Based on the lecture notes from STA404: Clinical Biostatistics. […] “Medicine is a science of uncertainty and an art of probability.” William Osler (1849-1919). Biostatistics is a fundamental discipline at the core of modern health data science (Lee et al. 2019). It is the science of managing medical uncertainty and biostatistical methods play a key role in the scientific assessment of the main areas of medical practice: Leonhard Held … Read more →

This involves a series of coding sessions for ST429. […] Coding sessions for ST429 are included here. In the programming sessions, we’ll put into practice the methods covered in the lectures/seminars to resolve risk management … Read more →

STA 444/5 - Introductory Data Science using R […] This book is intended for use during the STA 444/445 courses at Northern Arizona University. The book is broken into two sections based on the related course material. The STA 444 section covers basic introductory content for getting started with statistical programming in R. This course is intended for students of all backgrounds and pairs importantly with courses such as STA 570 (Statistical Methods I) and STA 471 (Regression Analysis). The first section covers details to allow students to work on basic statistical programming while … Read more →

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 →

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 →

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 →

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 atopenintro.org/book/ims. This textbook is itself a derivative of OpenIntro … Read more →

A BOOK FOR UNDERGRADUATE PROGRAMME IN AGRICULTURE […] Welcome to the book LECTURE NOTES ON STATISTICAL METHODS AND … Read more →

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 →

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 →

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 […] 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 →

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: jkang37@ucsc.edu; Office: E2 516 (by appointment only). Office Hour: Thursday 5:00 pm - 6:00 pm at BE 118 Discussion Section: Section A: … Read more →

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 →