Illuminating Statistical Analysis Using Scenarios and Simulations

Illuminating Statistical Analysis Using Scenarios and Simulations
Illuminating Statistical Analysis Using Scenarios and Simulations
by 作者: Jeffrey E. Kottemann
ISBN-10 书号: 1119296331
ISBN-13 书号: 9781119296331
Edition 版本: 1
Publisher Finelybook 出版日期: 2017-03-06
Pages: 312
Features an integrated approach of statistical scenarios and simulations to aid readers in developing key intuitions needed to understand the wide ranging concepts and methods of statistics and inference
Illuminating Statistical Analysis Using Scenarios and Simulations presents the basic concepts of statistics and statistical inference using the dual mechanisms of scenarios and simulations. This approach helps readers develop key intuitions and deep understandings of statistical analysis. Scenario-specific sampling simulations depict the results that would be obtained by a very large number of individuals investigating the same scenario,each with their own evidence,while graphical depictions of the simulation results present clear and direct pathways to intuitive methods for statistical inference. These intuitive methods can then be easily linked to traditional formulaic methods,and the author does not simply explain the linkages,but rather provides demonstrations throughout for a broad range of statistical phenomena. In addition,induction and deduction are repeatedly interwoven,which fosters a natural “need to know basis” for ordering the topic coverage.
Examining computer simulation results is central to the discussion and provides an illustrative way to (re)discover the properties of sample statistics,the role of chance,and to (re)invent corresponding principles of statistical inference. In addition,the simulation results foreshadow the various mathematical formulas that underlie statistical analysis.
In addition,this book:
Features both an intuitive and analytical perspective and includes a broad introduction to the use of Monte Carlo simulation and formulaic methods for statistical analysis
Presents straight-forward coverage of the essentials of basic statistics and ensures proper understanding of key concepts such as sampling distributions,the effects of sample size and variance on uncertainty,analysis of proportion,mean and rank differences,covariance,correlation,and regression
Introduces advanced topics such as Bayesian statistics,data mining,model cross-validation,robust regression,and resampling
Contains numerous example problems in each chapter with detailed solutions as well as an appendix that serves as a manual for constructing simulations quickly and easily using Microsoft® Office Excel®
Illuminating Statistical Analysis Using Scenarios and Simulations is an ideal textbook for courses,seminars,and workshops in statistics and statistical inference and is appropriate for self-study as well. The book also serves as a thought-provoking treatise for researchers,scientists,managers,technicians,and others with a keen interest in statistical analysis.
Partl: Sample Proportionsand the Normal Distribution
Chapter 1: Evidence and Verdicts
Chapter 2: Judging Coinsl
.G.o..
Chapter 3: Brief on BellShapes
Chapter4: Judging Coinsll
Chapter 5: Amount of Evidencel
Chapter 6: Variance of Evidencel
Chapter 7: Judging Opinion Splitsl
Chapter 8: Amount of Evidencell
Chapter 9: Variance ofEvidence ll
Chapter 10: JudgingOpinion Splitsl
Chapter 11: lt Has Been the Normal Distribution All Along
Chapter 12: Judging Opinion Split Differences
Chapter 13: Rescaling to Standard Errors
Chapter 14: The Standardized Normal Distribution Histogram
Chapter 15: The z-Distribution
Chapter 16: Brief on Two-Tail Versus One-Tail
Chapter 17: Brief on Type l Versus Type llErors
C…
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Partll: Sample Means and the Normal Distribution
Chapter 18: Scaled Data and Sample Means
Chapter 19: Distribution of Random Sample Means
Chapter 20: Amount of Evidence
Chapter 21: Variance of Evidence
Chapter 22: Homing in on the Population Meanl
Chapter 23: Homing in on the Population Mean lⅡ
Chapter 24: Homing in on the Population Mean ll
Chapter 25: Judging Mean Differences
Chapter 26: Sample Size,Variance,and Uncertainty
Chapter 27: Thet-Distribution
Part ll: Multiple Proportions and Means: The X2-and F-Distributions
Chapter 28: Multiple Proportions and the X2-Distribution
Chapter 29: Facing Degrees of Freedom
Chapter 30: Multiple Proportions: Goodness of Fit
LOD· WOUUOOI. LO
Chapter 31: Two-Way Proportions: Homogeneity
Chapter 32: Two-Way Proportions: Independence
Chapter 33: Variance Ratios and the F-Distribution
Chapter 34: Multiple Means and Variance Ratios: ANOVA
Chapter 35: Two-Way Means and Variance Ratios: ANOVA
Part IV: Linear Associations: Covariance,Correlation,and Regression
Chapter 36: Covariance
Chapter 37: Correlation
: Chapter 38: What Correlations Happen Just by Chance?
Chapter 39: Judging Correlation Differences
Chapter 40: Corelation with Mixed Data Types
Chapter 41: A Simple Regression Prediction Model
Chapter 42: Using Binomials Too
Chapter 43: A Multiple Regression Prediction Model
Chapter 44: Loose EndI(Collinearity)
Chapter 45: Loose End ll(SquaringR)
Chapter 46: Loose End ll(Adjusting R-Squared)
Chapter 47: Reality Strikes
Part V: Dealing with Unruly Scaled Data
Chapter 48: Obstaclesand Maneuvers
Chapter 49: Ordered Ranking Maneuver
Chapter 50: What Rank Sums Happen Just by Chance?
Chapter 51: Judging Rank Sum Differences
Chapter 52: Other Methods Using Ranks
Chapter 53: Transforming the Scale of Scaled Data
Chapter 54: Brief on Robust Regression
Chapter 55: Brief on Simulation and Resampling
Part VI: Review and Additional Concepts
Chapter 56: For Partl
Chapter 57: For Partll
Chapter 58: For Partll
Chapter 59: For PartIV
Chapter 60: For PartV


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