Probability and Statistics 897 Fully Solved Problems

ISBN-10: 007179557X

ISBN-13: 9780071795579

Edition: 4th 2013

Authors: John J. Schiller, R. Alu Srinivasan, Murray R. Spiegel

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The ideal review for your probability and statistics courseMore than 40 million students have trusted Schaum’s Outlines for their expert knowledge and helpful solved problems. Written by renowned experts in this field,Schaum's Outline of Probability and Statisticscovers what you need to know for your course and, more important, your exams. Step-by-step, the authors walk you through coming up with solutions to exercises in this topic.760 fully-worked problems solved step-by-stepHundreds of additional practice problems with answersCoverage of all course fundamentalsEasy to understand methodologyClear, concise explanations of all probability and statistics conceptsAppropriate for the following courses: introduction to probability & statistics, probability, business statistics, basic statistics, and beginning statisticsEasily understood review of basic probability and statistics principlesSupports all the major textbooks for the introductory probability and statistics course
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Book details

List price: $24.00
Edition: 4th
Copyright year: 2013
Publisher: McGraw-Hill Education
Publication date: 12/6/2012
Binding: Paperback
Pages: 432
Size: 8.00" wide x 10.50" long x 0.75" tall
Weight: 1.584
Language: English

John J. Schiller, is an Associate Professor of Mathematics at Temple University. He received his Ph.D. at the University of Pennsylvania and has published research papers in the areas of Riemann surfaces, discrete mathematics biology. He has also coauthored texts in finite mathematics, precalculus, and calculus.

Murray Speigel, Ph.D., was Former Professor and Chairman of the Mathematics Department at Rensselaer Polytechnic Institute, Hartford Graduate Center.

Probability
Basic Probability
Random Experiments
Sample Spaces
Events
The Concept of Probability
The Axioms of Probability
Some Important Theorems on Probability
Assignment of Probabilities Conditional Probability
Theorems on Conditional Probability
Independent Events Bayes' Theorem or Rule
Combinatorial Analysis
Fundamental Principle of Counting Tree Diagrams
Permutations
Combinations-' Binomial Coefficients
Stirling's Approximation to n!
Random Variables and Probability Distributions
Random Variables
Discrete Probability Distributions
Distribution Functions for Random Variables
Distribution Functions for Discrete Random Variables
Continuous Random Variables
Graphical Interpretations
Joint Distributions
Independent Random Variables Change of Variables
Probability Distributions of Functions of Random Variables
Convolutions
Conditional Distributions
Applications to Geometric Probability
Mathematical Expectation
Definition of Mathematical Expectation
Functions of Random Variables
Some Theorems on Expectation
The Variance and Standard Deviation
Some Theorems on Variance
Standardized Random Variables
Moments
Moment Generating Functions
Some Theorems on Moment Generating Functions
Characteristic Functions
Variance for Joint Distributions. Covariance
Correlation Coefficient
Conditional Expectation, Variance, and Moments Chebyshev's Inequality
Law of Large Numbers
Other Measures of Central Tendency Percentiles
Other Measures of Dispersion
Skewness and Kurtosis
Special Probability Distributions
The Binomial Distribution
Some Properties of the Binomial Distribution
The Law of Large Numbers for Bernoulli Trials
The Normal Distribution
Some Properties of the Normal Distribution
Relation Between Binomial and Normal Distributions
The Poisson Distribution
Some Properties of the Poisson Distribution
Relation Between the Binomial and Poisson Distributions
Relation Between the Poisson and Normal Distributions
The Central Limit Theorem
The Multinomial Distribution
The Hypergeometric Distribution
The Uniform Distribution
The Cauchy Distribution
The Gamma Distribution
The Beta Distribution
The Chi-Square Distribution
Student's t Distribution
The F Distribution
Relationships Among Chi-Square, t, and F Distributions
The Bivariate Normal Distribution Miscellaneous Distributions
Statistics
Sampling Theory
Population and Sample. Statistical Inference
Sampling With and Without Replacement Random Samples. Random Numbers
Population Parameters
Sample Statistics Sampling Distributions
The Sample Mean
Sampling Distribution of Means
Sampling Distribution of Proportions
Sampling Distribution of Differences and Sums
The Sample Variance
Sampling Distribution of Variances
Case Where Population Variance Is Unknown
Sampling Distribution of Ratios of Variances
Other Statistics
Frequency Distributions
Relative Frequency Distributions
Computation of Mean, Variance, and Moments for Grouped Data
Estimation Theory
Unbiased Estimates and Efficient Estimates
Point Estimates and Interval Estimates. Reliability
Confidence Interval Estimates of Population Parameters
Confidence Intervals for Means
Confidence Intervals for Proportions Confidence Intervals for Differences and Sums
Confidence Intervals for the Variance of a Normal Distribution
Confidence Intervals for Variance Ratios
Maximum Likelihood Estimates
Tests of Hypotheses and Significance
Statistical Decisions
Statistical Hypotheses. Null Hypotheses
Tests of Hypotheses and Significance
Type I and Type II Errors
Level of Significance
Tests Involving the Normal Distribution
One-Tailed and Two-Tailed Tests
P Value
Special Tests of Significance for Large Samples
Special Tests of Significance for Small Samples
Relationship Between Estimation Theory and Hypothesis Testing
Operating Characteristic Curves. Power of a Test Quality Control Charts
Fitting Theoretical Distributions to Sample Frequency Distributions The Chi-Square Test for Goodness of Fit
Contingency Tables
Yates' Correction for Continuity
Coefficient of Contingency
Curve Fitting, Regression, and Correlation
Curve Fitting
Regression
The Method of Least Squares
The Least-Squares Line
The Least-Squares Line in Terms of Sample Variances and Covariance
The Least-Squares Parabola
Multiple Regression
Standard Error of Estimate
The Linear Correlation Coefficient
Generalized Correlation Coefficient
Rank Correlation
Probability Interpretation of Regression
Probability Interpretation of Correlation
Sampling Theory of Regression Sampling Theory of Correlation
Correlation and Dependence
Analysis of Variance
The Purpose of Analysis of Variance
One-Way Classification or One-Factor Experiments Total Variation. Variation Within Treatments. Variation Between Treatments Shortcut Methods for Obtaining Variations
Linear Mathematical Model for Analysis of Variance
Expected Values of the Variations
Distributions of the Variations
The F Test for the Null Hypothesis of Equal Means
Analysis of Variance Tables Modifications for Unequal Numbers of Observations Two-Way Classification or Two-Factor Experiments
Notation for Two-Factor Experiments
Variations for Two-Factor Experiments
Analysis of Variance for Two-Factor Experiments
Two-Factor Experiments with Replication
Experimental Design
Nonparametric Tests
Introduction
The Sign Test
The Mann-Whitney U Test
The Kruskal-Wallis H Test
The H Test Corrected for Ties
The Runs Test for Randomness
Further Applications of the Runs Test
Spearman's Rank Correlation
Bayesian Methods
Subjective Probability
Prior and Posterior Distributions
Sampling From a Binomial Population
Sampling From a Poisson Population
Sampling From a Normal Population with Known Variance
Improper Prior Distributions
Conjugate Prior Distributions
Bayesian Point Estimation
Bayesian Interval Estimation
Bayesian Hypothesis Tests
Bayes Factors
Bayesian Predictive Distributions
Mathematical Topics
Special Sums
Euler's Formulas
The Gamma Function
The Beta Function
Special Integrals
Ordinates y of the Standard Normal Curve at z
Areas under the Standard Normal Curve from 0 to z
Percentile Values t<sub>p</sub> for Student's t Distribution with v Degrees of Freedom
Percentile Values x<sub>p</sub></sup>2</sup> for the Chi-Square Distribution with v Degrees of Freedom
95th and 99th Percentile Values for the F Distribution with v<sub>1</sub> v<sub>2</sub> Degrees of Freedom
Values of e<sup>-�</sup>
Random Numbers
Subject Index
Index for Solved Problems
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