Data analytics, statistics, and more
Multivariate statistical methods provide a means of exploring complex data sets for patterns and relationships from which hypotheses can be generated and subsequently tested. This post explores methods to manage non-detects when applying multivariate procedures to investigate (dis)similarities among data objects based on a set of descriptors.
Develope an unstratified, equal probability sample design and a stratified, equal probability sample design using Generalized Random Tessellation Stratified (GRTS) sampling.
Time-series analysis and forecasting is an important area of machine learning because many predictive learning problems involve a time component. This post examines time-series analysis using indoor air concentrations of trichloroethene and various explanatory varibles collected over time at a single location.
This post compares simple linear regression and multiple linear regression with and without shrinkage using an indoor air dataset consisting of trichloroethene concentrations and various explanatory variables, including radon concentration, temperature, barometric pressure, wind direction, and wind speed
This post explores linear regression with one-hot encoding. For those datasets with many categorical variables and where the categorical variables in turn have many unique levels, the number of features can quickly escalate. In these cases, label/ordinal encoding or some other alternative should be explored.