Introduction to Probability and Statistics for Engineers and Scientists

ISBN-10: 0125980574

ISBN-13: 9780125980579

Edition: 3rd 2004 (Revised)

Authors: Sheldon M. Ross

List price: $99.95
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This text presents an introduction to probability and statistics for students in engineering and the sciences who have a knowledge of elementary calculus.
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Book details

List price: $99.95
Edition: 3rd
Copyright year: 2004
Publisher: Elsevier Science & Technology Books
Publication date: 7/6/2004
Binding: Hardcover
Pages: 640
Size: 7.50" wide x 9.25" long x 1.00" tall
Weight: 2.684
Language: English

Introduction to Statistics
Data Collection and Descriptive Statistics
Inferential Statistics and Probability Models
Populations and Samples
A Brief History of Statistics
Descriptive Statistics
Describing Data Sets
Frequency Tables and Graphs
Relative Frequency Tables and Graphs
Grouped Data, Histograms, Ogives, and Stem and Leaf Plots
Summarizing Data Sets
Sample Mean, Sample Median, and Sample Mode
Sample Variance and Sample Standard Deviation
Sample Percentiles and Box Plots
Chebyshev's Inequality
Normal Data Sets
Paired Data Sets and the Sample Correlation Coefficient
Elements of Probability
Sample Space and Events
Venn Diagrams and the Algebra of Events
Axioms of Probability
Sample Spaces Having Equally Likely Outcomes
Conditional Probability
Bayes' Formula
Independent Events
Random Variables and Expectation
Random Variables
Types of Random Variables
Jointly Distributed Random Variables
Independent Random Variables
Conditional Distributions
Properties of the Expected Value
Expected Value of Sums of Random Variables
Covariance and Variance of Sums of Random Variables
Moment Generating Functions
Chebyshev's Inequality and the Weak Law of Large Numbers
Special Random Variables
The Bernoulli and Binomial Random Variables
Computing the Binomial Distribution Function
The Poisson Random Variable
Computing the Poisson Distribution Function
The Hypergeometric Random Variable
The Uniform Random Variable
Normal Random Variables
Exponential Random Variables
The Poisson Process
The Gamma Distribution
Distributions Arising from the Normal
The Chi-Square Distribution
The Relation Between Chi-Square and Gamma Random Variables
The t-Distribution
The F-Distribution
The Logistics Distribution
Distributions of Sampling Statistics
The Sample Mean
The Central Limit Theorem
Approximate Distribution of the Sample Mean
How Large a Sample is Needed?
The Sample Variance
Sampling Distributions from a Normal Population
Distribution of the Sample Mean
Joint Distribution of X and S[superscript 2]
Sampling from a Finite Population
Parameter Estimation
Maximum Likelihood Estimators
Estimating Life Distributions
Interval Estimates
Confidence Interval for a Normal Mean When the Variance is Unknown
Confidence Intervals for the Variances of a Normal Distribution
Estimating the Difference in Means of Two Normal Populations
Approximate Confidence Interval for the Mean of a Bernoulli Random Variable
Confidence Interval of the Mean of the Exponential Distribution
Evaluating a Point Estimator
The Bayes Estimator
Hypothesis Testing
Significance Levels
Tests Concerning the Mean of a Normal Population
Case of Known Variance
Case of Unknown Variance: The t-Test
Testing the Equality of Means of Two Normal Populations
Case of Known Variances
Case of Unknown Variances
Case of Unknown and Unequal Variances
The Paired t-Test
Hypothesis Tests Concerning the Variance of a Normal Population
Testing for the Equality of Variances of Two Normal Populations
Hypothesis Tests in Bernoulli Populations
Testing the Equality of Parameters in Two Bernoulli Populations
Tests Concerning the Mean of a Poisson Distribution
Testing the Relationship Between Two Poisson Parameters
Least Squares Estimators of the Regression Parameters
Distribution of the Estimators
Statistical Inferences about the Regression Parameters
Inferences Concerning [beta]
Regression to the Mean
Inferences Concerning [alpha]
Inferences Concerning the Mean Response [alpha] + [beta]x[subscript 0]
Prediction Interval of a Future Response
Summary of Distributional Results
The Coefficient of Determination and the Sample Correlation Coefficient
Analysis of Residuals: Assessing the Model
Transforming to Linearity
Weighted Least Squares
Polynomial Regression
Multiple Linear Regression
Predicting Future Responses
Logistic Regression Models for Binary Output Data
Analysis of Variance
An Overview
One-Way Analysis of Variance
Multiple Comparisons of Sample Means
One-Way Analysis of Variance with Unequal Sample Sizes
Two-Factor Analysis of Variance: Introduction and Parameter Estimation
Two-Factor Analysis of Variance: Testing Hypotheses
Two-Way Analysis of Variance with Interaction
Goodness of Fit Tests and Categorical Data Analysis
Goodness of Fit Tests When all Parameters are Specified
Determining the Critical Region by Simulation
Goodness of Fit Tests When Some Parameters are Unspecified
Tests of Independence in Contingency Tables
Tests of Independence in Contingency Tables Having Fixed Marginal Totals
The Kolmogorov-Smirnov Goodness of Fit Test for Continuous Data
Nonparametric Hypothesis Tests
The Sign Test
The Signed Rank Test
The Two-Sample Problem
The Classical Approximation and Simulation
The Runs Test for Randomness
Quality Control
Control Charts for Average Values: The X-Control Chart
Case of Unknown [mu] and [sigma]
S-Control Charts
Control Charts for the Fraction Defective
Control Charts for Number of Defects
Other Control Charts for Detecting Changes in the Population Mean
Moving-Average Control Charts
Exponentially Weighted Moving-Average Control Charts
Cumulative Sum Control Charts
Life Testing
Hazard Rate Functions
The Exponential Distribution in Life Testing
Simultaneous Testing--Stopping at the rth Failure
Sequential Testing
Simultaneous Testing--Stopping by a Fixed Time
The Bayesian Approach
A Two-Sample Problem
The Weibull Distribution in Life Testing
Parameter Estimation by Least Squares
Appendix of Tables
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