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Statistics and Probability for Engineers and Scientists

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ISBN-10: 1118098722

ISBN-13: 9781118098721

Edition: 2012

Authors: Bhisham C. Gupta, Irwin Guttman

List price: $34.95
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Description:

• Low-priced Format: The Preliminary Edition is paperback, and the price is a fraction of the cost of a typical new textbook for the engineering statistics course.• Simple, easy-to-read format: The Preliminary Edition is in a clean manuscript style. The first edition will have professionally designed two-color pages, and illustrations. • The Preliminary Edition includes instructions for the three most popular software tools (Minitab, JMP, and Microsoft Excel). • Software Integration: MINITAB, Microsoft Excel, and JMP. The text incorporates the three most popular software tools -- MINITAB and Microsoft Excel throughout, and JMP at the end of each chapter. This step-by-step approach to the use of software means no prior knowledge of their use is required. After completing a course using this text, students will be able to use these software packages to analyze statistical data in their field of interest.• Breadth of coverage: Besides many popular statistical techniques, the text includes discussion of certain aspects of sampling distributions, nonparametric tests, Phase II control charts, reliability theory, design of experiments, and response surface Methodology. Phase II control charts are discussed in a separate chapter that includes the use of the statistical packages to implement these charts. • Design of experiments and response surface methodology are treated in sufficient breadth and depth to be appropriate for a two-course sequence in engineering statistics that includes probability and design of experiments.• Real data in examples and homework problems illustrate the importance of statistics and probability as a tool for engineers and scientists in their professional lives. All the data sets with 20 or more data points are available on the website in three formats: MINITAB, Microsoft Excel and JMP.• Case studies in each chapter further illustrate the importance of statistical techniques in professional practice.
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Book details

List price: $34.95
Copyright year: 2012
Publisher: John Wiley & Sons, Incorporated
Publication date: 9/13/2011
Binding: Paperback
Pages: 984
Size: 8.25" wide x 10.50" long x 1.25" tall
Weight: 4.048
Language: English

Introduction
Describing Data Graphically and Numerically
Getting Started With Statistics
What is Statistics?
Population and Sample in a Statistical Study
Classification of Various Types of Data
Frequency Distribution Tables for Qualitative and Quantitative Data
Graphical Description of Qualitative and Quantitative Data
Dot Plot
Pie Chart
Bar Chart
Histograms
Line Graph
Stem-and-Leaf Plot
Numerical Measures of Quantitative Data
Measures of Centrality
Measures of Dispersion
Numerical Measures of Grouped Data
Measures of Relative Position
Box-Whisker Plot
Measures of Association
Case Studies
Using JMP
Review Practice Problems
Elements of Probability
Random Experiments, Sample Spaces, and Events
Concepts of Probability
Techniques of Counting Sample Points
Tree Diagrams
Permutations
Combinations
Arrangements of n Objects Involving Several Kinds of Objects
Application of Combinations to Probability Problems
Conditional Probability
Bayes' Theorem
Introducing Random Variables
Review Practice Problems
Discrete Random Variables and Some Important Discrete Probability Distributions
Graphical Descriptions of Discrete Distributions
Mean and Variance of a Discrete Random Variable
The Moment-Generating Function Expectation of a Special Function
The Discrete Uniform Distribution
The Hypergeometric Distribution
The Bernoulli Distribution
The Binomial Distribution
The Multinomial Distribution
The Poisson Distribution
Poisson Distribution as a Limiting Form of the Binomial
The Negative Binomial Distribution
Some Derivations and Proofs (Optional)
Proof that the Probability Function of the Hypergeometric Distribution Sums to
Mean and the Variance of the Hypergeometric Distribution
Mean and the Variance of the Binomial Distribution
Mean and the Variance of the Poisson Distribution
Derivation of the Poisson Distribution
A Case Study
Using JMP
Review Practice Problems
Continuous Random Variables and Some Important Continuous Probability Distributions
Continuous Random Variables
Mean and Variance of Continuous Random Variables
The Moment-Generating Function - Expectation of a Special Function
Chebychev's Inequality
The Uniform Distribution
The Normal Distribution
Definition and Properties
The Standard Normal Distribution
The Moment-Generating Function of the Normal Distribution
Distribution of Linear Combinations of Independent Normal Variables
Approximation of the Binomial Distribution by the Normal Distribution
A Test of Normality
The Lognormal Distribution
The Exponential Distribution
The Gamma Distribution
The Weibull Distribution
A Case Study
Using JMP
Review Practice Problems
Distribution Functions of Random Variables
Distribution Functions of Two Random Variables
Case of Two Discrete Random Variables
Case of Two Continuous Random Variables
The Mean Value and Variance of Functions of Two Random Variables
Conditional Distributions
Correlation Between Two Random Variables
Bivariate Normal Distribution
Extension to Several Random Variables
The Moment-Generating Function Revisited
Review Practice Problems
Sampling Distribution
Random Sampling
Random Sampling from an Infinite Population
Random Sampling from a Finite Population
The Sampling Distribution of the Mean
The Central Limit Theorem
Sampling from a Normal Population
The Chi-Square Distribution
The Student t Distribution
Snedecor's F Distribution
Order Statistics
Distribution of the Largest Element in a Sample
Distribution of the Smallest Element in a Sample
Distribution of the Median of a Sample and of the kth-Order Statistic
The Range as an Estimate of in Normal Samples
Using JMP
Review Practice Problems
Estimation of Population Parameters
Introduction
Point Estimators for the Population Mean and Variance
Properties of Point Estimators
Methods of Finding Point Estimators
Interval Estimators for the Mean of a Normal Population
Known
Unknown
Sample Size is Large
Interval Estimators for the Difference of Means of Two Normal Populations
Variances are Known
Variances are Unknown
Interval Estimators for the Variance of a Normal Population
Interval Estimators for the Ratio of Variances of Two Normal Populations
Point and Interval Estimators for the Parameters of Binomial Populations
One Binomial Population
Two Binomial Populations
Determination of Sample Size
Some Supplemental Information (Optional)
Proof of
Predicting an Arbitrary Observation
A Case Study
Using JMP
Review Practice Problems
Hypothesis Testing
Introduction
Basic Concepts of Testing a Statistical Hypothesis
Tests Concerning the Mean of a Normal Distribution Having Known Variance
Tests Concerning the Mean of a Normal Population Having Unknown Variance
Large Sample Theory
Tests Concerning the Difference of Means of Two Populations Having Distributions with Known Variances
Tests Concerning the Difference of Means of Two Populations Having Distributions with Unknown Variances
Two Population Variances Are Equal
Two Population Variances Are Not Equal
The Paired t-Test
Testing Population Proportions
Testing Concerning the One Population Proportion
Testing Concerning the Difference Between Two Population Proportions
Tests Concerning the Variance of a Normal Distribution
Tests Concerning the Ratio of Variances of Two Normal Populations
An Alternative Technique for Testing of Statistical Hypotheses: Using Confidence Intervals
Sequential Tests of Hypotheses (Optional)
A One-Sided Sequential Testing Procedure
A Two-Sided Sequential Testing Procedure
Case Studies
Using JMP
Review Practice Problems
Elements of Reliability Theory
The Reliability Function
The Hazard Rate
Employing the Hazard Function
Estimation: Exponential Distribution
Hypothesis Testing: Exponential Distribution
Estimation: Weibull Distribution
Case Studies
Using JMP
Review Practice Problems
Statistical Quality Control and Phase I Control Charts
Basic Concepts of Quality and Its Benefits
What Is a Process?
Common and Assignable Causes
Control Charts
Control Charts for Variables
Shewhart and R Control Chart
Shewhart and R Control Chart When Process Mean and Process Standard Deviation Are Known
The Shewhart and S Control Chart
Control Charts for Attributes
The p Chart: Control Chart for the Fraction of Nonconforming Units
The p Chart: Control Chart for the Fraction of Nonconforming units with Variable Sample Sizes
The np Control Chart: Control Chart for Number of Nonconforming Units
The C Control Chart
The U Control Chart
Process Capability
Case Studies
Using JMP
Review Practice Problems
Statistical Quality Control and Phase II Control Charts
Basic Concepts of CUSUM Control Chart
Designing a CUSUM Control Chart
Two-Sided CUSUM Control Chart Using a Numerical Procedure
The Fast Initial Response (FIR) Feature for the CUSUM Control Chart
The Combined Shewhart-CUSUM Control Chart
The CUSUM Control Chart for Controlling Process Variability
The Moving Average (MA) Control Chart
The Exponentially Weighted Moving Average (EWMA) Control Chart
Case Studies
Using JMP
Review Practice Problems
Analysis of Categorical Data
Introduction
The Chi-Square Goodness of Fit Test
Contingency Tables
The 2 2 Case Parameters Known
The Case Parameters Unknown
The Contingency Table
Chi-Square Test for Homogeneity
Comments on the Distribution of the Lack-of-Fit Statistic (optional)
Case Studies
Using JMP
Review Practice Problems
Nonparametric Tests
Introduction
The Sign Test
One-Sample Test
The Wilcoxon Signed-Rank Test
Two-sample Test
The Mann-Whitney (Wilcoxon) W Test for Two Samples
Run Tests
Runs Above and Below the Median
The Wald-Wolfowitz Run Test
Spearman Rank Correlation
Using JMP
Review Practice Problems
Simple Linear Regression Analysis
Introduction
Fitting the Simple Linear Regression Model
Simple Linear Regression Model
Fitting a Straight Line by Least Squares
Sampling Distributions of the Estimators of Regression Coefficients
Unbiased Estimator of
Further Inferences Concerning Regression Coefficients and
Confidence Interval for with Confidence Coefficient
Confidence Interval for with Confidence Coefficient
Confidence Interval for with Confidence Coefficient
Prediction Interval for a Future Observation with Confidence Coefficient
Test of Hypotheses for
Analysis of Variance Approach to Simple Regression Analysis
Residual Analysis
Transformations
Inference About
A Case Study
Using JMP
Review Practice Problems
Multiple Linear Regression Analysis
Introduction
The Multiple Linear Regression Model
Estimation of Regression Coefficients
Estimation of Regression Coefficients Using Matrix Notation
Properties of the Least-Squares Estimators
The Analysis of Variance Table
More Inferences About Regression Parameters
The Multiple Linear Regression Model Using Qualitative or Categorical Predictor Variables
Standardized Regression Coefficients
Building Regression Type Prediction Models
Residual Analysis
Certain Criteria for Model Selection