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Probability and Statistics for Engineering and the Sciences, 10th Edition
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Overview
Prepare your students with the data analysis skills they need in their future careers with the market-leading “Probability and Statistics for Engineering and the Sciences,” 10th Edition, with WebAssign, offering a comprehensive introduction to probability models and statistical methods common in engineering and scientific disciplines. New to this edition, authors Leif Ellingson and Anna Panorska -- who both have years of experience teaching statistics at all levels -- emphasize fundamental concepts, models and methodologies, while also providing the underlying rationale to motivate learning. Revisions to the 10th edition help students recall foundational concepts as they read the text and work through exercises. With updated content, authentic problem scenarios in examples and exercises incorporate real-world data to actively engage students and demonstrate practical relevance.
- Revisions to the text remind students of foundational concepts at point of need, within readings and in exercises.
- The text now includes R code, connecting students to the real-world practice of statistics, while still offering the flexibility to use any statistical software program.
- Updates to data in examples and exercises throughout the text enhance student engagement.
- WebAssign reinforces learning with a range of automatically graded exercise types, study resources, an interactive eBook and the Statistical Analysis and Learning Tool (SALT).
- Resampling methods, including parametric and non-parametric bootstrap for estimation of the error of estimators, prepare students with current statistical methodology.
- Revisions to Chapter 5 help students connect material from Chapters 1–4 on descriptive statistics and probability distributions to the rest of their studies in statistical methods.
- The text emphasizes variation, particularly in a detailed description of pooled t procedures for analysis and the nature of variation in the slope estimate in simple linear regression.
- Simulation Experiments help students understand sampling distributions and insights to gain from them, particularly when derivations are too complex to carry out.
1. OVERVIEW AND DESCRIPTIVE STATISTICS.
Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability.
2. PROBABILITY.
Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability.
Counting Techniques. Conditional Probability. Independence.
3. DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS.
Random Variables. Probability Distributions for Discrete Random Variables.
Expected Values. The Binomial Probability Distribution. Hypergeometric and Negative Binomial Distributions. The Poisson Probability Distribution.
4. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS.
Probability Density Functions. Cumulative Distribution Functions and Expected Values. The Normal Distribution. The Exponential and Gamma Distributions. Other Continuous Distributions. Probability Plots.
5. JOINT PROBABILITY DISTRIBUTIONS AND RANDOM SAMPLES.
Jointly Distributed Random Variables. Expected Values, Covariance, and Correlation.
Statistics and Their Distributions. The Distribution of the Sample Mean. The Distribution of a Linear Combination.
6. POINT ESTIMATION.
Some General Concepts of Point Estimation. Methods of Point Estimation.
7. STATISTICAL INTERVALS BASED ON A SINGLE SAMPLE.
Basic Properties of Confidence Intervals. Large-Sample Confidence Intervals for a Population Mean and Proportion. Intervals Based on a Normal Population Distribution.
Confidence Intervals for the Variance and Standard Deviation of a Normal Population.
8. TESTS OF HYPOTHESIS BASED ON A SINGLE SAMPLE.
Hypotheses and Test Procedures. z Tests for Hypotheses About a Population Mean.
The One-Sample t Test. Tests Concerning a Population Proportion. Further Aspects of Hypothesis Testing.
9. INFERENCES BASED ON TWO SAMPLES.
z Tests and Confidence Intervals for a Difference between Two Population Means.
The Two-Sample t Test and Confidence Interval. Analysis of Paired Data. Inferences Concerning a Difference between Population Proportions. Inferences Concerning Two Population Variances.
10. THE ANALYSIS OF VARIANCE.
Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA.
11. MULTIFACTOR ANALYSIS OF VARIANCE.
Two-Factor ANOVA with Kij = 1. Two-Factor ANOVA with Kij > 1. Three-Factor ANOVA. 2p Factorial Experiments.
12. SIMPLE LINEAR REGRESSION AND CORRELATION.
The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter β1. Inferences Concerning µY•x* and the Prediction of Future Y Values. Correlation.
13. NONLINEAR AND MULTIPLE REGRESSION.
Assessing Model Adequacy. Regression with Transformed Variables. Polynomial Regression. Multiple Regression Analysis. Other Issues in Multiple Regression.
14. GOODNESS-OF-FIT TESTS AND CATEGORICAL DATA ANALYSIS.
Goodness-of-Fit Tests When Category Probabilities Are Completely Specified. Goodness-of-Fit Tests for Composite Hypotheses. Two-Way Contingency Tables.
15. DISTRIBUTION-FREE PROCEDURES.
The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA.
16. QUALITY CONTROL METHODS.
General Comments on Control Charts. Control Charts for Process Location. Control Charts for Process Variation. Control Charts for Attributes. CUSUM Procedures.
Acceptance Sampling.
Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability.
2. PROBABILITY.
Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability.
Counting Techniques. Conditional Probability. Independence.
3. DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS.
Random Variables. Probability Distributions for Discrete Random Variables.
Expected Values. The Binomial Probability Distribution. Hypergeometric and Negative Binomial Distributions. The Poisson Probability Distribution.
4. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS.
Probability Density Functions. Cumulative Distribution Functions and Expected Values. The Normal Distribution. The Exponential and Gamma Distributions. Other Continuous Distributions. Probability Plots.
5. JOINT PROBABILITY DISTRIBUTIONS AND RANDOM SAMPLES.
Jointly Distributed Random Variables. Expected Values, Covariance, and Correlation.
Statistics and Their Distributions. The Distribution of the Sample Mean. The Distribution of a Linear Combination.
6. POINT ESTIMATION.
Some General Concepts of Point Estimation. Methods of Point Estimation.
7. STATISTICAL INTERVALS BASED ON A SINGLE SAMPLE.
Basic Properties of Confidence Intervals. Large-Sample Confidence Intervals for a Population Mean and Proportion. Intervals Based on a Normal Population Distribution.
Confidence Intervals for the Variance and Standard Deviation of a Normal Population.
8. TESTS OF HYPOTHESIS BASED ON A SINGLE SAMPLE.
Hypotheses and Test Procedures. z Tests for Hypotheses About a Population Mean.
The One-Sample t Test. Tests Concerning a Population Proportion. Further Aspects of Hypothesis Testing.
9. INFERENCES BASED ON TWO SAMPLES.
z Tests and Confidence Intervals for a Difference between Two Population Means.
The Two-Sample t Test and Confidence Interval. Analysis of Paired Data. Inferences Concerning a Difference between Population Proportions. Inferences Concerning Two Population Variances.
10. THE ANALYSIS OF VARIANCE.
Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA.
11. MULTIFACTOR ANALYSIS OF VARIANCE.
Two-Factor ANOVA with Kij = 1. Two-Factor ANOVA with Kij > 1. Three-Factor ANOVA. 2p Factorial Experiments.
12. SIMPLE LINEAR REGRESSION AND CORRELATION.
The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter β1. Inferences Concerning µY•x* and the Prediction of Future Y Values. Correlation.
13. NONLINEAR AND MULTIPLE REGRESSION.
Assessing Model Adequacy. Regression with Transformed Variables. Polynomial Regression. Multiple Regression Analysis. Other Issues in Multiple Regression.
14. GOODNESS-OF-FIT TESTS AND CATEGORICAL DATA ANALYSIS.
Goodness-of-Fit Tests When Category Probabilities Are Completely Specified. Goodness-of-Fit Tests for Composite Hypotheses. Two-Way Contingency Tables.
15. DISTRIBUTION-FREE PROCEDURES.
The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA.
16. QUALITY CONTROL METHODS.
General Comments on Control Charts. Control Charts for Process Location. Control Charts for Process Variation. Control Charts for Attributes. CUSUM Procedures.
Acceptance Sampling.
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