Chapter 9 Section 1.ppt
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This document provides an overview and preview of Chapter 9 from the textbook Elementary Statistics. Chapter 9 discusses methods for making statistical inferences when there are two samples or populations, extending the techniques introduced in Chapters 7 and 8 which dealt with single samples or populations. The chapter covers topics like comparing two proportions, comparing two means from independent samples, comparing two dependent samples using matched pairs, and comparing two variances or standard deviations. Examples of applications discussed include comparing weight gain in college freshmen, comparing polio rates in children given a vaccine or placebo, and comparing cholesterol levels in subjects given Lipitor or a placebo.
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Copyright © 2017, 2014, 2011 Pearson Education, Inc.
Chapter 10 - *
Two-Sample Tests
Chapter 10
Copyright © 2017, 2014, 2011 Pearson Education, Inc.
Chapter 10 - *
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In this chapter, you learn: How to compare the means of two independent populations.
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Copyright © 2017, 2014, 2011 Pearson Education, Inc.
Chapter 10 - *
Online TopicEffect Size: Section 10.5.
Copyright © 2017, 2014, 2011 Pearson Education, Inc.
Chapter 10 - *
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Accounting for Variance in Machine Learning Benchmarks
Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent
Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.
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- 1. Section 9.1-‹#› Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series by Mario F. Triola
- 2. Section 9.1-‹#› Copyright © 2014, 2012, 2010 Pearson Education, Inc. Chapter 9 Inferences from Two Samples 9-1 Review and Preview 9-2 Two Proportions 9-3 Two Means: Independent Samples 9-4 Two Dependent Samples (Matched Pairs) 9-5 Two Variances or Standard Deviations
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