Probability and Statistics for Engineers

Name: Probability and Statistics for Engineers
Price: 39.49 USD
Availability: InStock
ISBN: 9780534403027

ISBN-10: 0534403026

ISBN-13: 9780534403027

Edition: 5th 2011

Authors: James T. McClave, Madhuri Mulekar, Richard L. Scheaffer

This item qualifies for FREE shipping.

30 day, 100% satisfaction guarantee!

International, Softcover : $41.42

Marketplace

1 new & used from $39.49

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

Description:

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…

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: 3.432
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…




Data Collection and Exploring Univariate Distributions


Introduction


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


Boxplots


Detecting Outliers


Summary


Supplemental Exercises



Exploring Bivariate Distributions and Estimating Relations


Introduction


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


Transformations


Reading Computer Printout


Summary


Supplemental Exercises



Obtaining Data


Introduction


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


Summary


Supplemental Exercises



Probability


Introduction


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


Summary


Supplemental Exercises



Discrete Probability Distributions


Introduction


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


Summary


Supplementary Exercises



Continuous Probability Distributions


Introduction


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


Reliability


The Moment-generating Functions for Continuous Random Variables


Simulating probability distributions


Summary


Supplementary Exercises



Multivariate Probability Distributions


Introduction


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


Summary


Supplementary Exercises



Statistics, Sampling Distributions, and Control Charts


Introduction


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


Summary


Supplementary Exercises



Estimation


Introduction


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


Summary


Supplementary Exercises



Hypothesis Testing


Introduction


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


Summary


Supplementary Exercises



Estimation and Inference for Regression Parameters


Introduction


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


Summary


Supplemental Exercises



Analysis of Variance


Introduction


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


ANOVA for RBD


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


Summary


Supplemental Exercises


Appendix


References