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Statistical Thinking

Name: Statistical Thinking
Brand: Princeton University Press
SKU: 9780691250939
Price: 133.24 EUR
Availability: InStock

Analyzing Data in an Uncertain World

Автор: Russell A. Poldrack

Език:

Корици: С твърди корици

Издател: Princeton University Press

Наличност: 50% вероятност

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133.24 € 260.60 лв

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

Информация за книгата

Автор

Russell A. Poldrack

Език

Английски език

Корици

Книга - С твърди корици

Издадена

2023

страници

248

EAN

9780691250939

ISBN

0691250936

Enbook ID

42143807

Издател

Princeton University Press

Теглоt

666

Размери

178 x 254 x 15

Пълно описание

An essential introduction to statistics for students of psychology and the social sciencesStatistical 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. Introduces statistics from a uniquely modern standpoint, helping students to use the basic ideas of statistics to analyze real dataPresents a model of statistics that ties together a broad range of statistical techniques that can be used to answer many different kinds of questionsExplains how to use statistics to generate reproducible findings and avoid common mistakes in statistical practiceIncludes a wealth of examples using real-world dataAccompanied 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