Probability and Statistics for Engineers

ISBN-10: 0534403026

ISBN-13: 9780534403027

Edition: 5th 2011

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PROBABILITY AND STATISTICS FOR ENGINEERS provides a one-semester, calculus-based introduction to engineering statistics that focuses on making intelligent sense of real engineering data and interpreting results. Traditional topics are presented thorough an accessible modern framework that emphasizes the statistical thinking, data collection and analysis, decision-making, and process improvement skills that engineers need on a daily basis to solve real problems. The book continues to be driven by its hallmark array of engineering applications--thoroughly expanded and modernized for the 5th edition--which tackle timely, interesting, and illuminating scenarios that show students the rich context behind the concepts. Within the presentation of topics and applications the authors continually develop users' intuition for collecting their own real data, analyzing it with the latest graphical tools, and interpreting the results with a goal of improving quality control and problem-solving process. Users will not only gain solid understanding of concepts and their real-life practicality, but will learn to become active statistical practitioners for their own future careers.
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Book details

Edition: 5th
Copyright year: 2011
Publisher: Brooks/Cole
Publication date: 6/22/2010
Binding: Hardcover
Pages: 848
Size: 8.25" wide x 10.00" long x 1.50" tall
Weight: 3.938
Language: English

James T. McClave, Info Tech, Inc./ University of Florida P. Goerge Benson, Terry College of Business, University of Georgia Terry Sincich, University of South Florida

Richard L. Scheaffer, Professor Emeritus of Statistics, University of Florida, received his Ph.D. in statistics from Florida State University. Accompanying a career of teaching, research and administration, Dr. Scheaffer has led efforts on the improvement of statistics education throughout the school and college curriculum. Co-author of five textbooks, he was one of the developers of the Quantitative Literacy Project that formed the basis of the data analysis strand in the curriculum standards of the National Council of Teachers of Mathematics. He also led the task force that developed the AP Statistics Program, for which he served as Chief Faculty Consultant. Dr. Scheaffer is a Fellow and past president of the American Statistical Association, a past chair of the Conference Board of the Mathematical Sciences, and an advisor on numerous statistics education projects.

Data Collection and Exploring Univariate Distributions
A model for problem solving and its application
Types of data and frequency distribution tables
Tools for describing data: Graphical methods
Graphing Categorical Data
Graphing Numerical Data
Visualizing distributions
Tool for Describing Data: Numerical measures
Measures of Center
Measures of Position
Measures of variation (or spread)
Reading Computer Printouts
The effect of shifting and scaling of measurements on summary measures
Summary Measures and Decisions
The Empirical Rule
Standardized Values and z-scores
Detecting Outliers
Supplemental Exercises
Exploring Bivariate Distributions and Estimating Relations
Two-way table for categorical data
Time series analysis
Scatterplots: Graphical analysis of association between measurements
Correlation: Estimating the strength of linear relation
Regression: Modeling linear relationships
The Coefficient of Determination
Residual Analysis: Assessing the adequacy of the model
Reading Computer Printout
Supplemental Exercises
Obtaining Data
Overview of methods of data collection
Planning and Conducting Surveys
Planning and Conducting Experiments
Completely Randomized Design
Randomized Block Design
Planning and Conducting an Observational Study
Supplemental Exercises
Sample space and relationships among events
Definition of probability
Counting rules useful in probability
Conditional probability and independence
Rules of probability
Odds, odds ratios, and risk ratio
Supplemental Exercises
Discrete Probability Distributions
Random variables and their probability distributions
Expected values of random variables
The Bernoulli distribution
The Binomial distribution
The Geometric and Negative Binomial distributions
The Geometric distribution
The Negative Binomial distribution
The Poisson distribution
The hypergeometric distribution
The Moment-Generating Function
Simulating probability distributions
Supplementary Exercises
Continuous Probability Distributions
Continuous random variables and their probability distributions
Expected values of continuous random variables
The Uniform distribution
The exponential distribution
The Gamma distribution
The Normal distribution
The Lognormal Distribution
The Beta distribution
The Weibull distribution
The Moment-generating Functions for Continuous Random Variables
Simulating probability distributions
Supplementary Exercises
Multivariate Probability Distributions
Bivariate and Marginal Probability Distributions
Conditional Probability Distributions
Independent Random Variables
Expected Values of Functions of Random Variables
The Multinomial Distribution
More on the Moment-Generating Function
Conditional Expectations
Compounding and Its Applications
Supplementary Exercises
Statistics, Sampling Distributions, and Control Charts
The sampling distributions
The sampling distribution of X (General Distribution)
The sampling distribution of X (Normal Distribution)
The sampling distribution of sample proportion Y/n (Large sample)
The sampling distribution of S? (Normal Distribution)
Sampling Distributions: the multiple-sample case
The sampling distribution of (X1 - X2)
The sampling distribution of XD
The sampling distribution of (^p1 - ^p2)
The sampling distribution of S?1/S?2
Control Charts
The X-Chart: Known ? and s
The X and R-Charts: Unknown ? and s
The X and S-Charts: Unknown ? and s
The p-Chart
The c-chart
The u-chart
Process Capability
Supplementary Exercises
Point estimators and their properties
Confidence Intervals: the Single-Sample Case
Confidence Interval for ?: General Distribution
Confidence Interval for Mean: Normal Distribution
Confidence Interval for Proportion: Large sample case
Confidence interval for s?
Confidence Intervals: the Multiple Samples Case
Confidence Interval for Linear Functions of Means: General Distributions
Confidence Interval for Linear Functions of Means: Normal Distributions
Large Samples Confidence Intervals for Linear Functions of Proportions
Confidence Interval for s?2/s?1: Normal distribution case
Prediction Intervals
Tolerance Intervals
The Method of Maximum Likelihood
Bayes Estimators
Supplementary Exercises
Hypothesis Testing
Terminology of Hypothesis Testing
Hypothesis Testing: the Single-Sample Case
Testing for Mean: General Distributions Case
Testing a Mean: Normal distribution Case
Testing for Proportion: Large Sample Case
Testing for Variance: Normal Distribution Case
Hypothesis Testing: the Multiple-Sample Case
Testing the Difference between Two means: General Distributions Case
Testing the Difference between Two means: Normal Distributions case
Testing the difference between the means for paired samples
Testing the ratio of variances: Normal distributions case. ?? tests on Frequency data
Testing parameters of the multinomial distribution
Testing equality among Binomial parameters
Test of Independence
Goodness of Fit Tests. ?? Test Kolmogorov-Smirnov test
Using Computer Programs to Fit Distributions
Acceptance Sampling
Acceptance Sampling by Attributes
Acceptance Sampling by Variables
Supplementary Exercises
Estimation and Inference for Regression Parameters
Regression models with one predictor variable
The probability distribution of random error component
Making inferences about slope
Estimating slope using a confidence interval
Testing a hypothesis about slope
Connection between inference for slope and correlation coefficient
Using the simple linear model for estimation and prediction
Multiple regression analysis
Fitting the model: the least-squares approach
Estimation of error variance
Inferences in multiple regression
A test of model adequacy
Estimating and testing hypothesis about individual
Parameters Using the multiple regression model for estimation and prediction
Model building: a test for portion of a model
Other regression models
Response surface method
Modeling a time trend
Logistic regression
Checking conditions and some pitfalls
Checking conditions
Some pitfalls
Reading printouts
Supplemental Exercises
Analysis of Variance
Review of Designed Experiments
Analysis of Variance (ANOVA) Technique
Analysis of Variance for Completely Randomized Design
Relationship of ANOVA for CRD with a t test and Regression
Equivalence between a t test and an F test for CRD with 2 treatments
ANOVA for CRD and Regression Analysis
Estimation for Completely randomized design
Analysis of Variance for the Randomized Block Design
Relation between a Paired t test and an F test for RBD
ANOVA for RBD and Regression Analysis
Bonferroni Method for Estimation for RBD
Factorial Experiments
Analysis of variance for the Factorial Experiment
Fitting Higher Order Models
Supplemental Exercises
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