approx. 45 mins

R is one of the most widely used statistics software packages in the world. Its versatility as a programming language and its interconnectivity with email, web page generation and other computer processes make it a bit daunting for people just starting to use it for data analysis. It need not be that way. This webinar introduces you to R software and its use for data analysis. You'll learn how to type commands, install and load packages, and use the pull-down menus of R Commander (Rcmdr) to compute confidence intervals and a test for whether the mean exceeds a numerical standard.

approx. 64 mins

pdf of Powerpoint slides

.zip file of the two scripts and three data sets used in our Intro to R webinar

Traditional parametric tests for differences in means (Analysis of Variance, t-tests and more) as well as t-intervals require data within groups to follow a normal distribution. If this isn't so, p-values may be inflated so that differences in means are not detected, and confidence intervals are often too wide. Permutation tests and bootstrap intervals avoid the normality assumption, returning accurate p-values and interval widths while being distribution-free. These methods are widely used in a variety of applied statistics fields including environmental science, but have not been sufficiently used in water quality, air quality and soils applications. This webinar will describe how these methods work, where you can find them, and demonstrate their benefits over older traditional methods.presented in this webinar.

approx. 60 mins

3. Which of These Things is Not Like the Others?

How Multiple Comparison Tests Work

Multiple comparison tests determine which groups differ from others. Why are they needed following an ANOVA or Kruskal-Wallis test? How do they work? There are familiar types such as Tukey's test, and a newish version called the False Discovery Rate. Learn why the False Discovery Rate is a method you should probably be using.

approx. 50 mins

How Multiple Comparison Tests Work

Multiple comparison tests determine which groups differ from others. Why are they needed following an ANOVA or Kruskal-Wallis test? How do they work? There are familiar types such as Tukey's test, and a newish version called the False Discovery Rate. Learn why the False Discovery Rate is a method you should probably be using.

approx. 50 mins

approx. 65 mins

approx. 60 mins

Seven common errors in statistical analysis by environmental scientists all stem from an outdated understanding of statistics. I'll define the seven 'perilous errors' and how each can be avoided. They revolve around old ideas about hypothesis tests, p-values, using logarithms of data, evaluating what is a good regression equation, evaluating outliers and dealing with nondetects. Understanding why each error is perilous can save the scientist from publishing incorrect statements, using inefficient analysis methods, and wasting scarce financial resources. These errors have persisted through the years -- break the cycle and step into the 21st Century.

approx. 63 mins (9 minutes per error!)