Statistics


Methods and Tools T3130 Statistics
Exam Code 3130
Credit Points 5 ECTS
Term Winter
Module Coordination Prof. Dr.-Ing. Jackson Roehrig
Lecturers Prof. Dr.-Ing. Jackson Roehrig
Workload (h) 45 Attendance 105 Self-Study

 

Prerequisites Since the number of attendees is expected to be higher than the computer lab capacity, the computer lab will not be used for lectures. Therefore, attendees are expected to bring their laptops installed with the required software to the lectures. If you do not have a laptop, you can catch up on exercises at our lab after the lectures, or eventually a classmate will share his/her laptop with you.The following software should be installed on your computer (available for Windows, Mac OS X, and Linux):

  • Anaconda: it is a completely free Python distribution (including for commercial use and redistribution). It includes over 195 of the most popular Python packages for science, math, engineering, data analysis. Download it from http://continuum.io/downloads.
  • PyCharm: download it from https://www.jetbrains.com/pycharm/download/. The community edition fulfills our purposes.

It is recommended to attend online courses or follow tutorials on Python before the module starts. Some recommendations:

Intended Learning Outcome Statistical methods are essential in different contexts of natural resources management systems, like for example for data assessment and monitoring, model building, and project evaluation. The students are able to organize, analyze and present data using descriptive and inferential methods.
Content IntroductionGeneral concepts

  • Descriptive statistics, inference,
  • Questionnaires, observation, experiment
  • Qualitative and quantitative
  • Nominal, ordinal, metric scales
  • Discrete and continuous
  • Univariate, bivariate, multivariate, time series
  • Regression and correlation

Frequency

  • Absolute frequency
  • Cumulative absolute frequency
  • Relative frequency
  • Cumulative relative frequency
  • Histogram, pareto diagram

Univariate Probability

  • Discrete probability distribution
  • Cumulative discrete probability distribution
  • Probability density function
  • Cumulative probability density function

Python

Random variables

  • Measures of central tendency
  • Measures of dispersion
  • Measures of symmetry
  • Skewness
  • Measures of peakedness
  • Kurtosis
  • Set operations
  • Probability
  • Properties: definition, sum of probabilities
  • Combinatorics
  • Conditional distribution

Discrete and continuous probability distributions

  • Discrete distributions
  • Continuous distributions

Confidence intervals and hypothesis testing Regression and correlation

  • Linear regression
  • Multiple linear regression
  • Logistic linear regression
  • Statistical downscaling of global circulation model data

Geostatistics

Teaching Method Lectures, exercises on computer, discussion of examples from module Project
Assesment Method Written Examination (100%)
Recommended Reading Online tutorial and other documents will be given during the lecture
Version 29.05.2015

 

Next Methods and Tools Module: Eco-Balancing and Decision Support Systems

Sidebar