Data analytics, statistics, and more
Mass-based analyses of groundwater contaminants provide complementary information not readily quantified using single-well analytics. This post describes methods that can be used to evaluate contaminant concentrations measured in wells to determine how plume mass and plume center-of-mass change through time.
Left-censored environmental data are problematic because censored (nondetect) values are known only to range between zero and the detection or reporting limit. Fortunately, methods are available for analyzing data containing a mixture of detects and nondetects that make few or no assumptions about the data, or that substitute arbitrary values for the nondetects.
The Mahalanobis distance is a statistical technique that can be used to measure how distant a point is from the centroid of the data. Mahalanobis distances can be converted into probabilities using a chi-squared distribution. By specifying a significance level, this process is commonly used as an outlier detection method.
This post performs exploratory data analysis using time-series plots to visually identify essential economic information contained in the U.S. Bureau of Labor Statistics Employment Situation Report for February 2022.
This is a brief tutorial on using R and the trendMK package for the statistical analysis of groundwater monitoring data. The trendMK package is designed to analyze censored data sets containing many sampling locations and monitoring constituents.