J. Susan Milton,
Jesse Arnold
McGraw-Hill Education
4e druk, 2002
9780071198592
Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences (Int'l Ed)
Specificaties
Paperback, blz.
|
Engels
McGraw-Hill Education |
4e druk, 2002
ISBN13: 9780071198592
Rubricering
Verwachte levertijd ongeveer 11 werkdagen
Samenvatting
This well-respected text is designed for the first course in probability and statistics taken by students majoring in Engineering and the Computing Sciences. The prerequisite is one year of calculus. The text offers a balanced presentation of applications and theory. The authors take care to develop the theoretical foundations for the statistical methods presented at a level that is accessible to students with only a calculus background. They explore the practical implications of the formal results to problem-solving so students gain an understanding of the logic behind the techniques as well as practice in using them. The examples, exercises, and applications were chosen specifically for students in engineering and computer science and include opportunities for real data analysis.
Specificaties
Inhoudsopgave
<H3>Chapter 1 - Introduction to Probability and Counting<H4>1.1 Interpreting Probabilities<H4>1.2 Sample Spaces and Events<H4>1.3 Permutations and Combinations<H4>Chapter Summary<H4>Exercises<H4>Review Exercises<H3>Chapter 2 - Some Probability Laws<H4>2.1 Axioms of Probability<H4>2.2 Conditional Probability<H4>2.3 Independence and the Multiplication Rule<H4>2.4 Bayes' Theorem<H4>Chapter Summary<H4>Exercises<H4>Review Exercises<H3>Chapter 3 - Discrete Distributions<H4>3.1 Random Variables<H4>3.2 Discrete Probablility Densities<H4>3.3 Expectation and Distribution Parameters<H4>3.4 Geometric Distribution and the Moment Generating Function<H4>3.5 Binomial Distribution<H4>3.6 Negative Binomial Distribution<H4>3.7 Hypergeometric Distribution<H4>3.8 Poisson Distribution<H4>Chapter Summary<H4>Exercises<H4>Review Exercises<H3>Chapter 4 - Continuous Distributions<H4>4.1 Continuous Densities<H4>4.2 Expectation and Distribution Parameters<H4>4.3 Gamma, Exponential, and Chi-Squared Distributions<H4>4.4 Normal Distribution<H4>4.5 Normal Probability Rule and Chebyshev's Inequality<H4>4.6 Normal Approximation to the Binomial Distribution<H4>4.7 Weibull Distribution and Reliability<H4>4.8 Transformation of Variables<H4>4.9 Simulating a Continuous Distribution<H4>Chapter Summary<H4>Exercises<H4>Review Exercises<H3>Chapter 5 - Joint Distributions<H4>5.1 Joint Densities and Independence<H4>5.2 Expectation and Covariance<H4>5.3 Correlation<H4>5.4 Conditional Densities and Regression<H4>5.5 Transformation of Variables<H4>Chapter Summary<H4>Exercises<H4>Review Exercises<H3>Chapter 6 - Descriptive Statistics<H4>6.1 Random Sampling<H4>6.2 Picturing the Distribution<H4>6.3 Sample Statistics<H4>6.4 Boxplots<H4>Chapter Summary<H4>Exercises<H4>Review Exercises<H3>Chapter 7 - Estimation<H4>7.1 Point Estimation<H4>7.2 The Method of Moments and Maximum Likelihood<H4>7.3 Functions of Random Variables--Distribution of X<H4>7.4 Interval Estimation and the Central Limit Theorem<H4>Chapter Summary<H4>Exercises<H4>Review Exercises<H3>Chapter 8 - Inferences on the Mean and Variance of a Distribution<H4>8.1 Interval Estimation of Variability<H4>8.2 Estimating the Mean and the Student-t Distribution<H4>8.3 Hypothesis Testing<H4>8.4 Significance Testing<H4>8.5 Hypothesis and Significance Tests on the Mean<H4>8.6 Hypothesis Test on the Variance<H4>8.7 Alternative Nonparametric Methods<H4>Chapter Summary<H4>Exercises<H4>Review Exercises<H3>Chapter 9 - Inferences on Proportions<H4>9.1 Estimating Proportions<H4>9.2 Testing Hypothesis on a Proportion<H4>9.3 Comparing Two Proportions Estimation<H4>9.4 Coparing Two Proportions: Hypothesis Testing<H4>Chapter Summary<H4>Exercises<H4>Review Exercises<H3>Chapter 10 - Comparing Two Means and Two Variances<H4>10.1 Point Estimation: Independent Samples<H4>10.2 Comparing Variances: The F Distribution<H4>10.3 Comparing Means: Variances Equal (Pooled Test)<H4>10.4 Comparing Means: Variances Unequal<H4>10.5 Compairing Means: Paried Data<H4>10.6 Alternative Nonparametric Methods<H4>10.7 A Note on Technology<H4>Chapter Summary<H4>Exercises<H4>Review Exercises<H3>Chapter 11 - Sample Linear Regression and Correlation<H4>11.1 Model and Parameter Estimation<H4>11.2 Properties of Least-Squares Estimators<H4>11.3 Confidence Interval Estimation and Hypothesis Testing<H4>11.4 Repeated Measurements and Lack of Fit<H4>11.5 Residual Analysis<H4>11.6 Correlation<H4>Chapter Summary<H4>Exercises<H4>Review Exercises<H3>Chapter 12 - Multiple Linear Regression Models<H4>12.1 Least-Squares Procedures for Model Fitting<H4>12.2 A Matrix Approach to Least Squares<H4>12.3 Properties of the Least-Squares Estimators<H4>12.4 Interval Estimation<H4>12.5 Testing Hypothesis about Model Parameters<H4>12.6 Use of Indicator or