Название: Statistical Thinking: Analyzing Data in an Uncertain World
Автор: Russell A. Poldrack
Издательство: Princeton University Press
Год: 2023
Страниц: 281
Язык: английский
Формат: pdf (true)
Размер: 50.5 MB

An essential introduction to statistics for students of psychology and the social sciences.

Statistical thinking is increasingly essential to understanding our complex world and making informed decisions based on uncertain data. This incisive undergraduate textbook introduces students to the main ideas of statistics in a way that focuses on deep comprehension rather than rote application or mathematical immersion. The presentation of statistical concepts is thoroughly modern, sharing cutting-edge ideas from the fields of machine learning and data science that help students effectively use statistical methods to ask questions about data. Statistical Thinking provides the tools to describe complex patterns that emerge from data and to make accurate predictions and decisions based on data.

The only way to really learn statistics is to do statistics. While many statistics courses historically have been taught using point-and-click statistical software, it is increasingly common for statistical education to use open source languages in which students can code their own analyses. I think that being able to code one’s analyses is essential in order to gain a deep appreciation for statistical analysis, which is why the students in my course at Stanford are expected to learn to use the R statistical programming language to analyze data, alongside the theoretical knowledge that they learn from this book. There are two openly available companions to this textbook that can help the reader get started learning to program; one focuses on the R programming language, and another focuses on the Python language.

Introduces statistics from a uniquely modern standpoint, helping students to use the basic ideas of statistics to analyze real data
Presents a model of statistics that ties together a broad range of statistical techniques that can be used to answer many different kinds of questions
Explains how to use statistics to generate reproducible findings and avoid common mistakes in statistical practice
Includes a wealth of examples using real-world data
Accompanied by computer code in R and in Python―freely available online―that enables students to see how each example is generated and to code their own analyses




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