Statistics with JMP graphs, descriptive statistics, and probability
Peter Goos, Department of Statistics, University of Leuven, Faculty of Bio-Science Engineering and University of Antwerp, Faculty of Applied Economics, Belgium David Meintrup, Department of Mathematics and Statistics, University of Applied Sciences Ingolstadt, Faculty of Mechanical Engineering, Germ...
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| Format: | eBook |
| Language: | English |
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New York
Wiley
2015
John Wiley & Sons, Incorporated Wiley-Blackwell John Wiley & Sons, Ltd |
| Edition: | 1 |
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| ISBN: | 1119035708, 9781119035701, 1119035740, 9781119035749 |
| Online Access: | Get full text |
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Table of Contents:
- 8.8.1 Tables with probability distributions and cumulative distribution functions -- 8.8.2 Graphical representations -- 8.9 The simulation of discrete random variables with JMP -- Chapter 9 Important continuous probability densities -- 9.1 The continuous uniform density -- 9.2 The exponential density -- 9.2.1 Definition and statistics -- 9.2.2 Some interesting properties -- 9.3 The gamma density -- 9.4 The Weibull density -- 9.5 The beta density -- 9.6 Other densities -- 9.7 Graphical representations and probability calculations in JMP -- 9.8 Simulating continuous random variables in JMP -- Chapter 10 The normal distribution -- 10.1 The normal density -- 10.2 Calculation of probabilities for normally distributed variables -- 10.2.1 The standard normal distribution -- 10.2.2 General normally distributed variables -- 10.2.3 JMP -- 10.2.4 Examples -- 10.3 Lognormal probability density -- Chapter 11 Multivariate random variables -- 11.1 Introductory notions -- 11.2 Joint (discrete) probability distributions -- 11.3 Marginal or unconditional (discrete) probability distribution -- 11.4 Conditional (discrete) probability distribution -- 11.5 Examples of discrete bivariate random variables -- 11.6 The multinomial probability distribution -- 11.7 Joint (continuous) probability density -- 11.8 Marginal or unconditional (continuous) probability density -- 11.9 Conditional (continuous) probability density -- Chapter 12 Functions of several random variables -- 12.1 Functions of several random variables -- 12.2 Expected value of functions of several random variables -- 12.3 Conditional expected values -- 12.4 Probability distributions of functions of random variables -- 12.4.1 Discrete random variables -- 12.4.2 Continuous random variables -- 12.5 Functions of independent Poisson, normally, and lognormally distributed random variables
- Chapter 13 Covariance, correlation, and variance of linear functions -- 13.1 Covariance and correlation -- 13.2 Variance of linear functions of two random variables -- 13.3 Variance of linear functions of several random variables -- 13.4 Variance of linear functions of independent random variables -- 13.4.1 Two independent random variables -- 13.4.2 Several pairwise independent random variables -- 13.5 Linear functions of normally distributed random variables -- 13.6 Bivariate and multivariate normal density -- 13.6.1 Bivariate normal probability density -- 13.6.2 Graphical representations -- 13.6.3 Independence, marginal, and conditional densities -- 13.6.4 General multivariate normal density -- Chapter 14 The central limit theorem -- 14.1 Probability density of the sample mean from a normally distributed population -- 14.2 Probability distribution and density of the sample mean from a non-normally distributed population -- 14.2.1 Central limit theorem -- 14.2.2 Illustration of the central limit theorem -- 14.3 Applications -- 14.4 Normal approximation of the binomial distribution -- Appendix A The Greek alphabet -- Appendix B Binomial distribution -- Appendix C Poisson distribution -- Appendix D Exponential distribution -- Appendix E Standard normal distribution -- Index -- EULA
- Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgments -- Chapter 1 What is statistics? -- 1.1 Why statistics? -- 1.2 Definition of statistics -- 1.3 Examples -- 1.4 The subject of statistics -- 1.5 Probability -- 1.6 Software -- Chapter 2 Data and its representation -- 2.1 Types of data and measurement scales -- 2.1.1 Categorical or qualitative variables -- 2.1.2 Quantitative variables -- 2.1.3 Hierarchy of scales -- 2.1.4 Measurement scales in JMP -- 2.2 The data matrix -- 2.3 Representing univariate qualitative variables -- 2.4 Representing univariate quantitative variables -- 2.4.1 Stem and leaf diagram -- 2.4.2 Needle charts for univariate discrete quantitative variables -- 2.4.3 Histograms and frequency polygons for continuous variables -- 2.4.4 Empirical cumulative distribution functions -- 2.5 Representing bivariate data -- 2.5.1 Qualitative variables -- 2.5.2 Quantitative variables -- 2.6 Representing time series -- 2.7 The use of maps -- 2.8 More graphical capabilities -- Chapter 3 Descriptive statistics of sample data -- 3.1 Measures of central tendency or location -- 3.1.1 Median -- 3.1.2 Mode -- 3.1.3 Arithmetic mean -- 3.1.4 Geometric mean -- 3.2 Measures of relative location -- 3.2.1 Order statistics, quantiles, percentiles, deciles -- 3.2.2 Quartiles -- 3.3 Measures of variation or spread -- 3.3.1 Range -- 3.3.2 Interquartile range -- 3.3.3 Mean absolute deviation -- 3.3.4 Variance -- 3.3.5 Standard deviation -- 3.3.6 Coefficient of variation -- 3.3.7 Dispersion indices for nominal and ordinal variables -- 3.4 Measures of skewness -- 3.5 Kurtosis -- 3.6 Transformation and standardization of data -- 3.7 Box plots -- 3.8 Variability charts -- 3.9 Bivariate data -- 3.9.1 Covariance -- 3.9.2 Correlation -- 3.9.3 Rank correlation -- 3.10 Complementarity of statistics and graphics
- 3.11 Descriptive statistics using JMP -- Chapter 4 Probability -- 4.1 Random experiments -- 4.2 Definition of probability -- 4.3 Calculation rules -- 4.4 Conditional probability -- 4.5 Independent and dependent events -- 4.6 Total probability and Bayes' rule -- 4.7 Simulating random experiments -- Chapter 5 Additional aspects of probability theory -- 5.1 Combinatorics -- 5.1.1 Addition rule -- 5.1.2 Multiplication principle -- 5.1.3 Permutations -- 5.1.4 Combinations -- 5.2 Number of possible orders -- 5.2.1 Two different objects -- 5.2.2 More than two different objects -- 5.3 Applications of probability theory -- 5.3.1 Sequences of independent random experiments -- 5.3.2 Euromillions -- Chapter 6 Univariate random variables -- 6.1 Random variables and distribution functions -- 6.2 Discrete random variables and probability distributions -- 6.3 Continuous random variables and probability densities -- 6.4 Functions of random variables -- 6.4.1 Functions of one discrete random variable -- 6.4.2 Functions of one continuous random variable -- 6.5 Families of probability distributions and probability densities -- 6.6 Simulation of random variables -- Chapter 7 Statistics of populations and processes -- 7.1 Expected value of a random variable -- 7.2 Expected value of a function of a random variable -- 7.3 Special cases -- 7.4 Variance and standard deviation of a random variable -- 7.5 Other statistics -- 7.6 Moment generating functions -- Chapter 8 Important discrete probability distributions -- 8.1 The uniform distribution -- 8.2 The Bernoulli distribution -- 8.3 The binomial distribution -- 8.3.1 Probability distribution -- 8.3.2 Expected value and variance -- 8.4 The hypergeometric distribution -- 8.5 The Poisson distribution -- 8.6 The geometric distribution -- 8.7 The negative binomial distribution -- 8.8 Probability distributions in JMP