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

By:

Ross, Sheldon M

Material type: Text

TextAmsterdam ; Boston : Elsevier Academic Press, c2004Edition: 3rd edDescription: xv, 624 p. : ill. ; 24 cm. + 1 computer discContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

0125980574 (text)
0125980590 (CDROM)

Subject(s):

DDC classification:

519.2 22 R.S.I

LOC classification:

TA340 .R67 2004

Online resources:

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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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Books	Main library A8	Faculty of Engineering & Technology (General)	519.2 R.S.I (Browse shelf(Opens below))	Available		00012423

Includes index.

engineering bookfair2015

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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