Training Courses at Neptune and Company, Inc.

Neptune and Company, Inc. has developed and presented training courses on a wide variety of environmental decision making topics. Many of these courses are aimed at nontechnical audiences. Materials covered are presented in terms of general yet rigorous concepts, allowing the audience to focus on understanding the essence of the approaches as opposed to mathematical components. Past courses consist of on-site and/or live web trainingInstructors are considered expert in their fields, and many have taught undergraduate and graduate courses. Examples include the following:

Statistics

Topics covered included exploratory data analysis (summary statistics and graphics), hypothesis testing, confidence intervals, testing assumptions, lognormal distributions, nonparametric statistics, correlation, regression, temporal analysis, spatial analysis, multivariate analysis, and bootstrapping. Additional topics include data integrity, validation, and verification; data quality objectives; data quality indicators; data quality assessment; sampling designs; and censored data. Example courses include:

  • Introductory: Introduction to Applied Statistics, Exploratory and Confirmatory Data Analysis, and Hypothesis Testing and Confidence Intervals
  • Design: Experimental Design—One Size of Experimental Unit; Experimental Design—More than One size of Experimental Unit; Sampling Design; and Spatial Sampling Design
  • Model Building: Regression and Lack of Fit Analyses, Multiple Regression and Model Selection, Nonlinear Models, and Analysis of Covariance and Model Comparison Techniques
  • Spatial: Exploratory, Descriptive, and Kriging

 

Decision Analysis

Materials covered include the basics of:

  • Rational decision making;
  • Use of decision analysis in the larger contexts of strategic decision making and policy;
  • Utility theory;
  • Structuring a decision problem;
  • Values and objectives;
  • Attributes;
  • Preference structures;
  • Risk attitudes;
  • Uncertainty;
  • Decision modeling ("static" and dynamic);
  • Costs;
  • Value-of-information analysis;
  • Presentation of results; and,
  • Interpretation.

 

Human and Ecological Risk Assessment

Materials covered include the basics of:

  • The role of risk assessment in rational decision-making;
  •  Regulatory context;
  • Toxicology;
  • Exposure analysis;
  • Spatially explicit approaches,
  • Deterministic approaches;
  • Probabilistic (e.g., Monte Carlo simulation) approaches;
  • Integration with other types of modeling;
  • Integration with risk management; and,
  • Interpretation.

 See also: Short Course on Ecological Risk Assessment Methods for Arid Environments.

 

Quality Assurance

Courses offered include:

  • Statistical Concepts for QA Practitioners

This course focuses on three important applied areas of statistics and is arranged in three modules. Graphical Data Analysis contains no statistical formulae and concentrates on the visual examination of data. Statistical Hypothesis Testing contains the appropriate way to perform a test of hypothesis, and Non-Normality looks at how an analyst can determine if the data collected are reasonably Normal in distribution—a key assumption for many statistical tests.

Prerequisites: General understanding of data collection.

 

  • Data Quality Indicators

This is an introduction to the data quality indicators of precision, bias (accuracy), representativeness, comparability, completeness, and sensitivity. After an overview of the terms attention will be given to the most important: Representativeness, Precision, and Sensitivity.

Prerequisites: General understanding of data collection and quality assurance.
 

  • Introduction to Data Quality Objectives

Using the Data Quality Objectives (DQO) Process is the Agency's preferred way of collecting data for environmental decision making. This course gives a thorough overview of the Process through reference to Agency programs, the Agency Quality System, and the consequences of potential decision errors.

Prerequisites: General understanding of data collection.
 

  • Introduction to Data Quality Assessment

This course focuses on what a data quality assessment is and how to determine if one has been done appropriately. No statistics are used, the emphasis is on "what should be there" in order for a manager to have confidence in the conclusions presented for a study.

Prerequisites: General understanding of data collection.
 

  • Interpretation of Monitoring Data

This course focuses on what to do with monitoring data that clearly cannot be regarded as a standard random sample. The course aims for an understanding of the need to use systematic planning for data collection, how to use graphical techniques to analyze data, and what elementary statistics can be used to examine data sequences.

Prerequisites: General understanding of data collection and elementary statistical analysis.
 

  • Using VISUAL SAMPLING PLAN (VSP) to Obtain Samples

Deciding on what samples to collect, where to get them, and how different sampling methods impact on the prior selection of false rejection and false acceptance rates can be somewhat difficult to do in the abstract. Visual Sampling Plan (VSP) is a tool that enables planners to see where their samples will be collected and what impact changing scenarios will have on the overall project. The latest version of this inter-Agency developed project (version 2.0) will be shown as it contains most of the sampling schemes outlined in the guidance document "Choosing a Sampling Plan for Environmental Data Collection," EPA QA/G-5S.

Prerequisites: Completion of DQO and DQA courses.
 

  • Interpreting Multivariate Analysis

The complexity of the tasks facing environmental investigators require the simultaneous measurement of many variables. These variables are usually related to each other in unknown ways but current assessment practice ignores this and analyzes each variable independent of the others. Multivariate Analysis deals with the simultaneous relationships among variables in an attempt to realize a better understanding of the environmental problem. The course explains some of the complex statistical techniques with recourse to mathematics or statistical theory.

Prerequisites: General understanding of data collection and elementary statistical analysis.