Statistics is Easy! Second Edition

✍ Scribed by Dennis Shasha, Manda Wilson, Steven G. Krantz

• Publisher: Morgan & Claypool Publishers
• Year: 2010
• Tongue: English
• Leaves: 175
• Series: Synthesis Lectures on Mathematics and Statistics
• Edition: 2
• Category: Library

✦ Synopsis

Statistics is the activity of inferring results about a population given a sample. Historically, statistics books assume an underlying distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot normally be sure of the underlying distribution. For that reason, this book presents a distribution-independent approach to statistics based on a simple computational counting idea called resampling. This book explains the basic concepts of resampling, then system atically presents the standard statistical measures along with programs (in the language Python) to calculate them using resampling, and finally illustrates the use of the measures and programs in a case study. The text uses junior high school algebra and many examples to explain the concepts. Th e ideal reader has mastered at least elementary mathematics, likes to think procedurally, and is comfortable with computers. Table of Contents: The Basic Idea / Pragmatic Considerations when Using Resampling / Terminology / The Essential Stats / Case Study: New Mexico's 2004 Presidential Ballots / References / Bias Corrected Confidence Intervals / Appendix B

✦ Table of Contents

Synthesis Lectures on Mathematics and Statistics......Page 3
ABSTRACT......Page 6
ACKNOWLEDGEMENTS......Page 8
Introduction......Page 10
4 The Essential Stats 27......Page 12
Appendix B 81......Page 13
The Basic Idea......Page 14
Pragmatic Considerations When Using Resampling......Page 24
Terminology......Page 32
The Essential Stats......Page 40
4.2.3 Example......Page 41
4.3.2 Calculate with example......Page 42
4.3.3 Pseudocode & code......Page 43
4.3.4 Calculate with example for multiple variables......Page 44
4.3.5 Pseudocode & code......Page 45
4.4.2 Calculate with Example......Page 46
4.5.1 Why and when......Page 49
4.5.2 Calculate with example......Page 50
4.6.1 Why and when......Page 54
4.6.2 Calculate with example......Page 55
4.6.3 Pseudocode & code......Page 59
4.7.2 Calculate with example......Page 60
4.8.2 Calculate & example......Page 62
4.10.1 Why and when......Page 65
4.10.3 False Discovery Rate......Page 66
5.1 Take a close look at the data......Page 72
5.1.1 What questions do we want to ask?......Page 76
5.1.2 How do we attempt to answer this question?......Page 77
5.1.3 Next: effect of ethnicity for each machine type......Page 79
5.1.5 What did we find out?......Page 86
References......Page 88
Bias Corrected Confidence Intervals......Page 90
Coinsig.py......Page 94
Diff2MeanSig.py......Page 96
Diff2MeanConf.py......Page 99
Diff2MeanConfCorr.py......Page 102
MeanConf.py......Page 106
ChiSquaredOne.py......Page 111
ChiSquaredMulti.py......Page 114
FishersExactTestSig.py......Page 117
OneWayAnovaSig.py......Page 122
OneWayAnovaConf.py......Page 127
TwoWayAnovaSig.py......Page 134
TwoWayAnovaConf.py......Page 140
RegressionSig.py......Page 149
RegressionConf.py......Page 153
CorrelationSig.py......Page 159
CorrelationConf.py......Page 162
machineANOVAcs.vals......Page 167
push_button.vals......Page 169
touch_screen.vals......Page 172
25to50.vals......Page 173
75to100.vals......Page 174