July 21, 2026
Hydrologic systems are both complex and extremely heterogeneous in time and space. As hydrological models are simplified versions of reality, their predictions are by definition subject to uncertainty. The field of stochastic hydrology focuses on quantifying this uncertainty along with prediction. This book aims to present an overview of the field of stochastic hydrology at an introductory level. A wide range of topics and methods will be treated, while each topic and method is only treated at a basic level: descriptive statistics; probability and random variables; hydrological statistics and extremes; random functions; time series analysis; geostatistics; forward stochastic modeling; and state prediction and Kalman filtering. The book can be used for a 10-15-week course at third-year undergraduate or first-year graduate level.
Marc F.P. Bierkens
August 23, 2023
To be able to demonstrate knowledge of the data analysis process To understand differences between methodologies and standards
HCAIM Consortium
September 1, 2020
Deze powerpoint wordt behandeld in de kennisclips over de betrouwbaarheidsanalyse. De powerpoint introduceert het concept betrouwbaarheidsanalyses, geeft informatie over Cronbach's Alfa, en vertelt hoe je een schaalt kunt maken met 'compute'.
Jasper Muis
December 19, 2018
Een PowerPoint met informatie over kwantitatief en kwalitatief onderzoek, meetniveaus, betrouwbaarheid, validiteit, afhankelijke en onafhankelijke variabelen, statistiek.
Guido, Shifra Bouter
May 8, 2018
In this micro lecture the following items will be explained regarding this lecture: Testing a bivariate causal hypothesis means studying: time order correlation/association *direction/sign *strenght *significance no third variable
Henk van der Kolk
In this micro lecture the following items will be explained regarding this micro lecture: Testing a bivariate causal hypothesis means studying: Time order Correlation/association Direction/sign Strength Significance No third variable construction of a bivariate cross table (correlation/association, direction/sign, strenght, significance)
In this micro lecture the following items will be explained regarding this lecture: Testing a bivariate causal hypothesis means studying: Time order Correlation/association Direction/sign Strength Significance No third variable Construction of a bivariate cross table ( correlation/association, direction/sign, strenght, significance)
In this micro lecture the following items will be explained regarding this lecture: Causality: time order association non-spurious relationship Bivariate associations between variables with various levels of meassurement
In this micro lecture the following items will be explained regarding this lecture: Causality: Time order, Association, Non-spurious relationship. Bivariate associations between variables with various levels of measurement.
In this micro lecture the following items will be explained regarding this lecture: Introducing three groups of research designs for testing causal relationships: cross-sectional research designs (a.k.a. correlational) interrupted time series classical experiments