# Search result: Catalogue data in Autumn Semester 2016

Environmental Sciences Bachelor | ||||||

Natural Science and Technical Electives | ||||||

Natural Science Modules | ||||||

Methodes of Statistical Data Analysis | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
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701-0105-00L | Applied Statistics for Environmental Sciences | W | 3 credits | 2G | C. Bigler, U. Brändle, M. Kalisch, L. Meier | |

Abstract | Statistical methods from current publications in environmental sciences are presented and applied. Students are enabled to understand the methods, clean datasets, analyse them using the software package R and present the results in a suitable form. They will be able to describe strengths and weaknesses of the methods for given fields of application. | |||||

Objective | Students are able to - use suitable statistical methods for data analysis in their subject area. - characterize data sets using explorative methods - check the suitability of data sets to answer a given question, prepare data sets for import to a statistics program and conduct the analysis. - interpret statistical analyses and process them graphically for use in presentations and publications. - describe the basics of statistical methods used in current publications. - use the software package R for statistical analysis | |||||

Content | Statistische Methoden: Regression (lineare Modelle; generalisierte lineare Modelle; GLMs); Varianzanalyse; gemischte Modelle für gruppierte Daten (mixed-effects models); Fragebogenstatistik; Tests (t Test; Chiquadrat Test; Fisher Test); Power-Analyse Werkzeuge: Explorative Datenanalyse für Hypothesenbildung; Auswahlverfahren für geeignete statistische Verfahren; Datenaufbereitung (Excel -> R; Datenbereinigung); graphische Darstellung von Resultaten; statistische Verfahren in Publikationen erkennen Wir arbeiten mit dem Softwarepaket R. Form: Im Wochenrhythmus finden alternierend Einführungen in eine neue Methode und Übungsstunden zum Thema statt. | |||||

Prerequisites / Notice | Besuch von "Mathematik IV: Statistik" oder vergleichbare Lehrveranstaltung | |||||

701-1671-00L | Sampling Techniques for Forest Inventories | W | 3 credits | 2V | D. Mandallaz | |

Abstract | Introduction to design and model assisted sampling theory for finite populations as well as to the infinite population model for forest inventory. Two-phase two-stage forest inventories with simple or cluster sampling. Small area estimation. Presentation of the Swiss National Inventory. Short introduction to Kriging techniques. | |||||

Objective | Students should have a good understanding of the concepts of general sampling theory in a modern framework. They should also master the specific problems arising in forest inventory and be able, if necessary, to read more specialized books or research papers. | |||||

Content | Inclusion probabilities. Horwitz-Thompson estimates. Simple random sampling. Stratified sampling. PPS sampling and multi-stage sampling. Model assisted procedures. Formalism of sampling theory in forest inventory. One-phase simple and cluster sampling schemes. Two-phase two-sampling schemes. Model-dependent and model assisted procedures. Small area estimation. Kriging techniques. The Swiss National Forest Inventory. | |||||

Lecture notes | Sampling techniques for forest inventories. Daniel Mandallaz, Chapman and Hall. A free electronic copy of the book is also available. A PDF file containing parts of the book will be mailed to the participants | |||||

Literature | Sampling methods for multiresource forest inventory. H.T. Schreuder, T.G. Gregoire, G.B. Wood, 1993, Wiley. Model assisted survey sampling, C.E. Särndal, B. Swenson, J. Wretman, 2003, Springer. Sampling methods, remote sensing and GIS multisource forest inventory M. Köhl, S. Magnussen, M. Marchetti, 2006, Springer. Sampling techniques for forest inventories, Daniel Mandallaz, 2007, Chapman and Hall. T.G. Gregoire, H.T. Valentine. Sampling strategies for natural resources and the environment, Chapman and Hall. | |||||

Prerequisites / Notice | A simulation software will be used throughtout the lectures to illustrate the theoretical developments. Upon request a half day field demonstration can be organized at the WSL outside the lecture time. A repetitorium for the exam is also offered. | |||||

401-0625-01L | Applied Analysis of Variance and Experimental Design | W | 5 credits | 2V + 1U | L. Meier | |

Abstract | Principles of experimental design. One-way analysis of variance. Multi-factor experiments and analysis of variance. Block designs. Latin square designs. Split-plot and strip-plot designs. Random effects and mixed effects models. Full factorials and fractional designs. | |||||

Objective | Participants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R. | |||||

Content | Principles of experimental design. One-way analysis of variance. Multi-factor experiments and analysis of variance. Block designs. Latin square designs. Split-plot and strip-plot designs. Random effects and mixed effects models. Full factorials and fractional designs. | |||||

Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||

Prerequisites / Notice | The exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held. | |||||

401-0649-00L | Applied Statistical Regression | W | 5 credits | 2V + 1U | M. Dettling | |

Abstract | This course offers a practically oriented introduction into regression modeling methods. The basic concepts and some mathematical background are included, with the emphasis lying in learning "good practice" that can be applied in every student's own projects and daily work life. A special focus will be laid in the use of the statistical software package R for regression analysis. | |||||

Objective | The students acquire advanced practical skills in linear regression analysis and are also familiar with its extensions to generalized linear modeling. | |||||

Content | The course starts with the basics of linear modeling, and then proceeds to parameter estimation, tests, confidence intervals, residual analysis, model choice, and prediction. More rarely touched but practically relevant topics that will be covered include variable transformations, multicollinearity problems and model interpretation, as well as general modeling strategies. The last third of the course is dedicated to an introduction to generalized linear models: this includes the generalized additive model, logistic regression for binary response variables, binomial regression for grouped data and poisson regression for count data. | |||||

Lecture notes | A script will be available. | |||||

Literature | Faraway (2005): Linear Models with R Faraway (2006): Extending the Linear Model with R Draper & Smith (1998): Applied Regression Analysis Fox (2008): Applied Regression Analysis and GLMs Montgomery et al. (2006): Introduction to Linear Regression Analysis | |||||

Prerequisites / Notice | The exercises, but also the classes will be based on procedures from the freely available, open-source statistical software package R, for which an introduction will be held. In the Mathematics Bachelor and Master programmes, the two course units 401-0649-00L "Applied Statistical Regression" and 401-3622-00L "Regression" are mutually exclusive. Registration for the examination of one of these two course units is only allowed if you have not registered for the examination of the other course unit. | |||||

401-6215-00L | Using R for Data Analysis and Graphics (Part I) | W | 1 credit | 1G | A. Drewek, A. J. Papritz | |

Abstract | The course provides the first part an introduction to the statistical software R for scientists. Topics covered are data generation and selection, graphical and basic statistical functions, creating simple functions, basic types of objects. | |||||

Objective | The students will be able to use the software R for simple data analysis. | |||||

Content | The course provides the first part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R. Part I of the course covers the following topics: - What is R? - R Basics: reading and writing data from/to files, creating vectors & matrices, selecting elements of dataframes, vectors and matrices, arithmetics; - Types of data: numeric, character, logical and categorical data, missing values; - Simple (statistical) functions: summary, mean, var, etc., simple statistical tests; - Writing simple functions; - Introduction to graphics: scatter-, boxplots and other high-level plotting functions, embellishing plots by title, axis labels, etc., adding elements (lines, points) to existing plots. The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org Note: Part I of UsingR is complemented and extended by Part II, which is offered during the second part of the semester and which can be taken independently from Part I. | |||||

Lecture notes | An Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf | |||||

Prerequisites / Notice | The course resources will be provided via the Moodle web learning platform Please login (with your ETH (or other University) username+password) at https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145 Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | |||||

401-6217-00L | Using R for Data Analysis and Graphics (Part II) | W | 1 credit | 1G | A. Drewek, A. J. Papritz | |

Abstract | The course provides the second part an introduction to the statistical software R for scientists. Topics are data generation and selection, graphical functions, important statistical functions, types of objects, models, programming and writing functions. Note: This part builds on "Using R... (Part I)", but can be taken independently if the basics of R are already known. | |||||

Objective | The students will be able to use the software R efficiently for data analysis. | |||||

Content | The course provides the second part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R. Part II of the course builds on part I and covers the following additional topics: - Elements of the R language: control structures (if, else, loops), lists, overview of R objects, attributes of R objects; - More on R functions; - Applying functions to elements of vectors, matrices and lists; - Object oriented programming with R: classes and methods; - Tayloring R: options - Extending basic R: packages The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org | |||||

Lecture notes | An Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf | |||||

Prerequisites / Notice | Basic knowledge of R equivalent to "Using R .. (part 1)" ( = 401-6215-00L ) is a prerequisite for this course. The course resources will be provided via the Moodle web learning platform Please login (with your ETH (or other University) username+password) at https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145 Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. |

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