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

Material type: TextPublication details: ©2004. Edition: 3rd edDescription: 1 online resource (xv, 624 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9780080470313; 0080470319; 6610961476; 9786610961474Genre/Form: Electronic books. | Electronic books. Additional physical formats: Print version:: Introduction to probability and statistics for engineers and scientists.DDC classification: 519.2 LOC classification: TA340 | .R67 2004ebOnline resources: Click here to access online
Contents:
Cover -- Contents -- Preface -- CHAPTER 1 INTRODUCTION TO STATISTICS -- 1.1 INTRODUCTION -- 1.2 DATA COLLECTION AND DESCRIPTIVE STATISTICS -- 1.3 INFERENTIAL STATISTICS AND PROBABILITY MODELS -- 1.4 POPULATIONS AND SAMPLES -- 1.5 A BRIEF HISTORY OF STATISTICS -- CHAPTER 2 DESCRIPTIVE STATISTICS -- 2.1 INTRODUCTION -- 2.2 DESCRIBING DATA SETS -- 2.2.1 Frequency Tables and Graphs -- 2.2.2 Relative Frequency Tables and Graphs -- 2.2.3 Grouped Data, Histograms, Ogives, and Stem and Leaf Plots -- 2.3 SUMMARIZING DATA SETS -- 2.3.1 Sample Mean, Sample Median, and Sample Mode -- 2.3.2 Sample Variance and Sample Standard Deviation -- 2.3.3 Sample Percentiles and Box Plots -- 2.4 CHEBYSHEV'S INEQUALITY -- 2.5 NORMAL DATA SETS -- 2.6 PAIRED DATA SETS AND THE SAMPLE CORRELATION COEFFICIENT -- CHAPTER 3 ELEMENTS OF PROBABILITY -- 3.1 INTRODUCTION -- 3.2 SAMPLE SPACE AND EVENTS -- 3.3 VENN DIAGRAMS AND THE ALGEBRA OF EVENTS -- 3.4 AXIOMS OF PROBABILITY -- 3.5 SAMPLE SPACES HAVING EQUALLY LIKELY OUTCOMES -- 3.6 CONDITIONAL PROBABILITY -- 3.7 BAYES' FORMULA -- 3.8 INDEPENDENT EVENTS -- CHAPTER 4 RANDOM VARIABLES AND EXPECTATION -- 4.1 RANDOM VARIABLES -- 4.2 TYPES OF RANDOM VARIABLES -- 4.3 JOINTLY DISTRIBUTED RANDOM VARIABLES -- 4.3.1 Independent Random Variables -- *4.3.2 Conditional Distributions -- 4.4 EXPECTATION -- 4.5 PROPERTIES OF THE EXPECTED VALUE -- 4.5.1 Expected Value of Sums of Random Variables -- 4.6 VARIANCE -- 4.7 COVARIANCE AND VARIANCE OF SUMS OF RANDOM VARIABLES -- 4.8 MOMENT GENERATING FUNCTIONS -- 4.9 CHEBYSHEV'S INEQUALITY AND THE WEAK LAW OF LARGE NUMBERS -- CHAPTER 5 SPECIAL RANDOM VARIABLES -- 5.1 THE BERNOULLI AND BINOMIAL RANDOM VARIABLES -- 5.1.1 Computing the Binomial Distribution Function -- 5.2 THE POISSON RANDOM VARIABLE -- 5.2.1 Computing the Poisson Distribution Function -- 5.3 THE HYPERGEOMETRIC RANDOM VARIABLE -- 5.4 THE UNIFORM RANDOM VARIABLE -- 5.5 NORMAL RANDOM VARIABLES -- 5.6 EXPONENTIAL RANDOM VARIABLES -- *5.6.1 The Poisson Process -- *5.7 THE GAMMA DISTRIBUTION -- 5.8 DISTRIBUTIONS ARISING FROM THE NORMAL -- 5.8.1 The Chi-Square Distribution -- 5.8.2 The t-Distribution -- 5.8.3 The F-Distribution -- *5.9 THE LOGISTICS DISTRIBUTION -- CHAPTER 6 DISTRIBUTIONS OF SAMPLING STATISTICS -- 6.1 INTRODUCTION -- 6.2 THE SAMPLE MEAN -- 6.3 THE CENTRAL LIMIT THEOREM -- 6.3.1 Approximate Distribution of the Sample Mean -- 6.3.2 How Large a Sample Is Needed? -- 6.4 THE SAMPLE VARIANCE -- 6.5 SAMPLING DISTRIBUTIONS FROM A NORMAL POPULATION -- 6.5.1 Distribution of the Sample Mean -- 6.5.2 Joint Distribution of X and S2 -- 6.6 SAMPLING FROM A FINITE POPULATION -- CHAPTER 7 PARAMETER ESTIMATION -- 7.1 INTRODUCTION -- 7.2 MAXIMUM LIKELIHOOD ESTIMATORS -- *7.2.1 Estimating Life Distributions -- 7.3 INTERVAL ESTIMATES -- 7.3.1 Confidence Interval for a Normal Mean When the Variance is Unknown -- 7.3.2 Confidence Intervals for the Variance of a Normal Distribution -- 7.4 ESTIMATING THE.
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Includes index.

Cover -- Contents -- Preface -- CHAPTER 1 INTRODUCTION TO STATISTICS -- 1.1 INTRODUCTION -- 1.2 DATA COLLECTION AND DESCRIPTIVE STATISTICS -- 1.3 INFERENTIAL STATISTICS AND PROBABILITY MODELS -- 1.4 POPULATIONS AND SAMPLES -- 1.5 A BRIEF HISTORY OF STATISTICS -- CHAPTER 2 DESCRIPTIVE STATISTICS -- 2.1 INTRODUCTION -- 2.2 DESCRIBING DATA SETS -- 2.2.1 Frequency Tables and Graphs -- 2.2.2 Relative Frequency Tables and Graphs -- 2.2.3 Grouped Data, Histograms, Ogives, and Stem and Leaf Plots -- 2.3 SUMMARIZING DATA SETS -- 2.3.1 Sample Mean, Sample Median, and Sample Mode -- 2.3.2 Sample Variance and Sample Standard Deviation -- 2.3.3 Sample Percentiles and Box Plots -- 2.4 CHEBYSHEV'S INEQUALITY -- 2.5 NORMAL DATA SETS -- 2.6 PAIRED DATA SETS AND THE SAMPLE CORRELATION COEFFICIENT -- CHAPTER 3 ELEMENTS OF PROBABILITY -- 3.1 INTRODUCTION -- 3.2 SAMPLE SPACE AND EVENTS -- 3.3 VENN DIAGRAMS AND THE ALGEBRA OF EVENTS -- 3.4 AXIOMS OF PROBABILITY -- 3.5 SAMPLE SPACES HAVING EQUALLY LIKELY OUTCOMES -- 3.6 CONDITIONAL PROBABILITY -- 3.7 BAYES' FORMULA -- 3.8 INDEPENDENT EVENTS -- CHAPTER 4 RANDOM VARIABLES AND EXPECTATION -- 4.1 RANDOM VARIABLES -- 4.2 TYPES OF RANDOM VARIABLES -- 4.3 JOINTLY DISTRIBUTED RANDOM VARIABLES -- 4.3.1 Independent Random Variables -- *4.3.2 Conditional Distributions -- 4.4 EXPECTATION -- 4.5 PROPERTIES OF THE EXPECTED VALUE -- 4.5.1 Expected Value of Sums of Random Variables -- 4.6 VARIANCE -- 4.7 COVARIANCE AND VARIANCE OF SUMS OF RANDOM VARIABLES -- 4.8 MOMENT GENERATING FUNCTIONS -- 4.9 CHEBYSHEV'S INEQUALITY AND THE WEAK LAW OF LARGE NUMBERS -- CHAPTER 5 SPECIAL RANDOM VARIABLES -- 5.1 THE BERNOULLI AND BINOMIAL RANDOM VARIABLES -- 5.1.1 Computing the Binomial Distribution Function -- 5.2 THE POISSON RANDOM VARIABLE -- 5.2.1 Computing the Poisson Distribution Function -- 5.3 THE HYPERGEOMETRIC RANDOM VARIABLE -- 5.4 THE UNIFORM RANDOM VARIABLE -- 5.5 NORMAL RANDOM VARIABLES -- 5.6 EXPONENTIAL RANDOM VARIABLES -- *5.6.1 The Poisson Process -- *5.7 THE GAMMA DISTRIBUTION -- 5.8 DISTRIBUTIONS ARISING FROM THE NORMAL -- 5.8.1 The Chi-Square Distribution -- 5.8.2 The t-Distribution -- 5.8.3 The F-Distribution -- *5.9 THE LOGISTICS DISTRIBUTION -- CHAPTER 6 DISTRIBUTIONS OF SAMPLING STATISTICS -- 6.1 INTRODUCTION -- 6.2 THE SAMPLE MEAN -- 6.3 THE CENTRAL LIMIT THEOREM -- 6.3.1 Approximate Distribution of the Sample Mean -- 6.3.2 How Large a Sample Is Needed? -- 6.4 THE SAMPLE VARIANCE -- 6.5 SAMPLING DISTRIBUTIONS FROM A NORMAL POPULATION -- 6.5.1 Distribution of the Sample Mean -- 6.5.2 Joint Distribution of X and S2 -- 6.6 SAMPLING FROM A FINITE POPULATION -- CHAPTER 7 PARAMETER ESTIMATION -- 7.1 INTRODUCTION -- 7.2 MAXIMUM LIKELIHOOD ESTIMATORS -- *7.2.1 Estimating Life Distributions -- 7.3 INTERVAL ESTIMATES -- 7.3.1 Confidence Interval for a Normal Mean When the Variance is Unknown -- 7.3.2 Confidence Intervals for the Variance of a Normal Distribution -- 7.4 ESTIMATING THE.

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