In this two-day course, the principles of statistics as applied to the analysis of environmental data will be discussed, with as little mathematical detail as possible. Examples will be drawn from environmental applications. These examples will demonstrate the results of different techniques, giving attendees a greater understanding of situations when each of the various techniques for environmental data analysis should be used.
Topics and techniques discussed will include:
- Statistical Principles & Probabilistic Data Models
- Sample Design
- Estimating Means, Medians & Variances& Other Parameters
- Dealing with Non-detects
- Fitting Data to Distributions
- Functions of Random Variables
- Statistical Intervals
- Confidence Intervals
- Tolerance Intervals
- Prediction Intervals
- Parametric Methods
- Non-Parametric Methods
- Hypothesis testing
- Parametric
- Non-Parametric
- Bootstrapping & Randomization
- Analysis of variance
- Linear regression
- Logistic regression
- Contingency tables
- Statistical graphics
- Multivariate methods
This course is intended for environmental professionals who have the need to analyze data using various statistical methodologies. The course is intended to familiarize attendees with commonly used statistical techniques, without using an overwhelming amount of mathematical detail.
Some basic understanding of environmental data sets and analysis is helpful.
- Statistical Principles & Probabilistic Data Models
- Sample Design
- Estimating Means, Medians & Variances& Other Parameters
- Dealing with Non-detects
- Fitting Data to Distributions
- Functions of Random Variables
- Statistical Intervals
- Confidence Intervals
- Tolerance Intervals
- Prediction Intervals
- Parametric Methods
- Non-Parametric Methods
- Hypothesis testing
- Parametric
- Non-Parametric
- Bootstrapping & Randomization
- Analysis of variance
- Linear regression
- Logistic regression
- Contingency tables
- Statistical graphics
- Multivariate methods