Data analytics, statistics, and more
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.
With the rapid spread in the novel coronavirus across countries, the World Health Organisation and several countries have published latest results on the impact of COVID-19 over the past few months. The objective of this post is to demonstrate how visualization using the R programmin language helps to derive informative insights from data sources.