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- Statistics with JMP by Peter Goos; David MeintrupCall Number: Available onlineISBN: 1119035759Publication Date: 2015-01-30Chapter 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

Thorough presentation of introductory statistics and probability theory, with numerous examples and applications using JMP, this book provides an accessible and thorough overview of the most important descriptive statistics for nominal, ordinal and quantitative data with particular attention to graphical representations. Throughout the book, the user-friendly, interactive statistical software package JMP is used for calculations, the computation of probabilities and the creation of figures. The examples are explained in detail, and accompanied by step-by-step instructions and screenshots. The reader will therefore develop an understanding of both the statistical theory and its applications. Traditional graphs such as needle charts, histograms and pie charts are included, as well as the more modern mosaic plots, bubble plots and heat maps. The authors discuss probability theory, particularly discrete probability distributions and continuous probability densities, including the binomial and Poisson distributions, and the exponential, normal and lognormal densities. They use numerous examples throughout to illustrate these distributions and densities.

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