Martin Mächler: Catalogue data in Autumn Semester 2023 
Name  Prof. em. Dr. Martin Mächler 
Name variants  Martin Maechler 
Address  Seminar für Statistik (SfS) ETH Zürich, HG GO 14.2 Rämistrasse 101 8092 Zürich SWITZERLAND 
Telephone  +41 44 632 34 08 
maechler@stat.math.ethz.ch  
URL  http://stat.ethz.ch/~maechler 
Department  Mathematics 
Relationship  Retired Adjunct Professor 
Number  Title  ECTS  Hours  Lecturers  

401564000L  ZüKoSt: Seminar on Applied Statistics  0 credits  1K  M. Kalisch, F. Balabdaoui, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. Mächler, L. Meier, N. Meinshausen, J. Peters, M. Robinson, C. Strobl  
Abstract  About 3 talks on applied statistics.  
Learning objective  See how statistical methods are applied in practice.  
Content  There will be about 3 talks on how statistical methods are applied in practice.  
Prerequisites / Notice  This is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web: http://stat.ethz.ch/events/zukost Course language is English or German and may depend on the speaker.  
Competencies 
 
401621500L  Using R for Data Analysis and Graphics (Part I)  1.5 credits  1G  M. Mächler  
Abstract  The course provides the first part an introduction to the statistical/graphical/data science software R (https://www.rproject.org/) for scientists. Topics covered are data generation and selection, graphical and basic statistical functions, creating simple functions, basic types of objects.  
Learning objective  The students will be able to use the software R for simple data analysis and graphics.  
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 highlevel 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 with R. 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/LamIntroductionToR_LHL.pdf  
Prerequisites / Notice  The course resources will be provided via the Moodle web learning platform. Subscribing via Mystudies *automatically* makes you a student participant of the Moodle course of this lecture, which is at https://moodleapp2.let.ethz.ch/course/view.php?id=20847  
Competencies 
 
401621700L  Using R for Data Analysis and Graphics (Part II)  1.5 credits  1G  M. Mächler  
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.  
Learning objective  The students will be able to use the software R efficiently for data analysis, graphics and simple programming  
Content  The course provides the second part of an introduction to the statistical software R (https://www.rproject.org/) 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/LamIntroductionToR_LHL.pdf  
Prerequisites / Notice  Basic knowledge of R equivalent to "Using R .. (part 1)" ( = 401621500L ) is a prerequisite for this course. The course resources will be provided via the Moodle web learning platform. As from FS 2019, subscribing via Mystudies should *automatically* make you a student participant of the Moodle course of this lecture, which is at https://moodleapp2.let.ethz.ch/course/view.php?id=20848  
Competencies 
 
447622100L  Nonparametric Regression Does not take place this semester. Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to registrar@ethz.ch. The Registrar's Office will then register you for the course.  1 credit  1G  M. Mächler  
Abstract  This course focusses on nonparametric estimation of probability densities and regression functions. These recent methods allow modelling without restrictive assumptions such as 'linear function'. These smoothing methods require a weight function and a smoothing parameter. Focus is on one dimension, higher dimensions and samples of curves are treated briefly. Exercises at the computer.  
Learning objective  Knowledge on estimation of probability densities and regression functions via various statistical methods. Understanding of the choice of weight function and of the smoothing parameter, also done automatically. Practical application on data sets at the computer.  
Competencies 
