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|Additional Physical Format:||Print version:
Goos, Peter., author.
Statistics with JMP.
Chichester, West Sussex ; Hoboken, NJ : John Wiley & Sons Inc., 2015
|Material Type:||Document, Internet resource|
|Document Type:||Internet Resource, Computer File|
|All Authors / Contributors:||
Peter Goos; David Meintrup
|ISBN:||9781119035756 1119035759 9781119035749 1119035740|
|Description:||1 online resource.|
|Contents:||Title Page; Copyright; Dedication; Preface; Software; Data files; 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; 2.2 The data matrix; 2.3 Representing univariate qualitative variables; 2.4 Representing univariate quantitative variables; 2.5 Representing bivariate data; 2.6 Representing time series; 2.7 The use of maps; 2.8 More graphical capabilities Chapter 3: Descriptive statistics of sample data3.1 Measures of central tendency or location; 3.2 Measures of relative location; 3.3 Measures of variation or spread; 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; 3.10 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' rule4.7 Simulating random experiments; Chapter 5: Additional aspects of probability theory; 5.1 Combinatorics; 5.2 Number of possible orders; 5.3 Applications of probability theory; 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; 6.5 Families of probability distributions and probability densities; 6.6 Simulation of random variables Chapter 7: Statistics of populations and processes7.1 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; 8.4 The hypergeometric distribution; 8.5 The Poisson distribution; 8.6 The geometric distribution; 8.7 The negative binomial distribution 8.8 Probability distributions in JMP8.9 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; 9.3 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; 10.3 Lognormal probability density|
|Responsibility:||Peter Goos, David Meintrup.|
For teachers of applied statistics, this book provides a rich resource of course material, examples and applications. (Zentralblatt MATH, 1 June 2015)