MODULE 17 ELEMENTARY STATISTICS

• Significant figures
• Measures of variation (e.g., mean, median, mode, skew, standard deviation, RPD, CV)
• Interpolation, extrapolation
• Hypothesis testing
• Principles of exploratory data analysis (EDA)

- Traditional EDA techniques
- Emerging EDA techniques
- Survey of EDA software

MODULE 18 APPLICATIONS OF REGRESSION TO WATER QUALITY ANALYSIS

• Basic concepts and applications
• Basic linear regression
• Detecting and analyzing trends
• Curve fitting with Excel
• Assumptions and limitations of regression analysis
• Regression software survey

MODULE 19 GIS/SPATIAL ANALYSIS

• Vector analyses (e.g., unions, intersections, clipping, buffer analyses)
• Raster analyses (e.g., neighborhood statistics, interpolation, filtering)
• Patch statistics (e.g., survey, assumptions and use)

MODULE 20 DATA VISUALIZATION AND PRESENTATION

• Basic graphical techniques and software
• Data visualization techniques

MODULE 21 INTRODUCTION TO MODELING

• Underlying assumptions of models
• Limitations of models
• Types of models (e.g., conceptual, empirical, mechanistic)
• Applications of models

- Pollutant loads (e.g., TMDLs)
- Lake water quality (e.g., WILMS and other spreadsheet models)
- Streams (e.g., In-stream Flow Incremental Methodology – IFIM)
- Stormwater, urban runoff