406-0603-AAL  Stochastics (Probability and Statistics)

 Semester Autumn Semester 2020 Lecturers M. Kalisch Periodicity every semester recurring course Language of instruction English Comment Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement.Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit.

Courses

NumberTitleHoursLecturers
406-0603-AA RStochastics (Probability and Statistics)
Self-study course. No presence required.
120s hrsM. Kalisch

Catalogue data

 Abstract Introduction to basic methods and fundamental concepts of statistics and probability theory for non-mathematicians. The concepts are presented on the basis of some descriptive examples. Learning the statistical program R for applying the acquired concepts will be a central theme. Objective The objective of this course is to build a solid fundament in probability and statistics. The student should understand some fundamental concepts and be able to apply these concepts to applications in the real world. Furthermore, the student should have a basic knowledge of the statistical programming language "R". Content From "Statistics for research" (online)Ch 1: The Role of StatisticsCh 2: Populations, Samples, and Probability DistributionsCh 3: Binomial DistributionsCh 6: Sampling Distribution of AveragesCh 7: Normal DistributionsCh 8: Student's t DistributionCh 9: Distributions of Two VariablesFrom "Introductory Statistics with R (online)"Ch 1: BasicsCh 2: The R EnvironmentCh 3: Probability and distributionsCh 4: Descriptive statistics and tablesCh 5: One- and two-sample testsCh 6: Regression and correlation Literature - "Statistics for research" by S. Dowdy et. al. (3rdedition); Print ISBN: 9780471267355; Online ISBN: 9780471477433; DOI:10.1002/0471477435 From within the ETH, this book is freely available online under: Link- "Introductory Statistics with R" by Peter Dalgaard; ISBN978-0-387-79053-4; DOI: 10.1007/978-0-387-79054-1 From within the ETH, this book is freely available online under: Link

Performance assessment

 Performance assessment information (valid until the course unit is held again) Performance assessment as a semester course ECTS credits 4 credits Examiners M. Kalisch Type graded semester performance Language of examination English Repetition Repetition possible without re-enrolling for the course unit.

Learning materials

 No public learning materials available. Only public learning materials are listed.

Groups

 No information on groups available.

Restrictions

 There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Computational Biology and Bioinformatics MasterCourse Units for Additional Admission RequirementsE-
Earth Sciences MasterCourse Units for Additional Admission RequirementsE-
Geomatic Engineering MasterCourse Units for Additional Admission RequirementsE-
Food Science MasterCourse Units for Additional Admission RequirementsE-
Pharmaceutical Sciences MasterCourse Units for Additional Admission RequirementsE-
Pharmacy MasterCourse Units for Additional Admission RequirementsE-
Spatial Development and Infrastructure Systems MasterCourse Units for Additional Admission RequirementsE-
Computational Science and Engineering MasterCourse Units for Additional Admission RequirementsE-
Statistics MasterCourse Units for Additional Admission RequirementsE-
Environmental Engineering MasterCourse Units for Additional Admission RequirementsE-
Environmental Sciences MasterCourse Units for Additional Admission RequirementsE-