Statistics For Hci

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Statistics for HCI

Statistics for HCI
Author :
Publisher : Morgan & Claypool Publishers
Total Pages : 183
Release :
ISBN-10 : 9781681737447
ISBN-13 : 1681737442
Rating : 4/5 (442 Downloads)

Book Synopsis Statistics for HCI by : Alan Dix

Download or read book Statistics for HCI written by Alan Dix and published by Morgan & Claypool Publishers. This book was released on 2020-04-10 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many people find statistics confusing, and perhaps even more confusing given recent publicity about problems with traditional p-values and alternative statistical techniques including confidence intervals and Bayesian statistics. This book aims to help readers navigate this morass: to understand the debates, to be able to read and assess other people's statistical reports, and make appropriate choices when designing and analysing their own experiments, empirical studies, and other forms of quantitative data gathering.


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Statistics for HCI
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Authors: Alan Dix
Categories: Computers
Type: BOOK - Published: 2020-04-10 - Publisher: Morgan & Claypool Publishers

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Many people find statistics confusing, and perhaps even more confusing given recent publicity about problems with traditional p-values and alternative statistic
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This book critically reflects on current statistical methods used in Human-Computer Interaction (HCI) and introduces a number of novel methods to the reader. Co
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Type: BOOK - Published: 2019-02-07 - Publisher: Cambridge University Press

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This book addresses common questions from HCI researchers when trying to do statistical analysis on their data.
The Wiley Handbook of Human Computer Interaction Set
Language: de
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Type: BOOK - Published: 2017-12-28 - Publisher: John Wiley & Sons

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In der Vergangenheit war die Mensch-Computer-Interaktion (Human-Computer Interaction) das Privileg einiger weniger. Heute ist Computertechnologie weit verbreite
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This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.