Introduction to Modern Statistics (original) (raw)
Introduction to
Modern Statistics
Bringing a fresh approach to intro statistics, IMS introduces multi-dimensional thinking early on and uses both simulation techniques and traditional methods
Introduction to Modern Statistics is a re-imagining of a previous title, Introduction to Statistics with Randomization and Simulation. The new book puts a heavy emphasis on exploratory data analysis (specifically exploring multivariate relationships using visualization, summarization, and descriptive models) and provides a thorough discussion of simulation-based inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches. The second edition of IMS has updated datasets, additional exercises, a new application for chapter 3, and updated text and code to reflect changes in best practices. Other highlights include:
Web native book. The online book is available in HTML, which offers easy navigation and searchability in the browser. The book is built with the bookdown package and the source code to reproduce the book can be found on GitHub. Along with the bookdown site, this book is also available as a PDF and in paperback. Read the book online here.
Tutorials. While the main text of the book is agnostic to statistical software and computing language, each part features 4-8 interactive R tutorials (for a total of 32 tutorials) that walk you through the implementation of the part content in R with the tidyverse for data wrangling and visualisation and the _tidyverse_-friendly infer package for inference. The self-paced and interactive R tutorials were developed using the learnr R package, and only an internet browser is needed to complete them. Browse the tutorials here.
Labs. Each part also features 1-2 R based labs. The labs consist of data analysis case studies and they also make heavy use of the tidyverse and infer packages. View the labs here.
Datasets. Datasets used in the book are marked with a link to where you can find the raw data. The majority of these point to the openintro package. You can install the openintro package from CRAN or get the development version on GitHub. Find out more about the package here.
Getting Started
Teachers
Teachers: Sample Exams
Restricted to Verified Teachers only. The sample exams currently available are from our other textbooks. We plan to add sample exams for IMS soon.
ISRS, Sample Midterms and Final Exam (Albert Kim) Available to Verified Teachers, click here to apply for access OpenIntro Statistics Exams, Set 1 Available to Verified Teachers, click here to apply for access Openintro Statistics Exams, Set 2 Available to Verified Teachers, click here to apply for access OpenIntro Statistics, Sample Exams (Adam Gilbert) Available to Verified Teachers, click here to apply for access Multiple choice exam question bank (RExams) Available to Verified Teachers, click here to apply for access ISLBS, Sample Midterm and Final Exams (Julie Vu) Available to Verified Teachers, click here to apply for access
What is Statistics?
Part 1: Introduction to data
Part 2: Exploratory data analysis
Part 3: Regression modeling
Part 4: Foundations of inference
Tutorial: Foundations of inference Online interactive R tutorial Lab - Intro to inference Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata Lab - Confidence levels Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
Part 5: Statistical inference
Tutorial: Statistical inference Online interactive R tutorial Lab - Inference for categorical data Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata Lab - Inference for numerical data Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata Sample size and power (one-sample) Supplemental section: on power in the one-sample scenario Better understand ANOVA calculations Supplemental section: Details behind ANOVA