Statistical Methods

Statistical Methods for Environmental Mixtures

by Andrea Bellavia

Statistical Methods for Environmental Mixtures

Humans are simultaneously exposed to a large number of environmental hazards. To allow a more accurate identification of the risks associated with environmental exposures and developing more targeted public health interventions, it is crucial that population-based studies account for the complexity of such exposures as environmental mixtures. This poses several analytic challenges and often requires the use of extensions of standard regression approaches or more flexible techinques for high-dimensional data. This document presents an extended version of the class material that was used in an introductory two-weeks course on statistical approaches for environmental mixtures. The main challanges and limitations of standard regression techniques are outlined, and recent methodological developments are introduced in a rigorous yet non-theoretical way. The course was designed for students and postdocs in environmental health with basic preliminary knoweldge on linear and logistic regression models. Sources and code examples to conduct a thorough analysis in R are also included. Read more →


ST429 - Statistical Methods for Risk Management

by Xiaolin Zhu


This involves a series of coding sessions for ST429. […] Coding sessions for ST429 are included … Read more →



by Robert B. Gramacy


Surrogates: a new graduate level textbook on topics lying at the interface between machine learning, spatial statistics, computer simulation, meta-modeling (i.e., emulation), and design of experiments. Gaussian process emphasis facilitates flexible nonparametric and nonlinear modeling, with applications to uncertainty quantification, sensitivity analysis, calibration of computer models to field data, sequential design and (blackbox) optimization under uncertainty. Presentation targets numerically competent scientists in the engineering, physical, and biological sciences. Treatment includes historical perspective and canonical examples, but primarily concentrates on modern statistical methods, computation and implementation in R at modern scale. Rmarkdown facilitates a fully reproducible tour complete with motivation from, application to, and illustration with, compelling real-data examples. Read more →


STAT 331

by Ben Prytherch


Ben Prytherch STAT 331, as the title states, is an “applied” statistics course. It is intended for anyone who has taken at least one introductory level statistics course, and who wants to learn more about the use of statistical methods in quantitative research. It covers many statistical tools that are usually considered too advanced for an introductory level class, but are nonetheless very popular. It also provides guidance on making data analysis decisions. Most assignments will involve looking up a published scientific paper for which the data are available and reproducing the main … Read more →


Notes for Predictive Modeling

by Eduardo García-Portugués

Notes for Predictive Modeling

Notes for Predictive Modeling. MSc in Big Data Analytics. Carlos III University of Madrid. [...] Welcome to the notes for Predictive Modeling. The course is part of the MSc in Big Data Analytics from Carlos III University of Madrid. The course is designed to have, roughly, one session per main topic in the syllabus. The schedule is tight due to time constraints, which will inevitably make the treatment of certain methods somehow superficial. Nevertheless, the course will hopefully give you a respectable panoramic view of different available statistical methods for predictive modeling. ... Read more →


Modern Statistical Methods for Psychology

by Mine Çetinkaya-Rundel and Johanna Hardin, tuned by Gregory Cox

Modern Statistical Methods for Psychology

This is the website for Modern Statistical Methods for Psychology, a modified version of Introduction to Modern Statistics, First Edition by Mine Çetinkaya-Rundel and Johanna Hardin, as modified by Gregory Cox. The original Introduction to Modern Statistics is a textbook from the OpenIntro project. — Version date of this modification: May 24, 2022. The original version of the Introduction to Modern Statistics textbook and its supplements, including slides, labs, and interactive tutorials, may be downloaded for free This textbook is itself a derivative of OpenIntro … Read more →



by Dr. Pratheesh P. Gopinath




Lab notes for Statistics for Social Sciences II: Multivariate Techniques

by Eduardo García-Portugués


Lab notes for Statistics for Social Sciences II: Multivariate Techniques […] Welcome to the lab notes for Statistics for Social Sciences II: Multivariate Techniques. Along these notes we will see how to effectively implement the statistical methods presented in the lectures. The exposition we will follow is based on learning by analyzing datasets and real-case studies, always with the help of statistical software. While doing so, we will illustrate the key insights of some multivariate techniques and the adequate use of advanced statistical software. Be advised that these notes are neither … Read more →


An Introduction to Acceptance Sampling and SPC with R

by John Lawson


The output format for this book is bookdown::gitbook. […] This book is an introduction to statistical methods used in monitoring, controlling, and improving quality. Topics covered are: acceptance sampling; Shewhart control charts for Phase I studies; graphical and statistical tools for discovering and eliminating the cause of out-of-control-conditions; Cusum and EWMA control charts for Phase II process monitoring; design and analysis of experiments for process troubleshooting and discovering ways to improve process output; and multivariate control charts for Phase I and Phase II studies … Read more →


Statistical Methods II

by Derek L. Sonderegger


The second semester of an Intro Stats course designed for graduate students in Biology, Forestry, Ecology, etc. […] These notes are intended to be used in the second semester of a two-semester sequence of Statistical Methodology. We assume that students have seen t-tests, Simple Regression, and ANOVA. The second semester emphasizes the uniform matrix notation (y = X\beta + \epsilon) and the interpretation of the coefficients. We cover model diagnostics, transformation, model selection, interactions of continuous and categorical predictors as well as introduce random effects in the … Read more →


Multivariate Statistical Analysis with R: PCA & Friends making a Hotdog

by Brian Nguyen

Multivariate Statistical Analysis with R: PCA & Friends making a Hotdog

Multivariate Statistical Analysis with R: PCA & Friends making a Hotdog […] Multivariate Analysis has been developed and evolved through many iterations by many different disciplines. Virtually all scientific domains need to use statistical methods under the Multivariate umbrella to analyze data with more than 1 variable. Thus, Multivariate Analysis has gotten many names and has been customized by many “-metric” disciplines throughout the years. Overall, Multivariate Analysis explore the relationships between observations and/or variables in a multivariate dataset. The strategies commonly … Read more →


STAT 7: Discussion Section Materials

by Jizhou Kang


This book contains all materials for my TA STAT 7: Statistical Methods for the Biological,Environmental, and Health Sciences at UCSC, Winter 2020. […] Course Title: Statistical Methods for the Biological, Environmental, and Health Sciences Instructor: Dr. Rajarshi Guhaniyogi TA: Jizhou ‘Joe’ Kang Bio: I’m a first year Ph.D. student at our statsitics department. This is my second time serving as TA for STAT 7, and fourth time as TA. Contact Info: Email:; Office: E2 516 (by appointment only). Office Hour: Thursday 5:00 pm - 6:00 pm at BE 118 Discussion Section: Section A: … Read more →


An Incomplete Solutions Guide to the NIST/SEMATECH e-Handbook of Statistical Methods

by Ray Hoobler


Analysis of case studies and exercies with a focus on using the tidyverse and ggplot2. This handbook was created using the bookdown package in RStudio. The output format for this example is bookdown::gitbook. […] Exploratory Data Analysis (EDA) is a philosophy on how to work with data, and for many applications, the workflow is better suited for scientist and engineers. As a scientist, we are trained to formulate a hypothesis and design a series of experiments that allow us to test the hypothesis effectively. Most data, however, doesn’t come from carefully controlled trials, but from … Read more →