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Advanced Statistics

MAT20306

Over deze cursus

This course covers several more advanced statistical models and associated designs, and techniques for statistical inference, as relevant to life science studies. The main topics are categorical data, (multiple) regression, analysis of variance (including multiple comparisons), analysis of covariance, and non-parametric tests. The aims of an analysis, the model assumptions, the properties (and limitations) of the models and associated inferential techniques and the interpretation of results in terms of the practical problem will be discussed. Focus will be upon students gaining an understanding of the model ingredients, an (intuitive) understanding of inferential techniques, insight into data structures and implications for choice of model and analysis. Students will be able to perform analysis of data with statistical software, i.e. with R-Studio.

Leerresultaten

After successful completion of this course students are expected to be able to:

  • Formulate a statistical hypothesis based on a research question
  • Recognize a valid experimental design or sampling procedure for data collection
  • Select an appropriate statistical model that allows valid estimation, quantification of uncertainty, and hypothesis testing given the research question and properties of the data
  • Produce relevant computer output in R (RStudio) for a given data set and research question
  • Interpret the outcome of the statistical analysis, draw valid conclusions and argue the relevance for the actual problem

Veronderstelde voorkennis

Assumed Knowledge:
MAT15303 Statistics 1 + MAT15403 Statistics 2 or MAT14303 Basic Statistics or MAT15403 Statistics 2.
The student should be familiar with 1) The principles of probability calculus and the subjects: estimation, construction of confidence intervals and hypothesis testing from statistical inference 2) Application of these principles to inference about central values (mean or success probability) for the 1-sample and 2-sample situations, in case of Normal observations and binary (0,1) observations 3) Methods of analysis for simple (one explanatory variable) linear regression.
To refresh this knowledge, (parts of) chapters 1 to 6 and 11 of the book can be studied. or the Brightspace Module 'Ready to Advance in Statistics' can be worked through. Via Brightspace > Discover, enroll yourself.

Link naar meer informatie

Als er nog iets onduidelijk is, kijk even naar de FAQ van Wageningen University.

Aanbod

  • Startdatum

    13 mei 2024

    • Einddatum
      5 juli 2024
    • Periode *
      Period 6
    • Locatie
      Wageningen
    • Voertaal
      Engels
    Inschrijvingsperiode gesloten
  • Startdatum

    2 september 2024

    • Einddatum
      25 oktober 2024
    • Periode *
      Period 1
    • Locatie
      Wageningen
    • Voertaal
      Engels
    • Inschrijven tussen
      1 jun, 00:00 - 4 aug 2024
  • Startdatum

    28 oktober 2024

    • Einddatum
      20 december 2024
    • Periode *
      Period 2
    • Locatie
      Wageningen
    • Voertaal
      Engels
    • Inschrijven tussen
      1 jun, 00:00 - 29 sep 2024
  • Startdatum

    6 januari 2025

    • Einddatum
      31 januari 2025
    • Periode *
      Period 3
    • Locatie
      Wageningen
    • Voertaal
      Engels
    • Inschrijven tussen
      1 jun, 00:00 - 24 nov 2024
  • Startdatum

    10 maart 2025

    • Einddatum
      2 mei 2025
    • Periode *
      Period 5
    • Locatie
      Wageningen
    • Voertaal
      Engels
    • Inschrijven tussen
      1 jun, 00:00 - 9 feb 2025
  • Startdatum

    12 mei 2025

    • Einddatum
      4 juli 2025
    • Periode *
      Period 6
    • Locatie
      Wageningen
    • Voertaal
      Engels
    • Inschrijven tussen
      1 jun, 00:00 - 6 apr 2025
Dit aanbod is voor studenten van TU Eindhoven