Judy Robertson, Maurits Kaptein's Modern Statistical Methods for HCI PDF

By Judy Robertson, Maurits Kaptein

ISBN-10: 3319266314

ISBN-13: 9783319266312

ISBN-10: 3319266330

ISBN-13: 9783319266336

This e-book severely displays on present statistical equipment utilized in Human-Computer interplay (HCI) and introduces a few novel easy methods to the reader.

Covering many ideas and methods for exploratory info research together with influence and gear calculations, experimental layout, occasion heritage research, non-parametric checking out and Bayesian inference; the learn contained during this booklet discusses tips to speak statistical effects quite, in addition to providing a common set of thoughts for authors and reviewers to enhance the standard of statistical research in HCI. every one bankruptcy provides [R] code for working analyses on HCI examples and explains how the implications will be interpreted.

Modern Statistical equipment for HCI is aimed toward researchers and graduate scholars who've a few wisdom of “traditional” null speculation importance checking out, yet who desire to increase their perform through the use of concepts that have lately emerged from information and comparable fields. This booklet significantly evaluates present practices in the box and helps a much less inflexible, procedural view of statistics in favour of reasonable statistical communique.

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Extra resources for Modern Statistical Methods for HCI

Example text

Statistical methods of inference allow us to do the following: given some hypothetical idea about our data generating process, we set out to actually collect data—our sample—and next answer questions about our data generating process. For example, we have a hypothetical description of a fair coin toss where P(H eads) = p k (1 − p)1−k (where k = {0, 1}, heads or tails). Next, we observe ten actual tosses (H, H, T, T, H, H, T, H, H, H— our sample). Based on this data we can start answering questions about the relationship between our hypothetical ideas of the data generating process and the actually observed data.

By Hadley Wickham (2009). ) by Deepayan Sarkar (2008). And finally, for more on missing data please consider Flexible Imputation of Missing Data by Stef van Buuren (2012). This book covers both the basic theory, as well as a large number of modern data-imputation methods. Chapter 2 Getting Started with [R]; a Brief Introduction Lianne Ippel Abstract In this chapter we provide some basics into [R] that will get you started and provide you with tools to continue the development of your skills in doing analyses in [R].

Before one reports, one needs to understand. This first part of the book therefore starts with an explanation of the software that is used throughout the book, with the minor exception of the specialist Bayes Factor software introduced in Chap. 9 all chapters use [R]. Second, we will have a look at data visualization: a key to understanding any dataset is the ability to explore it using both descriptive statistics as well as readable and intuitive plots. Finally, we end this part of the book by presenting methods that deal with “missingness” in a collected dataset: if the data you would like to analyze contains missing observations, likely you want to decide on how to deal with those prior to running your actual analysis.

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Modern Statistical Methods for HCI by Judy Robertson, Maurits Kaptein


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