http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Synthesis digital library of engineering and computer science
1 online resource (xii, 162 pages : illustrations)
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 systematically 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. The ideal reader has mastered at least elementary mathematics, likes to think procedurally, and is comfortable with computers.
2. Pragmatic considerations when using resampling.
4. The essential stats -- 4.1. Mean -- Why and when -- Calculate -- Example -- Pseudocode & code -- 4.2. Difference between two means -- Why and when -- Calculate -- Example -- Pseudocode & code -- 4.3. Chi-squared -- Why and when -- Calculate with example -- Pseudocode & code -- Calculate with example for multiple variables -- Pseudocode & code -- 4.4. Fisher's exact test -- Why and when -- Calculate with example -- Pseudocode & code -- 4.5. One-way ANOVA -- Why and when -- Calculate with example -- Statistics is easy -- Pseudocode & code -- 4.6. Multi-way ANOVA -- Why and when -- Calculate with example -- Pseudocode & code -- 4.7. Linear regression -- Why and when -- Calculate with example -- Pseudocode & code -- 4.8. Linear correlation -- Why and when -- Calculate & example -- Pseudocode & code -- 4.9. Multiple regression -- 4.10. Multiple testing -- Why and when -- Family wise error rate -- False discovery rate.
5. Case study: New Mexico's 2004 presidential ballots -- 5.1. Take a close look at the data -- What questions do we want to ask -- How do we attempt to answer this question -- Next: effect of ethnicity for each machine type -- We have used the following techniques -- What did we find out?
References -- Bias corrected confidence intervals -- Appendix B.
Part of: Synthesis digital library of engineering and computer science.
Title from PDF title page (viewed Oct. 13, 2010).
Includes bibliographical references (pages 75-76).
[San Rafael, Calif.] : Morgan & Claypool, 2010
Synthesis lectures on mathematics and statistics ; #8