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Regression Analysis
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Courseware
This course discusses the requirement, interpretation, methods and design of hydrological measurements. Following topics are covered: Accuracy requirements of measurements and error propagation: Related to a problem the required accuracy of measurements and the consequences for accuracy in the final result are discussed. Different types of errors are handled. Propagation of errors; for dependent and independent measurements, from mathematical relations and regression is demonstrated. Recapitulated is the theory of regression and correlation. Interpretation of measurements, data completion: By standard statistical methods screening of measured data is performed; double mass analysis, residual mass, simple rainfall-runoff modelling. Detection of trends; split record tests, Spearman rank tests. Methods to fill data gaps and do filtering on data series for noise reduction. Methods of hydrological measurements and measuring equipment: To determine quantitatively the most important elements in the hydrological cycle an overview is presented of most common hydrological measurements, measuring equipment and indirect determination methods i.e. for precipitation, evaporation, transpiration, river discharge and groundwater tables. Use, purpose and measurement techniques for tracers in hydrology is discussed. Advantages and disadvantages and specific condition/application of methods are discussed. Equipment is demonstrated and discussed. Areal distributed observation: Areal interpolation techniques of point observations; inverse distance, Thiessen, contouring, Kriging. Comparison of interpolation techniques and estimation of errors. Correlation analysis of areal distributed observation of rainfall. Design of measuring networks: Based on correlation characteristics from point measurements (e.g. rainfall stations) and accuracy requirements the design of a network of stations is demonstrated.
- Subjects:
- Hydraulic Engineering
- Keywords:
- Hydrology -- Measurement Hydrology
- Resource Type:
- Courseware
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Courseware
Statistics is the science that turns data into information and information into knowledge. This class covers applied statistical methodology from an analysis-of-data viewpoint. Topics covered include frequency distributions; measures of location; mean, median, mode; measures of dispersion; variance; graphic presentation; elementary probability; populations and samples; sampling distributions; one sample univariate inference problems, and two sample problems; categorical data; regression and correlation; and analysis of variance. Use of computers in data analysis is also explored.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Statistics
- Resource Type:
- Courseware
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Courseware
Provides students with the basic tools for analyzing experimental data, properly interpreting statistical reports in the literature, and reasoning under uncertain situations. Topics organized around three key theories: Probability, statistical, and the linear model. Probability theory covers axioms of probability, discrete and continuous probability models, law of large numbers, and the Central Limit Theorem. Statistical theory covers estimation, likelihood theory, Bayesian methods, bootstrap and other Monte Carlo methods, as well as hypothesis testing, confidence intervals, elementary design of experiments principles and goodness-of-fit. The linear model theory covers the simple regression model and the analysis of variance. Places equal emphasis on theory, data analyses, and simulation studies.
- Subjects:
- Mathematics and Statistics and Biology
- Keywords:
- Cognitive science Statistics
- Resource Type:
- Courseware