analysis of Variance (ANOVA) Analysis of Covariance (ANCOVA) Inference principles: Bayesian General linear model theory
13 Inference principles: Likelihood theory Introduction to the R program Simple and multiple regression Analysis of Variance (ANOVA) Analysis of Covariance (ANCOVA) Inference principles: Bayesian General linear model theory Project I
14 Inference principles: Likelihood theory Introduction to the R program Simple and multiple regression Analysis of Variance (ANOVA) Analysis of Covariance (ANCOVA) Inference principles: Bayesian General linear model theory Project I Resampling and stochastic simulation methods
15 Inference principles: Likelihood theory Introduction to the R program Simple and multiple regression Analysis of Variance (ANOVA) Analysis of Covariance (ANCOVA) Inference principles: Bayesian General linear model theory Project I Resampling and stochastic simulation methods Introduction to time series
16 Inference principles: Likelihood theory Introduction to the R program Simple and multiple regression Analysis of Variance (ANOVA) Analysis of Covariance (ANCOVA) Inference principles: Bayesian General linear model theory Project I Resampling and stochastic simulation methods Introduction to time series Introduction to survival data
17 Inference principles: Likelihood theory Introduction to the R program Simple and multiple regression Analysis of Variance (ANOVA) Analysis of Covariance (ANCOVA) Inference principles: Bayesian General linear model theory Project I Resampling and stochastic simulation methods Introduction to time series Introduction to survival data Project II
18 Inference principles: Likelihood theory Introduction to the R program Simple and multiple regression Analysis of Variance (ANOVA) Analysis of Covariance (ANCOVA) Inference principles: Bayesian General linear model theory Project I Resampling and stochastic simulation methods Introduction to time series Introduction to survival data Project II Oral exam
19 References Gilles Guillot Stat. modelling August 27, / 6
20 References Course topics not covered by a single book! Gilles Guillot (gigu@dtu.dk) Stat. modelling August 27, / 6
21 References Course topics not covered by a single book! Slides intended to be self-contented and available from the course web page. Gilles Guillot (gigu@dtu.dk) Stat. modelling August 27, / 6
22 References Course topics not covered by a single book! Slides intended to be self-contented and available from the course web page. Regression: Linear Models in Statistics, N. H. Bingham and J. M. Fry, Springer Undergraduate Mathematics Series, Gilles Guillot (gigu@dtu.dk) Stat. modelling August 27, / 6
23 References Course topics not covered by a single book! Slides intended to be self-contented and available from the course web page. Regression: Linear Models in Statistics, N. H. Bingham and J. M. Fry, Springer Undergraduate Mathematics Series, Regression with Linear Predictors P. K. Andersen L. T. Skovgaard, Springer Statistics for Biology and Health, Gilles Guillot (gigu@dtu.dk) Stat. modelling August 27, / 6
24 References Course topics not covered by a single book! Slides intended to be self-contented and available from the course web page. Regression: Linear Models in Statistics, N. H. Bingham and J. M. Fry, Springer Undergraduate Mathematics Series, Regression with Linear Predictors P. K. Andersen L. T. Skovgaard, Springer Statistics for Biology and Health, A modern approach to regression with R S. Sheather, Springer text in Statistics, 2009 Gilles Guillot (gigu@dtu.dk) Stat. modelling August 27, / 6
25 References Course topics not covered by a single book! Slides intended to be self-contented and available from the course web page. Regression: Linear Models in Statistics, N. H. Bingham and J. M. Fry, Springer Undergraduate Mathematics Series, Regression with Linear Predictors P. K. Andersen L. T. Skovgaard, Springer Statistics for Biology and Health, A modern approach to regression with R S. Sheather, Springer text in Statistics, 2009 Introductory statistics with R, P. Dalgaard, Series Statistics and Computing, Springer, Gilles Guillot (gigu@dtu.dk) Stat. modelling August 27, / 6
26 References Course topics not covered by a single book! Slides intended to be self-contented and available from the course web page. Regression: Linear Models in Statistics, N. H. Bingham and J. M. Fry, Springer Undergraduate Mathematics Series, Regression with Linear Predictors P. K. Andersen L. T. Skovgaard, Springer Statistics for Biology and Health, A modern approach to regression with R S. Sheather, Springer text in Statistics, 2009 Introductory statistics with R, P. Dalgaard, Series Statistics and Computing, Springer, Other refs TBA Gilles Guillot (gigu@dtu.dk) Stat. modelling August 27, / 6
Analysis of covariance
Analysis of covariance Statistical modelling: theory and practice Gilles Guillot gigu@dtu.dk October 2, 2013 Gilles Guillot (gigu@dtu.dk) ANCOVA October 2, 2013 1 / 12 1 Introductory example 2 3 Reading
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