Gupta B., Guttman I., Jayalath K. Statistics and Probability with Applications for Engineers and Scientists Using MINITAB, R and JMP

2nd Edition. — Wiley, 2020. — 1034 p. — ISBN 978-1-119-51664-4.Introduces basic concepts in probability and statistics to data science students, as well as engineers and scientistsAimed 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.This book, 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 websiteStatistics 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.True PDFPreface XVII.
Acknowledgments XXI.
About The Companion Site XXIII.
Introduction.
Designed Experiment.
Motivation for the Study.
Investigation.
Changing Criteria.
A Summary of the Various Phases of the Investigation.
A Survey.
An Observational Study.
A Set of Historical Data.
A Brief Description of What is Covered in this Book.Fundamentals of Probability and Statistics
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.
Nominal Data.
Ordinal Data.
Interval Data.
Ratio Data.
Frequency Distribution Tables for Qualitative and Quantitative Data.
Qualitative Data.
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.
Mean of a Grouped Data.
Median of a Grouped Data.
Mode of a Grouped Data.
Variance of a Grouped Data.
Measures of Relative Position.
Percentiles.
Quartiles.
Interquartile Range (IQR).
Coefficient of Variation.
Box-Whisker Plot.
Construction of a Box Plot.
How to Use the Box Plot.
Measures of Association.
Case Studies.
About St. Luke’s Hospital.
Using JMP.
Review Practice Problems.
Elements of Probability.Random Experiments, Sample Spaces, and Events.
Random Experiments and Sample Spaces.
Events.
Concepts of Probability.
Techniques of Counting Sample Points.
Tree Diagram.
Permutations.
Combinations.
Arrangements of n Objects Involving Several Kinds of Objects.
Conditional Probability.
Bayes’s 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.
Expected Value of Discrete Random Variables and Their Functions.
The Moment-Generating Function-Expected Value of a Special Function of X.
The Discrete Uniform Distribution.
The Hypergeometric Distribution.
The Bernoulli Distribution.
The Binomial Distribution.
The Multinomial Distribution.
The Poisson Distribution.
Definition and Properties of the Poisson Distribution.
Poisson Process.
Poisson Distribution as a Limiting Form of the Binomial.
The Negative Binomial Distribution.
Some Derivations and Proofs (Optional).
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.
Expected Value of Continuous Random Variables and Their Functions.
The Moment-Generating Function and Expected Value of a Special Function of X.
Chebyshev’s Inequality.
The Uniform Distribution.
Definition and Properties.
Mean and Standard Deviation of the Uniform Distribution.
The Normal Distribution.
Definition and Properties.
The Standard Normal Distribution.
The Moment-Generating Function of the Normal Distribution.
Distribution of Linear Combination of Independent Normal Variables.
Approximation of the Binomial and Poisson Distributions by the Normal Distribution.
Approximation of the Binomial Distribution by the Normal Distribution.
Approximation of the Poisson Distribution by the Normal Distribution.
A Test of Normality.
Probability Models Commonly used in Reliability Theory.
The Lognormal Distribution.
The Exponential Distribution.
The Gamma Distribution.
The Weibull Distribution.
A Case Study.
Using JMP.
Review Practice Problems.
Distribution of 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 Distributions.
Random Sampling.
Random Sampling from an Infinite Population.
Random Sampling from a Finite Population.
The Sampling Distribution of the Sample Mean.
Normal Sampled Population.
Nonnormal Sampled Population.
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.
Other Uses of Order Statistics.
Using JMP.
Review Practice Problems.
Estimation of Population Parameters.Point Estimators for the Population Mean and Variance.
Properties of Point Estimators.
Methods of Finding Point Estimators.
Interval Estimators for the Mean μ of a Normal Population.
σ2 Known.
σ2 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 Estimator 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.
One Population Mean.
Difference of Two Population Means.
One Population Proportion.
Difference of Two Population Proportions.
Some Supplemental Information.
A Case Study.
Using JMP.
Review Practice Problems.
Hypothesis Testing.Basic Concepts of Testing a Statistical Hypothesis.
Hypothesis Formulation.
Risk Assessment.
Tests Concerning the Mean of a Normal Population Having Known Variance.
Case of a One-Tail (Left-Sided) Test.
Case of a One-Tail (Right-Sided) Test.
Case of a Two-Tail Test.
Tests Concerning the Mean of a Normal Population Having Unknown Variance.
Case of a Left-Tail Test.
Case of a Right-Tail Test.
The Two-Tail Case.
Large Sample Theory.
Tests Concerning the Difference of Means of Two Populations Having Distributions with Known Variances.
The Left-Tail Test.
The Right-Tail Test.
The Two-Tail Test.
Tests Concerning the Difference of Means of Two Populations Having Normal Distributions with Unknown Variances.
Two Population Variances are Equal.
Two Population Variances are Unequal.
The Paired t-Test.
Testing Population Proportions.
Test Concerning One Population Proportion.
Test Concerning the Difference Between Two Population Proportions.
Tests Concerning the Variance of a Normal Population.
Tests Concerning the Ratio of Variances of Two Normal Populations.
Testing of Statistical Hypotheses using Confidence Intervals.
Sequential Tests of Hypotheses.
A One-Tail Sequential Testing Procedure.
A Two-Tail Sequential Testing Procedure.
Case Studies.
Using JMP.
Review Practice Problems.Statistics in Actions
Elements of Reliability Theory.
The Reliability Function.
The Hazard Rate Function.
Employing the Hazard Function.
Estimation: Exponential Distribution.
Hypothesis Testing: Exponential Distribution.
Estimation: Weibull Distribution.
Case Studies.
Using JMP.
Review Practice Problems.
On Data Mining.What is Data Mining?
Big Data.
Data Reduction.
Data Visualization.
Data Preparation.
Missing Data.
Outlier Detection and Remedial Measures.
Classification.
Evaluating a Classification Model.
Decision Trees.
Classification and Regression Trees (CART).
Further Reading.
Case Studie.
Using JMP.
Review Practice Problems.
Cluster Analysis.Similarity Measures.
Common Similarity Coefficients.
Hierarchical Clustering Methods.
Single Linkage.
Complete Linkage.
Average Linkage.
Ward’s Hierarchical Clustering.
Nonhierarchical Clustering Methods.
K-Means Method.
Density-Based Clustering.
Model-Based Clustering.
A Case Study.
Using JMP.
Review Practice Problems.
Analysis of Categorical Data.The Chi-Square Goodness-of-Fit Test.
Contingency Tables.
The 2 × 2 Case with Known Parameters.
The 2 × 2 Case with Unknown Parameters.
The r × s Contingency Table.
Chi-Square Test for Homogeneity.
Comments on the Distribution of the Lack-of-Fit Statistics.
Case Studies.
Using JMP.
Review Practice Problems.
Nonparametric Tests.The Sign Test.
One-Sample Test.
The Wilcoxon Signed-Rank Test.
Two-Sample Test.
Mann–Whitney (Wilcoxon) W Test for Two Samples.
Runs Test.
Runs above and below the Median.
The Wald–Wolfowitz Run Test.
Spearman Rank Correlation.
Using JMP.
Review Practice Problems.
Simple Linear Regression Analysis.Fitting the Simple Linear Regression Model.
Simple Linear Regression Model.
Fitting a Straight Line by Least Squares.
Sampling Distribution of the Estimators of Regression Coefficients.
Unbiased Estimator of σ2.
Further Inferences Concerning Regression Coefficients (β0, β1), E(Y ), and Y.
Confidence Interval for β1 with Confidence Coefficient (1 − α).
Confidence Interval for β0 with Confidence Coefficient (1 − α).
Confidence Interval for E(Y |X) with Confidence Coefficient (1 − α).
Prediction Interval for a Future Observation Y with Confidence Coefficient (1 − α).
Tests of Hypotheses for β0 and β1.
Test of Hypotheses for β1.
Test of Hypotheses for β0.
Analysis of Variance Approach to Simple Linear Regression Analysis.
Residual Analysis.
Transformations.
Inference About ρ.
A Case Study.
Using JMP.
Review Practice Problems.
Multiple Linear Regression Analysis.Multiple Linear Regression Models.
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 Coefficients.
Multiple Linear Regression Model Using Quantitative and Qualitative Predictor Variables.
Single Qualitative Variable with Two Categories.
Single Qualitative Variable with Three or More Categories.
Standardized Regression Coefficients.
Multicollinearity.
Consequences of Multicollinearity.
Building Regression Type Prediction Models.
First Variable to Enter into the Model.
Residual Analysis and Certain Criteria for Model Selection.
Residual Analysis.
Certain Criteria for Model Selection.
Logistic Regression.
Case Studies.
Using JMP.
Review Practice Problems.
Analysis of Variance.The Design Models.
Estimable Parameters.
Estimable Functions.
One-Way Experimental Layouts.
The Model and Its Analysis.
Confidence Intervals for Treatment Means.
Multiple Comparisons.
Determination of Sample Size.
The Kruskal–Wallis Test for One-Way Layouts (Nonparametric Method).
Randomized Complete Block (RCB) Designs.
The Friedman Fr-Test for Randomized Complete Block Design (Nonparametric Method).
Experiments with One Missing Observation in an RCB-Design Experiment.
Experiments with Several Missing Observations in an RCB-Design Experiment.
Two-Way Experimental Layouts.
Two-Way Experimental Layouts with One Observation per Cell.
Two-Way Experimental Layouts with r > 1 Observations per Cell.
Blocking in Two-Way Experimental Layouts.
Extending Two-Way Experimental Designs to n-Way Experimental Layouts.
Latin Square Designs.
Random-Effects and Mixed-Effects Models.
Random-Effects Model.
Mixed-Effects Model.
Nested (Hierarchical) Designs.
A Case Study.
Using JMP.
Review Practice Problems.
The 2k Factorial Designs.The Factorial Designs.
The 2k Factorial Designs.
Unreplicated 2k Factorial Designs.
Blocking in the 2k Factorial Design.
Confounding in the 2k Factorial Design.
Yates’s Algorithm for the 2k Factorial Designs.
The 2k Fractional Factorial Designs.
One-half Replicate of a 2k Factorial Design.
One-quarter Replicate of a 2k Factorial Design.
Case Studies.
Using JMP.
Review Practice Problems.
Response Surfaces.Basic Concepts of Response Surface Methodology.
First-Order Designs.
Second-Order Designs.
Central Composite Designs (CCDs).
Some Other First-Order and Second-Order Designs.
Determination of Optimum or Near-Optimum Point.
The Method of Steepest Ascent.
Analysis of a Fitted Second-Order Response Surface.
Anova Table for a Second-Order Model.
Case Studies.
Using JMP.
Review Practice Problems.
Statistical Quality Control — Phase I Control Charts.
Statistical Quality Control — Phase II Control Charts.Statistical Tables.
Answers to Selected Problems.