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 Wainwright, Martin (Martin J.), author.
 Cambridge ; New York, NY : Cambridge University Press, 2019.
 Description
 Book — pages cm.
 Summary

 1. Introduction
 2. Basic tail and concentration bounds
 3. Concentration of measure
 4. Uniform laws of large numbers
 5. Metric entropy and its uses
 6. Random matrices and covariance estimation
 7. Sparse linear models in high dimensions
 8. Principal component analysis in high dimensions
 9. Decomposability and restricted strong convexity
 10. Matrix estimation with rank constraints
 11. Graphical models for highdimensional data
 12. Reproducing kernel Hilbert spaces
 13. Nonparametric least squares
 14. Localization and uniform laws
 15. Minimax lower bounds References Author index Subject index.
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QA276.18 .W35 2019  Unknown 
2. Introduction to mathematical statistics [2019]
 Hogg, Robert V. author.
 Eighth edition.  Boston : Pearson, [2019]
 Description
 Book — xiii, 746 pages ; 26 cm
 Online
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QA276 .H59 2019  Unknown 
3. Linear algebra and learning from data [2019]
 Strang, Gilbert, author.
 Wellesley, MA : WellesleyCambridge Press, [2019]
 Description
 Book — xiii, 432 pages ; 25 cm
 Online
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QA184.2 .S77 2019  Unknown 
 Moulin, Pierre, 1963 author.
 Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2019.
 Description
 Book — xxi, 399 pages ; 26 cm
 Summary

 1. Introduction Part I. Hypothesis Testing:
 2. Binary hypothesis testing
 3. Multiple hypothesis testing
 4. Composite hypothesis testing
 5. Signal detection
 6. Convex statistical distances
 7. Performance bounds for hypothesis testing
 8. Large deviations and error exponents for hypothesis testing
 9. Sequential and quickest change detection
 10. Detection of random processes Part II. Estimation:
 11. Bayesian parameter estimation
 12. Minimum variance unbiased estimation
 13. Information inequality and CramerRao lower bound
 14. Maximum likelihood estimation
 15. Signal estimation.
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QA276 .M73 2019  Unknown 
5. Statistics : learning from data [2019]
 Peck, Roxy, author.
 Edition 2.  Boston, MA, USA : Cengage Learning, Inc., [2019]
 Description
 Book — xvii, 887 pages ; 29 cm
 Summary

 Section I: COLLECTING DATA.
 1. Collecting Data in Reasonable Ways. Statistics: It's All About Variability. Statistical Studies: Observation and Experimentation. Collecting Data: Planning an Observational Study. Collecting Data: Planning an Experiment. The Importance of Random Selection and Random Assignment: What Types of Conclusions Are Reasonable? Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
 Section II: DESCRIBING DATA DISTRIBUTIONS.
 2. Graphical Methods for Describing Data Distributions. Selecting an Appropriate Graphical Display. Displaying Categorical Data: Bar Charts and Comparative Bar Charts. Displaying Numerical Data: Dotplots, StemandLeaf Displays, and Histograms. Displaying Bivariate Numerical Data: Scatterplots and TimeSeries Plots. Graphical Displays in the Media. Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
 3. Numerical Methods for Describing Data Distributions. Selecting Appropriate Numerical Summaries. Describing Center and Variability for Data Distributions that are Approximately Symmetric. Describing Center and Variability for Data Distributions that are Skewed or Have Outliers. Summarizing a Data Set: Boxplots. Measures of Relative Standing: zscores and Percentiles. Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
 4. Describing Bivariate Numerical Data. Correlation. Linear Regression: Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Describing Linear Relationships and Making PredictionsPutting it all Together. Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking. Bonus Material on Logistic Regression (Online).
 Section III: A FOUNDATION FOR INFERENCE: REASONING ABOUT PROBABILITY.
 5. Probability. Interpreting Probabilities. Computing Probabilities. Probabilities of More Complex Events: Unions, Intersections and Complements. Conditional Probability. Calculating Probabilities
 A More Formal Approach. Probability as a Basis for Making Decisions. Estimating Probabilities Empirically and Using Simulation (Optional). Chapter Activities.
 6. Random Variables and Probability Distributions. Random Variables. Probability Distributions for Discrete Random Variables. Probability Distributions for Continuous Random Variables. The Mean and Standard Deviation of a Random Variable. Normal Distribution. Checking for Normality. Binomial and Geometric Distributions (Optional). Using the Normal Distribution to Approximate a Discrete Distribution (Optional). Chapter Activities. Bonus Material on Counting Rules, The Poisson Distribution (Online).
 Section IV: LEARNING FROM SAMPLE DATA.
 7. An Overview of Statistical Inference
 Learning from Data. Statistical Inference
 What You Can Learn from Data. Selecting an Appropriate Method
 Four Key Questions. A FiveStep Process for Statistical Inference. Chapter Activities.
 8. Sampling Variability and Sampling Distributions. Statistics and Sampling Variability. The Sampling Distribution of a Sample Proportion. How Sampling Distributions Support Learning from Data. Chapter Activities.
 9. Estimating a Population Proportion. Selecting an Estimator. Estimating a Population Proportion
 Margin of Error. A Large Sample Confidence Interval for a Population Proportion. Choosing a Sample Size to Achieve a Desired Margin of Error. Bootstrap Confidence Intervals for a Population Proportion (Optional). Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
 10. Asking and Answering Questions about a Population Proportion. Hypotheses and Possible Conclusions. Potential Errors in Hypothesis Testing. The Logic of Hypothesis Testing
 An Informal Example. A Procedure for Carrying Out a Hypothesis Test. LargeSample Hypothesis Tests for a Population Proportion. Randomization Tests and Exact Binomial Tests for One Proportion (Optional). Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
 11. Asking and Answering Questions about the Difference between Two Population Proportions. Estimating the Difference between Two Population Proportions. Testing Hypotheses about the Difference between Two Population Proportions. Inference for Two Proportions Using Data from an Experiment. SimulationBased Inference for Two Proportions (Optional). Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
 12. Asking and Answering Questions about a Population Mean. The Sampling Distribution of the Sample Mean. A Confidence Interval for a Population Mean. Testing Hypotheses about a Population Mean. SimulationBased Inference for One Mean (Optional). Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
 13. Asking and Answering Questions about the Difference between Two Population Means. Two Samples: Paired versus Independent Samples. Learning About a Difference in Population Means Using Paired Samples. Learning About a Difference in Population Means Using Independent Samples. Inference for Two Means Using Data from an Experiment. SimulationBased Inference for Two Means (Optional). Avoid These Common Mistakes. Chapter Activities. Explorations in Statistical Thinking.
 Section V: ADDITIONAL OPPORTUNITIES TO LEARN FROM DATA.
 14. Learning from Categorical Data. ChiSquare Tests for Univariate Categorical Data. Tests for Homogeneity and Independence in a TwoWay Table. Avoid These Common Mistakes. Chapter Activities.
 15. Understanding RelationshipsNumerical Data
 Part 2 (Online). The Simple Linear Regression Model. Inferences Concerning the Slope of the Population Regression Line. Checking Model Adequacy.
 16. Asking and Answering Questions about More Than Two Means (Online). The Analysis of Variance
 SingleFactor ANOVA and the F Test. Multiple Comparisons. Appendix: ANOVA Computations.
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QA276.12 .P434 2019  Unknown 
6. Understanding basic statistics [2019]
 Brase, Charles Henry, author.
 Eighth edition.  Boston, MA : Cengage, [2019]
 Description
 Book — xxvii, 600, 54, 5 pages ; 28 cm
 Summary

Brase/Brase's UNDERSTANDING BASIC STATISTICS, 8th Edition, provides instructors a streamlined and effective way to teach the essentials of statistics, including early coverage of regression, within a more limited timeframe. With simulation questions, labs, projects, newssourced videos with questions, and more available in the online course, this solution includes an entire course package designed to teach students the basics of statistics and how to apply those to realworld situations. Help your students think statistically, overcome their apprehension about statistics, and learn to love a subject that once inspired anxiety. In this 8th Edition, students see the realworld significance of statistics and engage with new features that help them develop critical thinking and statistical literacy skills. The use of the graphing calculator, Microsoft Excel, MINITAB, MINITAB EXPRESS, and SPSS is covered but not required.
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QA276.12 .B725 2019  Unknown 
7. Algebraic statistics [2018]
 Sullivant, Seth, author.
 Providence, Rhode Island : American Mathematical Society, [2018]
 Description
 Book — xiii, 490 pages : illustrations ; 26 cm.
 Summary

 Introduction Probability Primer Algebra Primer Conditional Independence Statistics Primer Exponential Families Likelihood Inference The Cone of Sufficient Statistics Fisher's Exact Test Bounds on Cell Entries Exponential Random Graph Models Design of Experiments Graphical Models Hidden Variables Phylogenetic Models Identifiability Model Selection and Bayesian Integrals MAP Estimation and Parametric Inference Finite Metric Spaces Bibliography Index.
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QA276 .S8945 2018  Unknown 
 Rosen, Dietrich von, author.
 Cham : Springer, [2018]
 Description
 Book — xiii, 468 pages : illustrations ; 24 cm.
 Summary

 Introduction
 What Is Statistics
 What Is a Statistical Model
 The General Univariate Linear Model with a Known Dispersion
 The General Multivariate Linear Model
 Bilinear Regression Models : An Introduction
 Problems
 Literature
 References
 The Basic Ideas of Obtaining MLEs : A Known Dispersion
 Introduction
 Linear Models with a Focus on the Singular GaussMarkov Model
 Multivariate Linear Models
 BRM with a Known Dispersion Matrix
 EBRM... with a Known Dispersion Matrix
 EBRM... with a Known Dispersion Matrix
 Problems
 Literature
 References
 The Basic Ideas of Obtaining MLEs : Unknown Dispersion
 Introduction
 BRM and Its MLEs
 EBRM... and Its MLEs
 EBRM... and Its MLEs
 Reasons for Using Both the EBRM... and the EBRM...
 Problems
 Literature
 References
 Basic Properties of Estimators
 Introduction
 Asymptotic Properties of Estimators of Parameters in the BRM
 Moments of Estimators of Parameters in the BRM
 EBRM... and Uniqueness Conditions for MLEs
 Asymptotic Properties of Estimators of Parameters in the EBRM...
 Moments of Estimators of Parameters in the EBRM...
 EBRM... and Uniqueness Conditions for MLEs
 Asymptotic Properties of Estimators of Parameters in the EBRM...
 Moments of Estimators of Parameters in the EBRM...
 Problems
 Literature
 References
 Density Approximations
 Introduction
 Preparation
 Density Approximation for the Mean Parameter in the BRM
 Density Approximation for the Mean Parameter Estimators in the EBRM...
 Density Approximation for the Mean Parameter Estimators in the EBRM...
 Problems
 Literature
 References
 Residuals
 Introduction
 Residuals for the BRM
 Distribution Approximations of the Residuals in the BRM
 Mean Shift Evaluations of the Residuals in the BRM
 Residual Analysis for R₁ in the BRM
 Residuals for the EBRM³...
 Residuals for the EBRM³...
 Problems
 Literature
 References
 Testing Hypotheses
 Introduction
 Background
 Likelihood Ratio Testing, H₀ : FBG = 0, in the BRM
 Likelihood Ratio Testing H₀ : F₁BG₁ = 0 in the BRM with the Restrictions F₂BG₂ = 0, C(F'₁) ... C(F'₂)
 Likelihood Ratio Testing H₀ : F₂BG₂ = 0 in the BRM with the Restrictions F₁BG₁ = 0, C(F'₁) ... C(F'₂) and C(G₂) ...(G₁)
 Likelihood Ratio Testing H₀ : FiBGi = 0, i = 1,2, Against B Unrestricted in the BRM with C(F'₁) ... C(F'₂)
 Likelihood Ratio Testing H₀ : FiBGi = 0, i = 1,2, Against B Unrestricted in the BRM with C(F'₁) ... C(F'₂) and C(G₂) ...(G₁)
 A "Trace Test" for the BRM, Ho : FBG = 0 Against Unrestricted B
 A "Trace Test" for the BRM, Ho : FiBGi = 0, i = 1, 2, C(F'₁) ... C(F'₂), Against Unrestricted B
 The Likelihood Ratio Test Versus the "Trace Test"
 Testing an EBRM³... Against a BRM
 Estimating and Testing in the BRM with F₁BG₁ = F₂OG₂
 Problems
 Literature
 References
 Influential Observations
 Introduction
 Influence Analysis in Univariate Linear Models
 Influence Analysis in the BRM
 Influence Analysis in the EBRM³...
 Influence Analysis in the EBRM³...
 Problems
 Literature
 References
 Appendices
 Appendix A : Notation
 Appendix B : Useful Technical Results
 Problems
 Appendix C : Test Statistics
 References
 Subject Index
 Index : Theorems and Corollaries
 Index : Figures and Tables.
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QA278.2 .R67 2018  Unknown 
 Bluman, Allan G., author.
 Tenth edition.  New York, NY : McGrawHill Education, [2018]
 Description
 Book — xxiii, 808, SA44, I4 pages ; 29 cm
 Summary

 1 The Nature of Probability and Statistics1.1 Descriptive and Inferential Statistics1.2 Variables and Types of Data1.3 Data Collection and Sampling Techniques1.4 Observational and Experimental Studies1.5 Uses and Misuses of Statistics1.6 Computers and Calculators2 Frequency Distributions and Graphs2.1 Organizing Data2.2 Histograms, Frequency Polygons, and Ogives2.3 Other Types of Graphs3 Data Description3.1 Measures of CentralTendency3.2 Measures of Variation3.3 Measures of Position3.4 Exploratory Data Analysis4 Probability and Counting Rules4.1 Exploratory Data Analysis4.2 The Addition Rules for Probability4.3 The Multiplication Rules and ConditionalProbability4.4 Counting Rules4.5 Probability and Counting Rules5 Discrete Probability Distributions5.1 Probability Distributions5.2 Mean, Variance, Standard Deviation, and Expectation5.3 The Binomial Distribution5.4 Other Types of Distributions (Optional)6 The Normal Distribution6.1 Normal Distributions6.2 Applications of the NormalDistribution6.3 The Central Limit Theorem6.4 The Normal Approximation to the BinomialDistribution7 Confidence Intervals and Sample Size7.1 Confidence Intervals for the Mean When Ï Is Known7.2 Confidence Intervals for the Mean When Ï Is Unknown7.3 Confidence Intervals and Sample Size for Proportions7.4 Confidence Intervals for Variances and Standard Deviations8 Hypothesis Testing8.1 Steps in Hypothesis TestingTraditional Method8.2 z Test for a Mean8.3 t Test for a Mean8.4 z Test for a Proportion8.5 Ï
 2 Test for a Variance or Standard Deviation8.6 Additional Topics Regarding Hypothesis Testing9 Testing the Difference Between Two Means, Two Variances, and Two Proportions9.1 Testing the Difference Between Two Means: Using the z Test9.2 Testing the Difference Between Two Means of Independent Samples: Using the t Test9.3 Testing the Difference Between Two Means: Dependent Samples9.4 Testing the Difference Between Proportions9.5 Testing the Difference Between Two Variances10 Correlation and Regression10.1 Scatter Plots and Correlation10.2 Regression10.3 Coefficient of Determination and Standard Error of the Estimate10.4 Multiple Regression (Optional)11 Other ChiSquare Tests11.1 Test for Goodness of Fit11.2 Tests Using Contingency Tables12 Analysis of Variance12.1 OneWay Analysis of Variance12.2 The Scheffe Test and the Tukey Test12.3 TwoWay Analysis of Variance13 Nonparametric Statistics13.1 Advantages and Disadvantages of Nonparametric Methods13.2 The Sign Test13.3 The Wilcoxon Rank Sum Test13.4 The Wilcoxon SignedRank Test13.5 The KruskalWallis Test13.6 The Spearman Rank Correlation Coefficient and the Runs Test14 Sampling and Simulation14.1 Common Sampling Techniques14.2 Surveys and Questionnaire Design14.3 Simulation Techniques and the Monte Carlo MethodAppendicesAppendix A: TablesAppendix B: Data BankAppendix C: GlossaryAppendix D: Photos Credits Appendix E: Selected AnswersAdditional Topics OnlneAlgebra ReviewWriting the Research ReportBayes' TheoremAlternate Approach to the Standard Normal DistributionBibliography.
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QA276.12 .B59 2018  Unknown 
10. Excursions in modern mathematics [2018]
 Tannenbaum, Peter, 1946 author.
 9th edition.  [Upper Saddle, NJ] : Pearson, [2018]
 Description
 Book — xviii, 570 pages : color illustrations ; 29 cm
 Summary

 1 The Mathematics of Elections 1.1 The Basic Elements of an Election 1.2 The Plurality Method 1.3 The Borda Count Method 1.4 The PluralitywithElimination Method 1.5 The Method of Pairwise Comparisons 1.6 Fairness Criteria and Arrow's Impossibility Theorem
 2 The Mathematics of Power 2.1 An Introduction to Weighted Voting 2.2 Banzhaf Power 2.3 ShapleyShubik Power 2.4 Subsets and Permutations
 3 The Mathematics of Sharing 3.1 FairDivision Games 3.2 The DividerChooser Method 3.3 The LoneDivider Method 3.4 The LoneChooser Method 3.5 The Method of Sealed Bids 3.6 The Method of Markers
 4 The Mathematics of Apportionment 4.1 Apportionment Problems and Apportionment Methods 4.2 Hamilton's Method 4.3 Jefferson's Method 4.4 Adams's and Webster's Methods 4.5 The HuntingtonHill Method 4.6 The Quota Rule and Apportionment Paradoxes
 5 The Mathematics of Getting Around 5.1 StreetRouting Problems 5.2 An Introduction to Graphs 5.3 Euler's Theorems and Fleury's Algorithm 5.4 Eulerizing and SemiEulerizing Graphs
 6 The Mathematics of Touring 6.1 What Is a Traveling Salesman Problem? 6.2 Hamilton Paths and Circuits 6.3 The BruteForce Algorithm 6.4 The NearestNeighbor and Repetitive NearestNeighbor Algorithms 6.5 The CheapestLink Algorithm
 7 The Mathematics of Networks 7.1 Networks and Trees 7.2 Spanning Trees, MSTs, and MaxSTs 7.3 Kruskal's Algorithm
 8 The Mathematics of Scheduling 8.1 An Introduction to Scheduling 8.2 Directed Graphs 8.3 PriorityList Scheduling 8.4 The DecreasingTime Algorithm 8.5 Critical Paths and the CriticalPath Algorithm
 9 Population Growth Models 9.1 Sequences and Population Sequences 9.2 The Linear Growth Model 9.3 The Exponential Growth Model 9.4 The Logistic Growth Model
 10 Financial Mathematics 10.1 Percentages 10.2 Simple Interest 10.3 Compound Interest 10.4 Retirement Savings 10.5 Consumer Debt
 11 The Mathematics of Symmetry 11.1 Rigid Motions 11.2 Reflections 11.3 Rotations 11.4 Translations 11.5 Glide Reflections 11.6 Symmetries and Symmetry Types 11.7 Patterns
 12 Fractal Geometry 12.1 The Koch Snowflake and SelfSimilarity 12.2 The Sierpinski Gasket and the Chaos Game 12.3 The Twisted Sierpinski Gasket 12.4 The Mandelbrot Set
 13 Fibonacci Numbers and the Golden Ratio 13.1 Fibonacci Numbers 13.2 The Golden Ratio 13.3 Gnomons 13.4 Spiral Growth in Nature
 14 Censuses, Surveys, Polls, and Studies 14.1 Enumeration 14.2 Measurement 14.3 Cause and Effect
 15 Graphs, Charts, and Numbers 15.1 Graphs and Charts 15.2 Means, Medians, and Percentiles 15.3 Ranges and Standard Deviations
 16 Probabilities, Odds, and Expectations 16.1 Sample Spaces and Events 16.2 The Multiplication Rule, Permutations, and Combinations 16.3 Probabilities and Odds 16.4 Expectations 16.5 Measuring Risk
 17 The Mathematics of Normality 17.1 Approximately Normal Data Sets 17.2 Normal Curves and Normal Distributions 17.3 Modeling Approximately Normal Distributions 17.4 Normality in Random Events Answers to Selected Exercises Index Photo Credits.
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QA36 .T35 2018  Unknown 
 Pruim, Randall J., author.
 Second edition.  Providence, Rhode Island : American Mathematical Society, [2018]
 Description
 Book — xx, 820 pages ; 27 cm.
 Summary

 Data Probability and random variables Continuous distributions Parameter estimation and testing Likelihood Introduction to linear models More linear models A brief introduction to $\mathsf{R}$ Some mathematical preliminaries Geometry and linear algebra review Hints, answers, and solutions to selected exercises Bibliography Index to $\mathsf{R}$ functions, packages, and data sets Index.
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QA276.45 .R3 P78 2018  Unknown 
 Larsen, Richard J., author.
 Sixth edition.  [Upper Saddle River, NJ] : Pearson, [2018]
 Description
 Book — x, 742 pages : illustrations ; 26 cm
 Summary

 * Introduction * Probability * Random Variables * Special Distributions * Estimation * Hypothesis Testing * Inferences Based on the Normal Distribution * Types of Data: A Brief Overview * TwoSample Inferences * GoodnessofFit Tests * Regression * The Analysis of Variance * Randomized Block Designs * Nonparametric Statistics.
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QA276 .L314 2018  Unknown 
 Hedberg, E. C. (Eric Christopher), 1978 author.
 Thousand Oaks, California : Sage Publications, Inc., [2018]
 Description
 Book — xvii, 138 pages ; 22 cm.
 Summary

 1. The what, why, and when of power analysis
 2. Statistical distribution
 3. General topics in hypothesis testing and power analysis when the population standard deviation is known : the case of two group means
 4. The difference between two groups in simple random samples where the population standard deviation must be estimated
 5. Using covariates when testing the difference in sample group means for balanced design
 6. Multilevel models I : testing the difference in group means in twolevel cluster randomized trials
 7. Multilevel models II : testing the difference in group means in twolevel multisite randomized trials
 8. Reasonable assumptions
 9. Writing about power
 10. Conclusions, further reading, and regression.
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QA277 .H43 2018  Unknown 
 Harville, David A., author.
 Boca Raton, FL : CRC Press, Taylor & Francis Group, [2018]
 Description
 Book — xiii, 524 pages : illustrations ; 27 cm.
 Summary

 Preface
 1 Introduction
 Linear Statistical Models
 Regression Models
 Classificatory Models
 Hierarchical Models and RandomEffectsModels
 Statistical Inference
 An Overview
 2 Matrix Algebra: a Primer
 The Basics
 Partitioned Matrices and Vectors
 Trace of a (Square) Matrix
 Linear Spaces
 Inverse Matrices
 Ranks and Inverses of Partitioned Matrices
 OrthogonalMatrices
 IdempotentMatrices
 Linear Systems
 Generalized Inverses
 Linear Systems Revisited
 Projection Matrices
 Quadratic Forms
 Determinants
 Exercises
 Bibliographic and Supplementary Notes
 3 Random Vectors and Matrices
 Expected Values
 Variances, Covariances, and Correlations
 Standardized Version of a Random Variable
 Conditional Expected Values and Conditional Variances and Covariances
 Multivariate Normal Distribution
 Exercises
 Bibliographic and Supplementary Notes
 4 The General Linear Model
 Some Basic Types of Linear Models
 Some Specific Types of GaussMarkov Models (With Examples)
 Regression
 Heteroscedastic and Correlated Residual Effects
 Multivariate Data
 vi Contents
 Exercises
 Bibliographic and Supplementary Notes
 5 Estimation and Prediction: Classical Approach
 Linearity and Unbiasedness
 Translation Equivariance
 Estimability
 The Method of Least Squares
 Best LinearUnbiased or TranslationEquivariantEstimation of Estimable Functions
 (Under the GM Model)
 Simultaneous Estimation
 Estimation of Variability and Covariability
 Best (MinimumVariance) Unbiased Estimation
 LikelihoodBased Methods
 Prediction
 Exercises
 Bibliographic and Supplementary Notes
 6 Some Relevant Distributions and Their Properties
 ChiSquare, Gamma, Beta, and Dirichlet Distributions
 Noncentral ChiSquare Distribution
 Central and Noncentral F Distributions
 Central, Noncentral, and Multivariate t Distributions
 Moment Generating Function of the Distribution of One or More Quadratic Forms
 or SecondDegree Polynomials (in a Normally Distributed Random Vector)
 Distribution of Quadratic Forms or SecondDegree Polynomials (in a Normally
 Distributed Random Vector): ChiSquareness
 The Spectral Decomposition, With Application to the Distribution of Quadratic
 Forms
 More on the Distribution of Quadratic Forms or SecondDegree Polynomials (in a
 Normally Distributed Random Vector)
 Exercises
 Bibliographic and Supplementary Notes
 7 Confidence Intervals (or Sets) and Tests of Hypotheses
 "Setting the Stage": Response Surfaces in the Context of a Specific Application and
 in General
 Augmented GM Model
 The F Test (and Corresponding Confidence Set) and the S Method
 Some Optimality Properties
 OneSided t Tests and the Corresponding Confidence Bounds
 The Residual Variance : Confidence Intervals and Tests
 Multiple Comparisons and Simultaneous Confidence Intervals: Some Enhancements
 Prediction
 Exercises
 Bibliographic and Supplementary Notes
 References
 Index.
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QA188 .H3798 2018  Unknown 
 Baldi, Brigitte, author.
 Fourth edition.  New York, NY : W.H. Freeman, Macmillan Learning, [2018]
 Description
 Book — xxix, 729 pages : color illustrations, color map ; 29 cm
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QH323.5 .B35 2018  Unknown 
16. Schaum's outlines. Statistics [2018]
 Spiegel, Murray R., author.
 Sixth edition.  New York : McGrawHill Education, [2018]
 Description
 Book — xx, 579 pages : illustrations ; 28 cm.
 Summary

Tough Test Questions? Missed Lectures? Not Enough Time? Textbook too pricey? Fortunately, there's Schaum's. This allinonepackage includes more than 500 fullysolved problems, examples, and practice exercises to sharpen your problemsolving skills. Plus, you will have access to 25 detailed videos featuring math instructors who explain how to solve the most commonly tested problemsit's just like having your own virtual tutor! You'll find everything you need to build confidence, skills, and knowledge for the highest score possible. More than 40 million students have trusted Schaum's to help them succeed in the classroom and on exams. Schaum's is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easytofollow, topicbytopic format. Helpful tables and illustrations increase your understanding of the subject at hand. Schaum's Outline of Geometry, Sixth Edition features: * Over 500 problems, solved step by step * Updated content to match the latest curriculum * An accessible format for quick and easy review * Clear explanations for key concepts * Access to revised Schaums.com website and new app with access to 25 problemsolving videos, and more
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QA276.19 .S652 2018  Unknown 
 Mayo, Deborah G., author.
 Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2018.
 Description
 Book — xvi, 486 pages ; 23 cm
 Summary

 Preface Excursion
 1. How to Tell What's True about Statistical Inference: Tour I. Beyond probabilism and performance Tour II. Error probing tools vs. logics of evidence Excursion
 2. Taboos of Induction and Falsification: Tour I. Induction and confirmation Tour II. Falsification, pseudoscience, induction Excursion
 3. Statistical Tests and Scientific Inference: Tour I. Ingenious and severe tests Tour II. It's the methods, stupid Tour III. Capability and severity: deeper concepts Excursion
 4. Objectivity and Auditing: Tour I. The myth of 'the myth of objectivity' Tour II. Rejection fallacies: whose exaggerating what? Tour III. Auditing: biasing selection effects and randomization Tour IV. More auditing: objectivity and model checking Excursion
 5. Power and Severity: Tour I. Power: predata and postdata Tour II. How not to corrupt power Tour III. Deconstructing the NP vs. Fisher debates Excursion
 6. (Probabilist) Foundations Lost, (Probative) Foundations Found: Tour I. What ever happened to Bayesian foundations? Tour II. Pragmatic and error statistical Bayesians Souvenir (Z) farewell References Index.
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QA276 .M3755 2018  Unknown 
 Keen, K. J., author.
 Second edition.  Boca Raton, FL : CRC Press, Taylor & Francis Group, [2018]
 Description
 Book — xx, 590 pages ; 24 cm.
 Summary

 Introduction
 The graphical display of information
 Basic charts for the distribution of a single discrete variable
 Advanced charts for the distribution of a single discrete variable
 Exploratory plots for the distribution of a single continuous variable
 Diagnostic plots for the distribution of a continuous variable
 Nonparametric density estimation for a single continuous variable
 Parametric density estimation for a single continuous variable
 Depicting the distribution of two discrete variables
 Depicting the distribution of one continuous variable and one discrete variable
 Depicting the distribution of two continuous variables
 Simple linear regression: graphical displays
 Polynomial regression and data smoothing : graphical displays
 Visualizing multivariate data.
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QA276.45 .R3 K44 2018  Unknown 
19. Analysis of repeated measures data [2017]
 Islam, M. Ataharul, 1976 author.
 Singapore : Springer, [2017]
 Description
 Book — xix, 250 pages : illustrations ; 24 cm
 Summary

 Introduction. Linear Models. Univariate Exponential Family of Distributions. Generalized Linear Model. Covariate Dependent Markov Models. Model for Bivariate Binary Data. Model for Bivariate Geometric Model. Model for Bivariate Count Data. Models for Bivariate Exponential and Weibull Data. Quasi Likelihood Methods. Generalized Estimating Equations. A Generalized Multivariate Model. Multistate and Multistage Models. Analysing Data Using R and SAS.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA276 .I85 2017  Unknown 
20. Applied multivariate statistical concepts [2017]
 HahsVaughn, Debbie L. author.
 New York : Routledge, an imprint of the Taylor & Francis Group, 2017.
 Description
 Book — xii, 647 pages : illustrations ; 26 cm
 Summary

 1. Multivariate Statistics
 2. Univariate and Bivariate Statistics Review
 3. Data Screening
 4. Multiple Linear Regression
 5. Logistic Regression 6.Multivariate Analysis of Variance: Single Factor, Factorial, and Repeated Measures Designs
 7. Discriminant Analysis
 8. Cluster Analysis
 9. Exploratory Factor Analysis
 10. Path Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling
 11. Multilevel Linear Modeling
 12. Propensity Score Analysis.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA278 .H336 2017  Unknown 