Data analytics, statistics, and more
In this post, we explore three dimensionality reduction techniques specifically used for data exploration and visualization: principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP).
In this post, we will use ordinal logistic regression to provide general contrasts on the log odds ratio scale as an alternative to nonparametric ANOVA. Proportional odds ordinal logistic regression is a generalization of the Wilcoxon and Kruskal-Wallis tests that extends to multiple covariates and interactions.
In this post, we will evaluate whether sample depth and/or site location affect arsenic concentrations measured in soil. To address non-normality and heteroscedasticity, two-way ANOVA will be performed using the rank-transformation of the data values.
This post summarizes influent flow data containing no measurement date for 10 wastewater treatment facilities.
Detection of a long-term, temporal trend in environmental data is affected by a number of factors, including the size of the trend to be detected, the time span of the data, and the magnitude of variability and autocorrelation of the noise in the data. This post evaluates the power of the Mann-Kendall test to identify a trend for various combinations of trend, variability, and sample size using Monte Carlo simulation.