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Index
Cover
Schaum's Outline of Probability, Second Edition
Copyright Page
Preface
Contents
Chapter 1 Set Theory 1
1.1 Introduction.
1.2 Sets and Elements, Subsets.
1.3 Venn Diagrams.
1.4 Set Operations.
1.5 Finite and Countable Sets.
1.6 Counting Elements in Finite Sets, Inclusion-Exclusion Principle.
1.7 Products Sets.
1.8 Classes of Sets, Power Sets, Partitions.
1.9 Mathematical Induction.
Chapter 2 Techniques of Counting
2.1 Introduction.
2.2 Basic Counting Principles.
2.3 Factorial Notation.
2.4 Binomial Coefficients.
2.5 Permutations.
2.6 Combinations.
2.7 Tree Diagrams.
Chapter 3 Introduction to Probability
3.1 Introduction.
3.2 Sample Space and Events.
3.3 Axioms of Probability.
3.4 Finite Probability Spaces.
3.5 Infinite Sample Spaces.
3.6 Classical Birthday Problem.
Chapter 4 Conditional Probability and Independence
4.1 Introduction
4.2 Conditional Probability.
4.3 Finite Stochastic and Tree Diagrams.
4.4 Partitions, Total Probability, and Bayes’ Formula.
4.5 Independent Events.
4.6 Independent Repeated Trials.
Chapter 5 Random Variables
5.1 Introduction.
5.2 Random Variables.
5.3 Probability Distribution of a Finite Random Variable.
5.4 Expectation of a Finite Random Variable.
5.5 Variance and Standard Deviation.
5.6 Joint Distribution of Random Variables.
5.7 Independent Random Variables.
5.8 Functions of a Random Variable.
5.9 Discrete Random Variables in General.
5.10 Continuous Random Variables.
5.11 Cumulative Distribution Function.
5.12 Chebyshev’s Inequality and the Law of Large Numbers.
Chapter 6 Binomial and Normal Distributions
6.1 Introduction.
6.2 Bernoulli Trials, Binomial Distribution.
6.3 Normal Distribution.
6.4 Evaluating Normal Probabilities.
6.5 Normal Approximation of the Binomial Distribution.
6.6 Calculations of Binomial Probabilities Using the Normal Approximation.
6.7 Poisson Distribution.
6.8 Miscellaneous Discrete Random Variables.
6.9 Miscellaneous Continuous Random Variables.
Chapter 7 Markov Processes
7.1 Introduction.
7.2 Vectors and Matrices.
7.3 Probability Vectors and Stochastic Matrices.
7.4 Transition Matrix of a Markov Process.
7.5 State Distributions.
7.6 Regular Markov Processes and Stationary State Distributions.
Appendix A Descriptive Statistics
A.1 Introduction.
A.2 Frequency Tables, Histograms.
A.3 Measures of Central Tendency; Mean and Median.
A.4 Measures of Dispersion: Variance and Standard Deviation.
A.5 Bivariate Data, Scatterplots, Correlation Coefficients.
A.6 Methods of Least Squares, Regression Line, Curve Fitting.
Appendix B Chi-Square Distribution
B.1 Introduction.
B.2 Goodness of Fit, Null Hypothesis, Critical Values.
B.3 Goodness of Fit for Uniform and Prior Distributions.
B.4 Goodness of Fit for Binomial Distribution.
B.5 Goodness of Fit for Normal Distribution.
B.6 Chi-Square Test for Independence.
B.7 Chi-Square Test for Homogeneity.
Index
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