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Articles 1 - 2 of 2
Full-Text Articles in Applied Statistics
A Course In Data Science: R And Prediction Modeling, Adam Kapelner
A Course In Data Science: R And Prediction Modeling, Adam Kapelner
Open Educational Resources
This is a self-contained course in data science and machine learning using R. It covers philosophy of modeling with data, prediction via linear models, machine learning including support vector machines and random forests, probability estimation and asymmetric costs using logistic regression and probit regression, underfitting vs. overfitting, model validation, handling missingness and much more. There is formal instruction of data manipulation using dplyr and data.table, visualization using ggplot2 and statistical computing.
A Cost-Effective Method To Passively Sample Communities At The Forest Canopy-Aerosphere Interface, Michael Cunningham-Minnick, H. Patrick Roberts, Brian Kane Ph.D., Joan Milam, David I. King Ph.D.
A Cost-Effective Method To Passively Sample Communities At The Forest Canopy-Aerosphere Interface, Michael Cunningham-Minnick, H. Patrick Roberts, Brian Kane Ph.D., Joan Milam, David I. King Ph.D.
Data and Datasets
HOBO logger data of hourly measurements at canopy-aerosphere interface from June to August above temperate forest on campus of University of Massachusetts. Weather station data (precipitation and wind speeds) from nearby weather station extracted from Mesowest.com and needed for manuscript figures. Code (R language) to recreate foundation of figures in manuscript.