Coverart for item
The Resource Introduction to probability and statistics for engineers and scientists, Sheldon M. Ross

Introduction to probability and statistics for engineers and scientists, Sheldon M. Ross

Label
Introduction to probability and statistics for engineers and scientists
Title
Introduction to probability and statistics for engineers and scientists
Statement of responsibility
Sheldon M. Ross
Creator
Subject
Language
eng
Cataloging source
SSW
http://library.link/vocab/creatorName
Ross, Sheldon M
Dewey number
519.20245
Illustrations
illustrations
Index
index present
LC call number
TA340
LC item number
.R67 2004
Literary form
non fiction
http://library.link/vocab/subjectName
  • Probabilities
  • Mathematical statistics
  • Probabilités
  • Statistique mathématique
  • Ingénierie
Label
Introduction to probability and statistics for engineers and scientists, Sheldon M. Ross
Instantiates
Publication
Note
  • Includes index
  • Accompanying CD-ROM contains software for computing exercises in the text
Accompanying material
1 computer optical disc (4 3/4 in.)
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 3
  • 121
  • 4.8
  • Moment Generating Functions
  • 126
  • 4.9
  • Chebyshev's Inequality and the Weak Law of Large Numbers
  • 127
  • Chapter 5
  • Special Random Variables
  • 141
  • 1.5
  • 5.1
  • The Bernoulli and Binomial Random Variables
  • 141
  • 5.1.1
  • Computing the Binomial Distribution Function
  • 147
  • 5.2
  • The Poisson Random Variable
  • 148
  • 5.2.1
  • A Brief History of Statistics
  • Computing the Poisson Distribution Function
  • 155
  • 5.3
  • The Hypergeometric Random Variable
  • 156
  • 5.4
  • The Uniform Random Variable
  • 160
  • 5.5
  • Normal Random Variables
  • 3
  • 168
  • 5.6
  • Exponential Random Variables
  • 175
  • 5.6.1
  • The Poisson Process
  • 179
  • 5.7
  • The Gamma Distribution
  • 182
  • Chapter 2
  • 5.8
  • Distributions Arising from the Normal
  • 185
  • 5.8.1
  • The Chi-Square Distribution
  • 185
  • 5.8.1.1
  • The Relation Between Chi-Square and Gamma Random Variables
  • 187
  • 5.8.2
  • Descriptive Statistics
  • The t-Distribution
  • 189
  • 5.8.3
  • The F-Distribution
  • 191
  • 5.9
  • The Logistics Distribution
  • 192
  • Chapter 6
  • Distributions of Sampling Statistics
  • 9
  • 201
  • 6.2
  • The Sample Mean
  • 202
  • 6.3
  • The Central Limit Theorem
  • 204
  • 6.3.1
  • Approximate Distribution of the Sample Mean
  • 210
  • 2.2
  • 6.3.2
  • How Large a Sample is Needed?
  • 212
  • 6.4
  • The Sample Variance
  • 213
  • 6.5
  • Sampling Distributions from a Normal Population
  • 214
  • 6.5.1
  • Describing Data Sets
  • Distribution of the Sample Mean
  • 215
  • 6.5.2
  • Joint Distribution of X and S[superscript 2]
  • 215
  • 6.6
  • Sampling from a Finite Population
  • 217
  • Chapter 7
  • Parameter Estimation
  • 9
  • 229
  • 7.2
  • Maximum Likelihood Estimators
  • 230
  • 7.2.1
  • Estimating Life Distributions
  • 238
  • 7.3
  • Interval Estimates
  • 240
  • 1.2
  • 2.2.1
  • 7.3.1
  • Confidence Interval for a Normal Mean When the Variance is Unknown
  • 246
  • 7.3.2
  • Confidence Intervals for the Variances of a Normal Distribution
  • 251
  • 7.4
  • Estimating the Difference in Means of Two Normal Populations
  • 253
  • 7.5
  • Frequency Tables and Graphs
  • Approximate Confidence Interval for the Mean of a Bernoulli Random Variable
  • 260
  • 7.6
  • Confidence Interval of the Mean of the Exponential Distribution
  • 265
  • 7.7
  • Evaluating a Point Estimator
  • 266
  • 7.8
  • The Bayes Estimator
  • 10
  • 272
  • Chapter 8
  • Hypothesis Testing
  • 291
  • 8.2
  • Significance Levels
  • 292
  • 8.3
  • Tests Concerning the Mean of a Normal Population
  • 293
  • 2.2.2
  • 8.3.1
  • Case of Known Variance
  • 293
  • 8.3.2
  • Case of Unknown Variance: The t-Test
  • 305
  • 8.4
  • Testing the Equality of Means of Two Normal Populations
  • 312
  • 8.4.1
  • Relative Frequency Tables and Graphs
  • Case of Known Variances
  • 312
  • 8.4.2
  • Case of Unknown Variances
  • 314
  • 8.4.3
  • Case of Unknown and Unequal Variances
  • 318
  • 8.4.4
  • The Paired t-Test
  • 10
  • 319
  • 8.5
  • Hypothesis Tests Concerning the Variance of a Normal Population
  • 321
  • 8.5.1
  • Testing for the Equality of Variances of Two Normal Populations
  • 322
  • 8.6
  • Hypothesis Tests in Bernoulli Populations
  • 323
  • 2.2.3
  • 8.6.1
  • Testing the Equality of Parameters in Two Bernoulli Populations
  • 327
  • 8.7
  • Tests Concerning the Mean of a Poisson Distribution
  • 330
  • 8.7.1
  • Testing the Relationship Between Two Poisson Parameters
  • 331
  • Chapter 9
  • Grouped Data, Histograms, Ogives, and Stem and Leaf Plots
  • Regression
  • 351
  • 9.2
  • Least Squares Estimators of the Regression Parameters
  • 353
  • 9.3
  • Distribution of the Estimators
  • 355
  • 9.4
  • Statistical Inferences about the Regression Parameters
  • 14
  • 361
  • 9.4.1
  • Inferences Concerning [beta]
  • 362
  • 9.4.1.1
  • Regression to the Mean
  • 366
  • 9.4.2
  • Inferences Concerning [alpha]
  • 370
  • 2.3
  • 9.4.3
  • Inferences Concerning the Mean Response [alpha] + [beta]x[subscript 0]
  • 371
  • 9.4.4
  • Prediction Interval of a Future Response
  • 373
  • 9.4.5
  • Summary of Distributional Results
  • 375
  • 9.5
  • Data Collection and Descriptive Statistics
  • Summarizing Data Sets
  • The Coefficient of Determination and the Sample Correlation Coefficient
  • 376
  • 9.6
  • Analysis of Residuals: Assessing the Model
  • 378
  • 9.7
  • Transforming to Linearity
  • 381
  • 9.8
  • Weighted Least Squares
  • 17
  • 384
  • 9.9
  • Polynomial Regression
  • 391
  • 9.10
  • Multiple Linear Regression
  • 394
  • 9.10.1
  • Predicting Future Responses
  • 405
  • 2.3.1
  • 9.11
  • Logistic Regression Models for Binary Output Data
  • 410
  • Chapter 10
  • Analysis of Variance
  • 439
  • 10.2
  • An Overview
  • 440
  • 10.3
  • Sample Mean, Sample Median, and Sample Mode
  • One-Way Analysis of Variance
  • 442
  • 10.3.1
  • Multiple Comparisons of Sample Means
  • 450
  • 10.3.2
  • One-Way Analysis of Variance with Unequal Sample Sizes
  • 452
  • 10.4
  • Two-Factor Analysis of Variance: Introduction and Parameter Estimation
  • 17
  • 454
  • 10.5
  • Two-Factor Analysis of Variance: Testing Hypotheses
  • 458
  • 10.6
  • Two-Way Analysis of Variance with Interaction
  • 463
  • Chapter 11
  • Goodness of Fit Tests and Categorical Data Analysis
  • 483
  • 2.3.2
  • 11.2
  • Goodness of Fit Tests When all Parameters are Specified
  • 484
  • 11.2.1
  • Determining the Critical Region by Simulation
  • 490
  • 11.3
  • Goodness of Fit Tests When Some Parameters are Unspecified
  • 493
  • 11.4
  • Sample Variance and Sample Standard Deviation
  • Tests of Independence in Contingency Tables
  • 495
  • 11.5
  • Tests of Independence in Contingency Tables Having Fixed Marginal Totals
  • 499
  • 11.6
  • The Kolmogorov-Smirnov Goodness of Fit Test for Continuous Data
  • 504
  • Chapter 12
  • Nonparametric Hypothesis Tests
  • 22
  • 515
  • 12.2
  • The Sign Test
  • 515
  • 12.3
  • The Signed Rank Test
  • 519
  • 12.4
  • The Two-Sample Problem
  • 525
  • 2.3.3
  • 12.4.1
  • The Classical Approximation and Simulation
  • 529
  • 12.5
  • The Runs Test for Randomness
  • 533
  • Chapter 13
  • Quality Control
  • 545
  • 13.2
  • Sample Percentiles and Box Plots
  • Control Charts for Average Values: The X-Control Chart
  • 546
  • 13.2.1
  • Case of Unknown [mu] and [sigma]
  • 549
  • 13.3
  • S-Control Charts
  • 554
  • 13.4
  • Control Charts for the Fraction Defective
  • 1
  • 24
  • 557
  • 13.5
  • Control Charts for Number of Defects
  • 559
  • 13.6
  • Other Control Charts for Detecting Changes in the Population Mean
  • 563
  • 13.6.1
  • Moving-Average Control Charts
  • 563
  • 2.4
  • 13.6.2
  • Exponentially Weighted Moving-Average Control Charts
  • 565
  • 13.6.3
  • Cumulative Sum Control Charts
  • 571
  • Chapter 14
  • Life Testing
  • 581
  • 14.2
  • Chebyshev's Inequality
  • Hazard Rate Functions
  • 581
  • 14.3
  • The Exponential Distribution in Life Testing
  • 584
  • 14.3.1
  • Simultaneous Testing -- Stopping at the rth Failure
  • 584
  • 14.3.2
  • Sequential Testing
  • 27
  • 590
  • 14.3.3
  • Simultaneous Testing -- Stopping by a Fixed Time
  • 594
  • 14.3.4
  • The Bayesian Approach
  • 596
  • 14.4
  • A Two-Sample Problem
  • 598
  • 2.5
  • 14.5
  • The Weibull Distribution in Life Testing
  • 600
  • 14.5.1
  • Parameter Estimation by Least Squares
  • 602
  • Normal Data Sets
  • 31
  • 2.6
  • Paired Data Sets and the Sample Correlation Coefficient
  • 33
  • 1.3
  • Chapter 3
  • Elements of Probability
  • 55
  • 3.2
  • Sample Space and Events
  • 56
  • 3.3
  • Venn Diagrams and the Algebra of Events
  • 58
  • 3.4
  • Inferential Statistics and Probability Models
  • Axioms of Probability
  • 59
  • 3.5
  • Sample Spaces Having Equally Likely Outcomes
  • 61
  • 3.6
  • Conditional Probability
  • 67
  • 3.7
  • Bayes' Formula
  • 2
  • 70
  • 3.8
  • Independent Events
  • 76
  • Chapter 4
  • Random Variables and Expectation
  • 89
  • 4.2
  • Types of Random Variables
  • 92
  • 1.4
  • 4.3
  • Jointly Distributed Random Variables
  • 95
  • 4.3.1
  • Independent Random Variables
  • 101
  • 4.3.2
  • Conditional Distributions
  • 105
  • 4.5
  • Populations and Samples
  • Properties of the Expected Value
  • 111
  • 4.5.1
  • Expected Value of Sums of Random Variables
  • 115
  • 4.6
  • Variance
  • 118
  • 4.7
  • Covariance and Variance of Sums of Random Variables
Control code
55993151
Dimensions
24 cm +
Edition
3rd ed.
Extent
xv, 624 pages
Isbn
9780125980579
Isbn Type
(Text)
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Note
MU: CD-ROM in pocket.
Other physical details
illustrations
System control number
(OCoLC)55993151
System details
System requirements for CD-ROM: PC
Label
Introduction to probability and statistics for engineers and scientists, Sheldon M. Ross
Publication
Note
  • Includes index
  • Accompanying CD-ROM contains software for computing exercises in the text
Accompanying material
1 computer optical disc (4 3/4 in.)
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 3
  • 121
  • 4.8
  • Moment Generating Functions
  • 126
  • 4.9
  • Chebyshev's Inequality and the Weak Law of Large Numbers
  • 127
  • Chapter 5
  • Special Random Variables
  • 141
  • 1.5
  • 5.1
  • The Bernoulli and Binomial Random Variables
  • 141
  • 5.1.1
  • Computing the Binomial Distribution Function
  • 147
  • 5.2
  • The Poisson Random Variable
  • 148
  • 5.2.1
  • A Brief History of Statistics
  • Computing the Poisson Distribution Function
  • 155
  • 5.3
  • The Hypergeometric Random Variable
  • 156
  • 5.4
  • The Uniform Random Variable
  • 160
  • 5.5
  • Normal Random Variables
  • 3
  • 168
  • 5.6
  • Exponential Random Variables
  • 175
  • 5.6.1
  • The Poisson Process
  • 179
  • 5.7
  • The Gamma Distribution
  • 182
  • Chapter 2
  • 5.8
  • Distributions Arising from the Normal
  • 185
  • 5.8.1
  • The Chi-Square Distribution
  • 185
  • 5.8.1.1
  • The Relation Between Chi-Square and Gamma Random Variables
  • 187
  • 5.8.2
  • Descriptive Statistics
  • The t-Distribution
  • 189
  • 5.8.3
  • The F-Distribution
  • 191
  • 5.9
  • The Logistics Distribution
  • 192
  • Chapter 6
  • Distributions of Sampling Statistics
  • 9
  • 201
  • 6.2
  • The Sample Mean
  • 202
  • 6.3
  • The Central Limit Theorem
  • 204
  • 6.3.1
  • Approximate Distribution of the Sample Mean
  • 210
  • 2.2
  • 6.3.2
  • How Large a Sample is Needed?
  • 212
  • 6.4
  • The Sample Variance
  • 213
  • 6.5
  • Sampling Distributions from a Normal Population
  • 214
  • 6.5.1
  • Describing Data Sets
  • Distribution of the Sample Mean
  • 215
  • 6.5.2
  • Joint Distribution of X and S[superscript 2]
  • 215
  • 6.6
  • Sampling from a Finite Population
  • 217
  • Chapter 7
  • Parameter Estimation
  • 9
  • 229
  • 7.2
  • Maximum Likelihood Estimators
  • 230
  • 7.2.1
  • Estimating Life Distributions
  • 238
  • 7.3
  • Interval Estimates
  • 240
  • 1.2
  • 2.2.1
  • 7.3.1
  • Confidence Interval for a Normal Mean When the Variance is Unknown
  • 246
  • 7.3.2
  • Confidence Intervals for the Variances of a Normal Distribution
  • 251
  • 7.4
  • Estimating the Difference in Means of Two Normal Populations
  • 253
  • 7.5
  • Frequency Tables and Graphs
  • Approximate Confidence Interval for the Mean of a Bernoulli Random Variable
  • 260
  • 7.6
  • Confidence Interval of the Mean of the Exponential Distribution
  • 265
  • 7.7
  • Evaluating a Point Estimator
  • 266
  • 7.8
  • The Bayes Estimator
  • 10
  • 272
  • Chapter 8
  • Hypothesis Testing
  • 291
  • 8.2
  • Significance Levels
  • 292
  • 8.3
  • Tests Concerning the Mean of a Normal Population
  • 293
  • 2.2.2
  • 8.3.1
  • Case of Known Variance
  • 293
  • 8.3.2
  • Case of Unknown Variance: The t-Test
  • 305
  • 8.4
  • Testing the Equality of Means of Two Normal Populations
  • 312
  • 8.4.1
  • Relative Frequency Tables and Graphs
  • Case of Known Variances
  • 312
  • 8.4.2
  • Case of Unknown Variances
  • 314
  • 8.4.3
  • Case of Unknown and Unequal Variances
  • 318
  • 8.4.4
  • The Paired t-Test
  • 10
  • 319
  • 8.5
  • Hypothesis Tests Concerning the Variance of a Normal Population
  • 321
  • 8.5.1
  • Testing for the Equality of Variances of Two Normal Populations
  • 322
  • 8.6
  • Hypothesis Tests in Bernoulli Populations
  • 323
  • 2.2.3
  • 8.6.1
  • Testing the Equality of Parameters in Two Bernoulli Populations
  • 327
  • 8.7
  • Tests Concerning the Mean of a Poisson Distribution
  • 330
  • 8.7.1
  • Testing the Relationship Between Two Poisson Parameters
  • 331
  • Chapter 9
  • Grouped Data, Histograms, Ogives, and Stem and Leaf Plots
  • Regression
  • 351
  • 9.2
  • Least Squares Estimators of the Regression Parameters
  • 353
  • 9.3
  • Distribution of the Estimators
  • 355
  • 9.4
  • Statistical Inferences about the Regression Parameters
  • 14
  • 361
  • 9.4.1
  • Inferences Concerning [beta]
  • 362
  • 9.4.1.1
  • Regression to the Mean
  • 366
  • 9.4.2
  • Inferences Concerning [alpha]
  • 370
  • 2.3
  • 9.4.3
  • Inferences Concerning the Mean Response [alpha] + [beta]x[subscript 0]
  • 371
  • 9.4.4
  • Prediction Interval of a Future Response
  • 373
  • 9.4.5
  • Summary of Distributional Results
  • 375
  • 9.5
  • Data Collection and Descriptive Statistics
  • Summarizing Data Sets
  • The Coefficient of Determination and the Sample Correlation Coefficient
  • 376
  • 9.6
  • Analysis of Residuals: Assessing the Model
  • 378
  • 9.7
  • Transforming to Linearity
  • 381
  • 9.8
  • Weighted Least Squares
  • 17
  • 384
  • 9.9
  • Polynomial Regression
  • 391
  • 9.10
  • Multiple Linear Regression
  • 394
  • 9.10.1
  • Predicting Future Responses
  • 405
  • 2.3.1
  • 9.11
  • Logistic Regression Models for Binary Output Data
  • 410
  • Chapter 10
  • Analysis of Variance
  • 439
  • 10.2
  • An Overview
  • 440
  • 10.3
  • Sample Mean, Sample Median, and Sample Mode
  • One-Way Analysis of Variance
  • 442
  • 10.3.1
  • Multiple Comparisons of Sample Means
  • 450
  • 10.3.2
  • One-Way Analysis of Variance with Unequal Sample Sizes
  • 452
  • 10.4
  • Two-Factor Analysis of Variance: Introduction and Parameter Estimation
  • 17
  • 454
  • 10.5
  • Two-Factor Analysis of Variance: Testing Hypotheses
  • 458
  • 10.6
  • Two-Way Analysis of Variance with Interaction
  • 463
  • Chapter 11
  • Goodness of Fit Tests and Categorical Data Analysis
  • 483
  • 2.3.2
  • 11.2
  • Goodness of Fit Tests When all Parameters are Specified
  • 484
  • 11.2.1
  • Determining the Critical Region by Simulation
  • 490
  • 11.3
  • Goodness of Fit Tests When Some Parameters are Unspecified
  • 493
  • 11.4
  • Sample Variance and Sample Standard Deviation
  • Tests of Independence in Contingency Tables
  • 495
  • 11.5
  • Tests of Independence in Contingency Tables Having Fixed Marginal Totals
  • 499
  • 11.6
  • The Kolmogorov-Smirnov Goodness of Fit Test for Continuous Data
  • 504
  • Chapter 12
  • Nonparametric Hypothesis Tests
  • 22
  • 515
  • 12.2
  • The Sign Test
  • 515
  • 12.3
  • The Signed Rank Test
  • 519
  • 12.4
  • The Two-Sample Problem
  • 525
  • 2.3.3
  • 12.4.1
  • The Classical Approximation and Simulation
  • 529
  • 12.5
  • The Runs Test for Randomness
  • 533
  • Chapter 13
  • Quality Control
  • 545
  • 13.2
  • Sample Percentiles and Box Plots
  • Control Charts for Average Values: The X-Control Chart
  • 546
  • 13.2.1
  • Case of Unknown [mu] and [sigma]
  • 549
  • 13.3
  • S-Control Charts
  • 554
  • 13.4
  • Control Charts for the Fraction Defective
  • 1
  • 24
  • 557
  • 13.5
  • Control Charts for Number of Defects
  • 559
  • 13.6
  • Other Control Charts for Detecting Changes in the Population Mean
  • 563
  • 13.6.1
  • Moving-Average Control Charts
  • 563
  • 2.4
  • 13.6.2
  • Exponentially Weighted Moving-Average Control Charts
  • 565
  • 13.6.3
  • Cumulative Sum Control Charts
  • 571
  • Chapter 14
  • Life Testing
  • 581
  • 14.2
  • Chebyshev's Inequality
  • Hazard Rate Functions
  • 581
  • 14.3
  • The Exponential Distribution in Life Testing
  • 584
  • 14.3.1
  • Simultaneous Testing -- Stopping at the rth Failure
  • 584
  • 14.3.2
  • Sequential Testing
  • 27
  • 590
  • 14.3.3
  • Simultaneous Testing -- Stopping by a Fixed Time
  • 594
  • 14.3.4
  • The Bayesian Approach
  • 596
  • 14.4
  • A Two-Sample Problem
  • 598
  • 2.5
  • 14.5
  • The Weibull Distribution in Life Testing
  • 600
  • 14.5.1
  • Parameter Estimation by Least Squares
  • 602
  • Normal Data Sets
  • 31
  • 2.6
  • Paired Data Sets and the Sample Correlation Coefficient
  • 33
  • 1.3
  • Chapter 3
  • Elements of Probability
  • 55
  • 3.2
  • Sample Space and Events
  • 56
  • 3.3
  • Venn Diagrams and the Algebra of Events
  • 58
  • 3.4
  • Inferential Statistics and Probability Models
  • Axioms of Probability
  • 59
  • 3.5
  • Sample Spaces Having Equally Likely Outcomes
  • 61
  • 3.6
  • Conditional Probability
  • 67
  • 3.7
  • Bayes' Formula
  • 2
  • 70
  • 3.8
  • Independent Events
  • 76
  • Chapter 4
  • Random Variables and Expectation
  • 89
  • 4.2
  • Types of Random Variables
  • 92
  • 1.4
  • 4.3
  • Jointly Distributed Random Variables
  • 95
  • 4.3.1
  • Independent Random Variables
  • 101
  • 4.3.2
  • Conditional Distributions
  • 105
  • 4.5
  • Populations and Samples
  • Properties of the Expected Value
  • 111
  • 4.5.1
  • Expected Value of Sums of Random Variables
  • 115
  • 4.6
  • Variance
  • 118
  • 4.7
  • Covariance and Variance of Sums of Random Variables
Control code
55993151
Dimensions
24 cm +
Edition
3rd ed.
Extent
xv, 624 pages
Isbn
9780125980579
Isbn Type
(Text)
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Note
MU: CD-ROM in pocket.
Other physical details
illustrations
System control number
(OCoLC)55993151
System details
System requirements for CD-ROM: PC

Library Locations

    • Engineering Library & Technology CommonsBorrow it
      W2001 Lafferre Hall, Columbia, MO, 65211, US
      38.946102 -92.330125
Processing Feedback ...