Statistics and Probability with Applications for Engineers and Scientists Using MINITAB, R and JMP

Name: Statistics and Probability with Applications for Engineers and Scientists Using MINITAB, R and JMP
Brand: Wiley
Price: 125.99 EUR
Availability: OnlineOnly

Bhisham C. Gupta Irwin Guttman Kalanka P. Jayalath(Autor*in)

Wiley (Verlag)

2. Auflage

Erschienen am 24. Dezember 2019

1040 Seiten

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Beschreibung

Introduces basic concepts in probability and statistics to data science students, as well as engineers and scientists

Aimed at undergraduate/graduate-level engineering and natural science students, this timely, fully updated edition of a popular book on statistics and probability shows how real-world problems can be solved using statistical concepts. It removes Excel exhibits and replaces them with R software throughout, and updates both MINITAB and JMP software instructions and content. A new chapter discussing data mining--including big data, classification, machine learning, and visualization--is featured. Another new chapter covers cluster analysis methodologies in hierarchical, nonhierarchical, and model based clustering. The book also offers a chapter on Response Surfaces that previously appeared on the book's companion website.

Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP, Second Edition is broken into two parts. Part I covers topics such as: describing data graphically and numerically, elements of probability, discrete and continuous random variables and their probability distributions, distribution functions of random variables, sampling distributions, estimation of population parameters and hypothesis testing. Part II covers: elements of reliability theory, data mining, cluster analysis, analysis of categorical data, nonparametric tests, simple and multiple linear regression analysis, analysis of variance, factorial designs, response surfaces, and statistical quality control (SQC) including phase I and phase II control charts. The appendices contain statistical tables and charts and answers to selected problems.

* Features two new chapters--one on Data Mining and another on Cluster Analysis

* Now contains R exhibits including code, graphical display, and some results

* MINITAB and JMP have been updated to their latest versions

* Emphasizes the p-value approach and includes related practical interpretations

* Offers a more applied statistical focus, and features modified examples to better exhibit statistical concepts

* Supplemented with an Instructor's-only solutions manual on a book's companion website

Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP is an excellent text for graduate level data science students, and engineers and scientists. It is also an ideal introduction to applied statistics and probability for undergraduate students in engineering and the natural sciences.

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Inhalt

Cover
Title Page
Copyright
Contents
Chapter 1 Introduction
1.1 Designed Experiment
1.1.1 Motivation for the Study
1.1.2 Investigation
1.1.3 Changing Criteria
1.1.4 A Summary of the Various Phases of the Investigation
1.2 A Survey
1.3 An Observational Study
1.4 A Set of Historical Data
1.5 A Brief Description of What is Covered in this Book
Chapter 2 Describing Data Graphically and Numerically
2.1 Getting Started with Statistics
2.1.1 What Is Statistics?
2.1.2 Population and Sample in a Statistical Study
2.2 Classification of Various Types of Data
2.2.1 Nominal Data
2.2.2 Ordinal Data
2.2.3 Interval Data
2.2.4 Ratio Data
2.3 Frequency Distribution Tables for Qualitative and Quantitative Data
2.3.1 Qualitative Data
2.3.2 Quantitative Data
2.4 Graphical Description of Qualitative and Quantitative Data
2.4.1 Dot Plot
2.4.2 Pie Chart
2.4.3 Bar Chart
2.4.4 Histograms
2.4.5 Line Graph
2.4.6 Stem-and-Leaf Plot
2.5 Numerical Measures of Quantitative Data
2.5.1 Measures of Centrality
2.5.2 Measures of Dispersion
2.6 Numerical Measures of Grouped Data
2.6.1 Mean of a Grouped Data
2.6.2 Median of a Grouped Data
2.6.3 Mode of a Grouped Data
2.6.4 Variance of a Grouped Data
2.7 Measures of Relative Position
2.7.1 Percentiles
2.7.2 Quartiles
2.7.3 Interquartile Range (IQR)
2.7.4 Coefficient of Variation
2.8 Box-Whisker Plot
2.8.1 Construction of a Box Plot
2.8.2 How to Use the Box Plot
2.9 Measures of Association
2.10 Case Studies
2.10.1 About St. Luke's Hospital
2.11 Using JMP
2.11 Review Practice Problems
Chapter 3 Elements of Probability
3.1 Introduction
3.2 Random Experiments, Sample Spaces, and Events
3.2.1 Random Experiments and Sample Spaces
3.2.2 Events
3.3 Concepts of Probability
3.4 Techniques of Counting Sample Points
3.4.1 Tree Diagram
3.4.2 Permutations
3.4.3 Combinations
3.4.4 Arrangements of n Objects Involving Several Kinds of Objects
3.5 Conditional Probability
3.6 Bayes's Theorem
3.7 Introducing Random Variables
3.7 Review Practice Problems
Chapter 4 Discrete Random Variables and Some Important Discrete Probability Distributions
4.1 Graphical Descriptions of Discrete Distributions
4.2 Mean and Variance of a Discrete Random Variable
4.2.1 Expected Value of Discrete Random Variables and Their Functions
4.2.2 The Moment-Generating Function-Expected Value of a Special Function of X
4.3 The Discrete Uniform Distribution
4.4 The Hypergeometric Distribution
4.5 The Bernoulli Distribution
4.6 The Binomial Distribution
4.7 The Multinomial Distribution
4.8 The Poisson Distribution
4.8.1 Definition and Properties of the Poisson Distribution
4.8.2 Poisson Process
4.8.3 Poisson Distribution as a Limiting Form of the Binomial
4.9 The Negative Binomial Distribution
4.10 Some Derivations and Proofs (Optional)
4.11 A Case Study
4.12 Using JMP
4.12 Review Practice Problems
Chapter 5 Continuous Random Variables and Some Important Continuous Probability Distributions
5.1 Continuous Random Variables
5.2 Mean and Variance of Continuous Random Variables
5.2.1 Expected Value of Continuous Random Variables and Their Functions
5.2.2 The Moment-Generating Function and Expected Value of a Special Function of X
5.3 Chebyshev's Inequality
5.4 The Uniform Distribution
5.4.1 Definition and Properties
5.4.2 Mean and Standard Deviation of the Uniform Distribution
5.5 The Normal Distribution
5.5.1 Definition and Properties
5.5.2 The Standard Normal Distribution
5.5.3 The Moment-Generating Function of the Normal Distribution
5.6 Distribution of Linear Combination of Independent Normal Variables
5.7 Approximation of the Binomial and Poisson Distributions by the Normal Distribution
5.7.1 Approximation of the Binomial Distribution by the Normal Distribution
5.7.2 Approximation of the Poisson Distribution by the Normal Distribution
5.8 A Test of Normality
5.9 Probability Models Commonly used in Reliability Theory
5.9.1 The Lognormal Distribution
5.9.2 The Exponential Distribution
5.9.3 The Gamma Distribution
5.9.4 The Weibull Distribution
5.10 A Case Study
5.11 Using JMP
5.11 Review Practice Problems
Chapter 6 Distribution of Functions of Random Variables
6.1 Introduction
6.2 Distribution Functions of Two Random Variables
6.2.1 Case of Two Discrete Random Variables
6.2.2 Case of Two Continuous Random Variables
6.2.3 The Mean Value and Variance of Functions of Two Random Variables
6.2.4 Conditional Distributions
6.2.5 Correlation between Two Random Variables
6.2.6 Bivariate Normal Distribution
6.3 Extension to Several Random Variables
6.4 The Moment-Generating Function Revisited
6.4 Review Practice Problems
Chapter 7 Sampling Distributions
7.1 Random Sampling
7.1.1 Random Sampling from an Infinite Population
7.1.2 Random Sampling from a Finite Population
7.2 The Sampling Distribution of the Sample Mean
7.2.1 Normal Sampled Population
7.2.2 Nonnormal Sampled Population
7.2.3 The Central Limit Theorem
7.3 Sampling from a Normal Population
7.3.1 The Chi-Square Distribution
7.3.2 The Student t-Distribution
7.3.3 Snedecor's F-Distribution
7.4 Order Statistics
7.4.1 Distribution of the Largest Element in a Sample
7.4.2 Distribution of the Smallest Element in a Sample
7.4.3 Distribution of the Median of a Sample and of the kth Order Statistic
7.4.4 Other Uses of Order Statistics
7.5 Using JMP
7.5 Review Practice Problems
Chapter 8 Estimation of Population Parameters
8.1 Introduction
8.2 Point Estimators for the Population Mean and Variance
8.2.1 Properties of Point Estimators
8.2.2 Methods of Finding Point Estimators
8.3 Interval Estimators for the Mean µ of a Normal Population
8.3.1 s2 Known
8.3.2 s2 Unknown
8.3.3 Sample Size Is Large
8.4 Interval Estimators for The Difference of Means of Two Normal Populations
8.4.1 Variances Are Known
8.4.2 Variances Are Unknown
8.5 Interval Estimators for the Variance of a Normal Population
8.6 Interval Estimator for the Ratio of Variances of Two Normal Populations
8.7 Point and Interval Estimators for the Parameters of Binomial Populations
8.7.1 One Binomial Population
8.7.2 Two Binomial Populations
8.8 Determination of Sample Size
8.8.1 One Population Mean
8.8.2 Difference of Two Population Means
8.8.3 One Population Proportion
8.8.4 Difference of Two Population Proportions
8.9 Some Supplemental Information
8.10 A Case Study
8.11 Using JMP
8.11 Review Practice Problems
Chapter 9 Hypothesis Testing
9.1 Introduction
9.2 Basic Concepts of Testing a Statistical Hypothesis
9.2.1 Hypothesis Formulation
9.2.2 Risk Assessment
9.3 Tests Concerning the Mean of a Normal Population Having Known Variance
9.3.1 Case of a One-Tail (Left-Sided) Test
9.3.2 Case of a One-Tail (Right-Sided) Test
9.3.3 Case of a Two-Tail Test
9.4 Tests Concerning the Mean of a Normal Population Having Unknown Variance
9.4.1 Case of a Left-Tail Test
9.4.2 Case of a Right-Tail Test
9.4.3 The Two-Tail Case
9.5 Large Sample Theory
9.6 Tests Concerning the Difference of Means of Two Populations Having Distributions with Known Variances
9.6.1 The Left-Tail Test
9.6.2 The Right-Tail Test
9.6.3 The Two-Tail Test
9.7 Tests Concerning the Difference of Means of Two Populations Having Normal Distributions with Unknown Variances
9.7.1 Two Population Variances are Equal
9.7.2 Two Population Variances are Unequal
9.7.3 The Paired t-Test
9.8 Testing Population Proportions
9.8.1 Test Concerning One Population Proportion
9.8.2 Test Concerning the Difference Between Two Population Proportions
9.9 Tests Concerning the Variance of a Normal Population
9.10 Tests Concerning the Ratio of Variances of Two Normal Populations
9.11 Testing of Statistical Hypotheses using Confidence Intervals
9.12 Sequential Tests of Hypotheses
9.12.1 A One-Tail Sequential Testing Procedure
9.12.2 A Two-Tail Sequential Testing Procedure
9.13 Case Studies
9.14 Using JMP
9.14 Review Practice Problems
Chapter 10 Elements of Reliability Theory
10.1 The Reliability Function
10.1.1 The Hazard Rate Function
10.1.2 Employing the Hazard Function
10.2 Estimation: Exponential Distribution
10.3 Hypothesis Testing: Exponential Distribution
10.4 Estimation: Weibull Distribution
10.5 Case Studies
10.6 Using JMP
10.6 Review Practice Problems
Chapter 11 On Data Mining
11.1 Introduction
11.2 What is Data Mining?
11.2.1 Big Data
11.3 Data Reduction
11.4 Data Visualization
11.5 Data Preparation
11.5.1 Missing Data
11.5.2 Outlier Detection and Remedial Measures
11.6 Classification
11.6.1 Evaluating a Classification Model
11.7 Decision Trees
11.7.1 Classification and Regression Trees (CART)
11.7.2 Further Reading
11.8 Case Studies
11.9 Using JMP
11.9 Review Practice Problems
Chapter 12 Cluster Analysis
12.1 Introduction
12.2 Similarity Measures
12.2.1 Common Similarity Coefficients
12.3 Hierarchical Clustering Methods
12.3.1 Single Linkage
12.3.2 Complete Linkage
12.3.3 Average Linkage
12.3.4 Ward's Hierarchical Clustering
12.4 Nonhierarchical Clustering Methods
12.4.1 K-Means Method
12.5 Density-Based Clustering
12.6 Model-Based Clustering
12.7 A Case Study
12.8 Using JMP
12.8 Review Practice Problems
Chapter 13 Analysis of Categorical Data
13.1 Introduction
13.2 The Chi-Square Goodness-of-Fit Test
13.3 Contingency Tables
13.3.1 The 2×2 Case with Known Parameters
13.3.2 The 2×2 Case with Unknown Parameters
13.3.3 The r×s Contingency Table
13.4 Chi-Square Test for Homogeneity
13.5 Comments on the Distribution of the Lack-of-Fit Statistics
13.6 Case Studies
13.7 Using JMP
13.7 Review Practice Problems
Chapter 14 Nonparametric Tests
14.1 Introduction
14.2 The Sign Test
14.2.1 One-Sample Test
14.2.2 The Wilcoxon Signed-Rank Test
14.2.3 Two-Sample Test
14.3 Mann-Whitney (Wilcoxon) W Test for Two Samples
14.4 Runs Test
14.4.1 Runs above and below the Median
14.4.2 The Wald-Wolfowitz Run Test
14.5 Spearman Rank Correlation
14.6 Using JMP
14.6 Review Practice Problems
Chapter 15 Simple Linear Regression Analysis
15.1 Introduction
15.2 Fitting the Simple Linear Regression Model
15.2.1 Simple Linear Regression Model
15.2.2 Fitting a Straight Line by Least Squares
15.2.3 Sampling Distribution of the Estimators of Regression Coefficients
15.3 Unbiased Estimator of s2
15.4 Further Inferences Concerning Regression Coefficients (ß0, ß1), E(Y), and Y
15.4.1 Confidence Interval for ß1 with Confidence Coefficient (1-a)
15.4.2 Confidence Interval for ß0 with Confidence Coefficient (1-a)
15.4.3 Confidence Interval for E(Y|X) with Confidence Coefficient (1 - a)
15.4.4 Prediction Interval for a Future Observation Y with Confidence Coefficient (1-a)
15.5 Tests of Hypotheses for ß0 and ß1
15.5.1 Test of Hypotheses for ß1
15.5.2 Test of Hypotheses for ß0
15.6 Analysis of Variance Approach to Simple Linear Regression Analysis
15.7 Residual Analysis
15.8 Transformations
15.9 Inference About ?
15.10 A Case Study
15.11 Using JMP
15.11 Review Practice Problems
Chapter 16 Multiple Linear Regression Analysis
16.1 Introduction
16.2 Multiple Linear Regression Models
16.3 Estimation of Regression Coefficients
16.3.1 Estimation of Regression Coefficients Using Matrix Notation
16.3.2 Properties of the Least-Squares Estimators
16.3.3 The Analysis of Variance Table
16.3.4 More Inferences about Regression Coefficients
16.4 Multiple Linear Regression Model Using Quantitative and Qualitative Predictor Variables
16.4.1 Single Qualitative Variable with Two Categories
16.4.2 Single Qualitative Variable with Three or More Categories
16.5 Standardized Regression Coefficients
16.5.1 Multicollinearity
16.5.2 Consequences of Multicollinearity
16.6 Building Regression Type Prediction Models
16.6.1 First Variable to Enter into the Model
16.7 Residual Analysis and Certain Criteria for Model Selection
16.7.1 Residual Analysis
16.7.2 Certain Criteria for Model Selection
16.8 Logistic Regression
16.9 Case Studies
16.10 Using JMP
16.10 Review Practice Problems
Chapter 17 Analysis of Variance
17.1 Introduction
17.2 The Design Models
17.2.1 Estimable Parameters
17.2.2 Estimable Functions
17.3 One-Way Experimental Layouts
17.3.1 The Model and Its Analysis
17.3.2 Confidence Intervals for Treatment Means
17.3.3 Multiple Comparisons
17.3.4 Determination of Sample Size
17.3.5 The Kruskal-Wallis Test for One-Way Layouts (Nonparametric Method)
17.4 Randomized Complete Block (RCB) Designs
17.4.1 The Friedman Fr-Test for Randomized Complete Block Design (Nonparametric Method)
17.4.2 Experiments with One Missing Observation in an RCB-Design Experiment
17.4.3 Experiments with Several Missing Observations in an RCB-Design Experiment
17.5 Two-Way Experimental Layouts
17.5.1 Two-Way Experimental Layouts with One Observation per Cell
17.5.2 Two-Way Experimental Layouts with r& 1 Observations per Cell
17.5.3 Blocking in Two-Way Experimental Layouts
17.5.4 Extending Two-Way Experimental Designs to n-Way Experimental Layouts
17.6 Latin Square Designs
17.7 Random-Effects and Mixed-Effects Models
17.7.1 Random-Effects Model
17.7.2 Mixed-Effects Model
17.7.3 Nested (Hierarchical) Designs
17.8 A Case Study
17.9 Using JMP
17.9 Review Practice Problems
Chapter 18 The 2k Factorial Designs
18.1 Introduction
18.2 The Factorial Designs
18.3 The 2k Factorial Designs
18.4 Unreplicated 2k Factorial Designs
18.5 Blocking in the 2k Factorial Design
18.5.1 Confounding in the 2k Factorial Design
18.5.2 Yates's Algorithm for the 2k Factorial Designs
18.6 The 2k Fractional Factorial Designs
18.6.1 One-half Replicate of a 2k Factorial Design
18.6.2 One-quarter Replicate of a 2k Factorial Design
18.7 Case Studies
18.8 Using JMP
18.8 Review Practice Problems
Chapter 19 Response Surfaces
19.1 Introduction
19.1.1 Basic Concepts of Response Surface Methodology
19.2 First-Order Designs
19.3 Second-Order Designs
19.3.1 Central Composite Designs (CCDs)
19.3.2 Some Other First-Order and Second-Order Designs
19.4 Determination of Optimum or Near-Optimum Point
19.4.1 The Method of Steepest Ascent
19.4.2 Analysis of a Fitted Second-Order Response Surface
19.5 Anova Table for a Second-Order Model
19.6 Case Studies
19.7 Using JMP
19.7 Review Practice Problems
Chapter 20 Statistical Quality Control-Phase I Control Charts
Chapter 21 Statistical Quality Control-Phase II Control Charts
Appendix A Statistical Tables
Appendix B Answers to Selected Problems
Appendix C Bibliography
Index
EULA

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