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 Comincioli, V. (Valeriano)
 Milano : Ambrosiana, c1993.
 Description
 Book — xii, 489 p. ; 24 cm.
 Online
SAL3 (offcampus storage)
SAL3 (offcampus storage)  Status 

Stacks  Request 
QA401 .C649 1993  Available 
2. Simulating data with SAS [2013]
 Wicklin, Rick.
 Cary, N.C. : SAS Institute, ©2013.
 Description
 Book — 1 online resource
 Summary

 Introduction to simulation
 Simulating data from common univariate distributions
 Preliminary and background information
 Simulating data to estimate sampling distributions
 Using simulation to evaluate statistical techniques
 Strategies for efficient and effective simulation
 Advanced simulation of univariate data
 Simulating data from basic multivariate distributions
 Advanced simulation of multivariate data
 Building correlation and covariance matrices
 Simulating data for basic regression models
 Simulating data for advanced regression models
 Simulating data from times series models
 Simulating data from spatial models
 Resampling and bootstrap methods
 Moment matching and the momentratio diagram.
3. Simulating data with SAS [2013]
 Wicklin, Rick.
 Cary, N.C. : SAS Institute, ©2013.
 Description
 Book — 1 online resource
 Summary

 Introduction to simulation
 Simulating data from common univariate distributions
 Preliminary and background information
 Simulating data to estimate sampling distributions
 Using simulation to evaluate statistical techniques
 Strategies for efficient and effective simulation
 Advanced simulation of univariate data
 Simulating data from basic multivariate distributions
 Advanced simulation of multivariate data
 Building correlation and covariance matrices
 Simulating data for basic regression models
 Simulating data for advanced regression models
 Simulating data from times series models
 Simulating data from spatial models
 Resampling and bootstrap methods
 Moment matching and the momentratio diagram.
 Ahsanullah, Mohammad
 Amsterdam : Atlantis Press, c2013.
 Description
 Book — 1 online resource.
 Online

 dx.doi.org SpringerLink
 Google Books (Full view)
 Milan ; New York : Springer ; Heidelberg : PhysicaVerlag, c2013.
 Description
 Book — 1 online resource.
 Summary

 A New Unsupervised Classification Technique Through Nonlinear Non Parametric MixedEffects Models / Laura Azzimonti, Francesca Ieva
 Estimation Approaches for the Apparent Diffusion Coefficient in RiceDistributed MR Signals / Stefano Baraldo, Francesca Ieva
 Longitudinal Patterns of Financial Product Ownership: A Latent Growth Mixture Approach / Francesca Bassi, José G. Dias
 Computationally Efficient Inference Procedures for Vast Dimensional Realized Covariance Models / Luc Bauwens, Giuseppe Storti
 A GPU Software Library for LikelihoodBased Inference of Environmental Models with Large Datasets / Michela Cameletti, Francesco Finazzi
 Theoretical Regression Trees: A Tool for Multiple StructuralChange Models Analysis / Carmela Cappelli, Francesca Di Iorio
 Some Contributions to the Theory of Conditional Gibbs Partitions / Annalisa Cerquetti
 Estimation of Traffic Matrices for LRD Traffic / Pier Luigi Conti, Livia De Giovanni
 A Newton's Method for Benchmarking Time Series / Tommaso Di Fonzo, Marco Marini
 Spatial Smoothing for Data Distributed over Nonplanar Domains / Bree Ettinger, Tiziano Passerini
 Volatility Swings in the US Financial Markets / Giampiero M. Gallo, Edoardo Otranto
 Semicontinuous Regression Models with Skew Distributions / Anna Gottard, Elena Stanghellini
 Classification of Multivariate LinearCircular Data with Nonignorable Missing Values / Francesco Lagona, Marco Picone
 Multidimensional Connected Set Detection in Clustering Based on Nonparametric Density Estimation / Giovanna Menardi
 Using Integrated Nested Laplace Approximations for Modelling Spatial Healthcare Utilization / Monica Musio, ErikA. Sauleau
 Supply Function Prediction in Electricity Auctions / Matteo Pelagatti
 A Hierarchical Bayesian Model for RNASeq Data / Davide Risso, Gabriele Sales.
 Online

 dx.doi.org SpringerLink
 Google Books (Full view)
 Cleophas, Ton J.
 Dordrecht ; New York : Springer, c2012.
 Description
 Book — 1 online resource.
 Online

 dx.doi.org SpringerLink
 Google Books (Full view)
 Lantz, Brett, author.
 Birmingham, UK : Packt Publishing, 2013.
 Description
 Book — 1 online resource (vii, 375 pages) : illustrations.
 Summary

 Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface;
 Chapter 1: Introducing Machine Learning; The origins of machine learning; Uses and abuses of machine learning; Ethical considerations; How do machines learn?; Abstraction and knowledge representation; Generalization; Assessing the success of learning; Steps to apply machine learning to your data; Choosing a machine learning algorithm; Thinking about the input data; Thinking about types of machine learning algorithms; Matching your data to an appropriate algorithm.
 Using R for machine learningInstalling and loading R packages; Installing an R package; Installing a package using the pointandclick interface; Loading an R package; Summary;
 Chapter 2: Managing and Understanding Data; R data structures; Vectors; Factors; Lists; Data frames; Matrixes and arrays; Managing data with R; Saving and loading R data structures; Importing and saving data from CSV files; Importing data from SQL databases; Exploring and understanding data; Exploring the structure of data; Exploring numeric variables; Measuring the central tendency
 mean and median.
 Measuring spread
 quartiles and the fivenumber summaryVisualizing numeric variables
 boxplots; Visualizing numeric variables
 histograms; Understanding numeric data
 uniform and normal distributions; Measuring spread
 variance and standard deviation; Exploring categorical variables; Measuring the central tendency
 the mode; Exploring relationships between variables; Visualizing relationships
 scatterplots; Examining relationships
 twoway crosstabulations; Summary;
 Chapter 3: Lazy Learning
 Classification using Nearest Neighbors; Understanding classification using nearest neighbors.
 The kNN algorithmCalculating distance; Choosing an appropriate k; Preparing data for use with kNN; Why is the kNN algorithm lazy?; Diagnosing breast cancer with the kNN algorithm; Step
 1
 collecting data; Step
 2
 exploring and preparing the data; Transformation
 normalizing numeric data; Data preparation
 creating training and test datasets; Step
 3
 training a model on the data; Step
 4
 evaluating model performance; Step
 5
 improving model performance; Transformation
 zscore standardization; Testing alternative values of k; Summary.
 Chapter 4: Probabilistic Learning
 Classification using Naive BayesUnderstanding naive Bayes; Basic concepts of Bayesian methods; Probability; Joint probability; Conditional probability with Bayes' theorem; The naive Bayes algorithm; The naive Bayes classification; The Laplace estimator; Using numeric features with naive Bayes; Example
 filtering mobile phone spam with the naive Bayes algorithm; Step
 1
 collecting data; Step
 2
 exploring and preparing the data; Data preparation
 processing text data for analysis; Data preparation
 creating training and test datasets.
 Visualizing text data
 word clouds.
(source: Nielsen Book Data)
 Albalate, Amparo.
 London : ISTE ; Hoboken, NJ : Wiley, 2011.
 Description
 Book — 1 online resource (x, 244 pages) : illustrations
 Summary

 Machine generated contents note: pt. 1 State of the Art
 ch. 1 Introduction
 1.1. Organization of the book
 1.2. Utterance corpus
 1.3. Datasets from the UCI repository
 1.3.1. Wine dataset (wine)
 1.3.2. Wisconsin breast cancer dataset (breast)
 1.3.3. Handwritten digits dataset (Pendig)
 1.3.4. Pima Indians diabetes (diabetes)
 1.3.5. Iris dataset (Iris)
 1.4. Microarray dataset
 1.5. Simulated datasets
 1.5.1. Mixtures of Gaussians
 1.5.2. Spatial datasets with nonhomogeneous intercluster distance
 ch. 2 State of the Art in Clustering and SemiSupervised Techniques
 2.1. Introduction
 2.2. Unsupervised machine learning (clustering)
 2.3. A brief history of cluster analysis
 2.4. Cluster algorithms
 2.4.1. Hierarchical algorithms
 2.4.1.1. Agglomerative clustering
 2.4.1.2. Divisive algorithms
 2.4.2. Modelbased clustering
 2.4.2.1. The expectation maximization (EM) algorithm
 2.4.3. Partitional competitive models.
 2.4.3.1. Kmeans
 2.4.3.2. Neural gas
 2.4.3.3. Partitioning around Medoids (PAM)
 2.4.3.4. Selforganizing maps
 2.4.4. Densitybased clustering
 2.4.4.1. Direct density reachability
 2.4.4.2. Density reachability
 2.4.4.3. Density connection
 2.4.4.4. Border points
 2.4.4.5. Noise points
 2.4.4.6. DBSCAN algorithm
 2.4.5. Graphbased clustering
 2.4.5.1. Polebased overlapping clustering
 2.4.6. Affectation stage
 2.4.6.1. Advantages and drawbacks
 2.5. Applications of cluster analysis
 2.5.1. Image segmentation
 2.5.2. Molecular biology
 2.5.2.1. Biological considerations
 2.5.3. Information retrieval and document clustering
 2.5.3.1. Document preprocessing
 2.5.3.2. Boolean model representation
 2.5.3.3. Vector space model
 2.5.3.4. Term weighting
 2.5.3.5. Probabilistic models
 2.5.4. Clustering documents in information retrieval
 2.5.4.1. Clustering of presented results
 2.5.4.2. Postretrieval document browsing (ScatterGather)
 2.6. Evaluation methods.
 2.7. Internal cluster evaluation
 2.7.1. Entropy
 2.7.2. Purity
 2.7.3. Normalized mutual information
 2.8. External cluster validation
 2.8.1. Hartigan
 2.8.2. Davies Bouldin index
 2.8.3. Krzanowski and Lai index
 2.8.4. Silhouette
 2.8.5. Gap statistic
 2.9. Semisupervised learning
 2.9.1. Self training
 2.9.2. Cotraining
 2.9.3. Generative models
 2.10. Summary
 pt. 2 Approaches to SemiSupervised Classification
 ch. 3 SemiSupervised Classification Using Prior Word Clustering
 3.1. Introduction
 3.2. Dataset
 3.3. Utterance classification scheme
 3.3.1. Preprocessing
 3.3.1.1. Utterance vector representation
 3.3.2. Utterance classification
 3.4. Semisupervised approach based on term clustering
 3.4.1. Term clustering
 3.4.2. Semantic term dissimilarity
 3.4.2.1. Term vector of lexical cooccurrences
 3.4.2.2. Metric of dissimilarity
 3.4.3. Term vector truncation
 3.4.4. Term clustering
 3.4.5. Feature extraction and utterance feature vector.
 3.4.6. Evaluation
 3.5. Disambiguation
 3.5.1. Evaluation
 3.6. Summary
 ch. 4 SemiSupervised Classification Using Pattern Clustering
 4.1. Introduction
 4.2. New semisupervised algorithm using the cluster and label strategy
 4.2.1. Block diagram
 4.2.1.1. Dataset
 4.2.1.2. Clustering
 4.2.1.3. Optimum cluster labeling
 4.2.1.4. Classification
 4.3. Optimum cluster labeling
 4.3.1. Problem definition
 4.3.2. The Hungarian algorithm
 4.3.2.1. Weighted complete bipartite graph
 4.3.2.2. Matching, perfect matching and maximum weight matching
 4.3.2.3. Objective of Hungarian method
 4.3.2.4. Complexity considerations
 4.3.3. Genetic algorithms
 4.3.3.1. Reproduction operators
 4.3.3.2. Forming the next generation
 4.3.3.3. GAs applied to optimum cluster labeling
 4.3.3.4. Comparison of methods
 4.4. Supervised classification block
 4.4.1. Support vector machines
 4.4.1.1. The kernel trick for nonlinearly separable classes
 4.4.1.2. Multiclass classification
 4.4.2. Example.
 4.5. Datasets
 4.5.1. Mixtures of Gaussians
 4.5.2. Datasets from the UCI repository
 4.5.2.1. Iris dataset (Iris)
 4.5.2.2. Wine dataset (wine)
 4.5.2.3. Wisconsin breast cancer dataset (breast)
 4.5.2.4. Handwritten digits dataset (Pendig)
 4.5.2.5. Pima Indians diabetes (diabetes)
 4.5.3. Utterance dataset
 4.6. An analysis of the bounds for the cluster and label approaches
 4.7. Extension through cluster pruning
 4.7.1. Determination of silhouette thresholds
 4.7.2. Evaluation of the cluster pruning approach
 4.8. Simulations and results
 4.9. Summary
 pt. 3 Contributions to Unsupervised Classification  Algorithms to Detect the Optimal Number of Clusters
 ch. 5 Detection of the Number of Clusters through NonParametric Clustering Algorithms
 5.1. Introduction
 5.2. New hierarchical polebased clustering algorithm
 5.2.1. Polebased clustering basis module
 5.2.2. Hierarchical polebased clustering
 5.3. Evaluation
 5.3.1. Cluster evaluation metrics
 5.4. Datasets.
 5.4.1. Results
 5.4.2. Complexity considerations for large databases
 5.5. Summary
 ch. 6 Detecting the Number of Clusters through Cluster Validation
 6.1. Introduction
 6.2. Cluster validation methods
 6.2.1. Dunn index
 6.2.2. Hartigan
 6.2.3. Davies Bouldin index
 6.2.4. Krzanowski and Lai index
 6.2.5. Silhouette
 6.2.6. Hubert's & gamma;
 6.2.7. Gap statistic
 6.3. Combination approach based on quantiles
 6.4. Datasets
 6.4.1. Mixtures of Gaussians
 6.4.2. Cancer DNAmicroarray dataset
 6.4.3. Iris dataset
 6.5. Results
 6.5.1. Validation results of the five Gaussian dataset
 6.5.2. Validation results of the mixture of seven Gaussians
 6.5.3. Validation results of the NCI60 dataset
 6.5.4. Validation results of the Iris dataset
 6.5.5. Discussion
 6.6. Application of speech utterances
 6.7. Summary.
9. Measuring the software process : statistical process control for software process improvement [1999]
 Florac, William A.
 Reading, Mass. : AddisonWesley, [1999]
 Description
 Book — 1 online resource (xxii, 250 pages) : illustrations.
 Summary

 Ch. 1. Managing and Measuring Process Behavior
 Ch. 2. Planning for Measurement
 Ch. 3. Collecting the Data
 Ch. 4. Analyzing Process Behavior
 Ch. 5. Process Behavior Charts for Software Processes
 Ch. 6. More About Process Behavior Charts
 Ch. 7. Three Paths to Process Improvement
 Ch. 8. Getting Started
 App. A. Control Chart Tables and Formulas
 App. B. More About Analyzing Process Behavior
 App. C. Example Data and Calculations.
(source: Nielsen Book Data)
 Berlin ; New York : Springer, c2013.
 Description
 Book — 1 online resource.
 Online

 dx.doi.org SpringerLink
 Google Books (Full view)
11. Inference for diffusion processes [electronic resource] : with applications in life sciences [2013]
 Fuchs, Christiane.
 Berlin ; New York : Springer, c2013.
 Description
 Book — 1 online resource.
 Summary

 Stochastic Modelling
 Stochastic Modelling in Life Sciences
 Stochastic Differential Equations and Diffusions in a Nutshell
 Approximation of Markov Jump Processes by Diffusions
 Diffusion Models in Life Sciences
 Statistical Inference
 Parametric Inference for DiscretelyObserved Diffusions
 Bayesian Inference for Diffusions with LowFrequency Observations
 Applications
 Application I: Spread of Influenza
 Application II: Analysis of Molecular Binding
 Summary and Future Work.
 Online

 dx.doi.org SpringerLink
 Google Books (Full view)
12. Practical data analysis with JMP [2014]
 Carver, Robert H.
 2nd ed.  Cary, NC : SAS Institute, ©2014.
 Description
 Book — 1 online resource (xxiv, 460 pages .) : illustrations
 Summary

This book uses the powerful interactive and visual approach of JMP to introduce readers to the logic and methods of statistical thinking and data analysis. It enables you to discriminate among and to use fundamental techniques of analysis, enabling you to engage in statistical thinking by analyzing realworld problems. "Application Scenarios" at the end of each chapter challenge you to put your knowledge and skills to use with data sets that go beyond mere repetition of chapter examples, and three new review chapters help readers integrate ideas and techniques. In addition, the scope and sequence of the chapters have been updated with more coverage of data management and analysis of data. The book can stand on its own as a learning resource for professionals or be used to supplement a standard collegelevel introductiontostatistics textbook. Reflective of the broad applicability of statistical reasoning, the problems included inthe book come from a wide variety of disciplines, including engineering, life sciences, business, economics, among others, and include a number of international and historical examples.  Edited summary from back cover.
 Yau, Nathan.
 Indianapolis, Ind. : Wiley Pub., ©2011.
 Description
 Book — 1 online resource (xxvi, 358 pages) : color illustrations, color maps
 Summary

 Introduction xv
 1 Telling Stories with Data
 1 More Than Numbers
 2 What to Look For
 8 Design
 13 Wrapping Up
 20
 2 Handling Data
 21 Gather Data
 22 Formatting Data
 38 Wrapping Up
 52
 3 Choosing Tools to Visualize Data
 53 OutoftheBox Visualization
 54 Programming
 62 Illustration
 76 Mapping
 80 Survey Your Options
 88 Wrapping Up
 89
 4 Visualizing Patterns over Time
 91 What to Look for over Time
 92 Discrete Points in Time
 93 Continuous Data
 118 Wrapping Up
 132
 5 Visualizing Proportions
 135 What to Look for in Proportions
 136 Parts of a Whole
 136 Proportions over Time
 161 Wrapping Up
 178
 6 Visualizing Relationships
 179 What Relationships to Look For
 180 Correlation
 180 Distribution
 200 Comparison
 213 Wrapping Up
 226
 7 Spotting Differences
 227 What to Look For
 228 Comparing across Multiple Variables
 228 Reducing Dimensions
 258 Searching for Outliers
 265 Wrapping Up
 269
 8 Visualizing Spatial Relationships
 271 What to Look For
 272 Specific Locations
 272 Regions
 285 Over Space and Time
 302 Wrapping Up
 325
 9 Designing with a Purpose
 327 Prepare Yourself
 328 Prepare Your Readers
 330 Visual Cues
 334 Good Visualization
 340 Wrapping Up
 341 Index 343.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Lander, Jared P.
 Upper Saddle River, NJ : AddisonWesley, ©2014.
 Description
 Book — 1 online resource (1 volume) : illustrations.
 Summary

 Foreword xiii Preface xv Acknowledgments xix About the Author xxi
 Chapter 1: Getting R 11.1 Downloading R
 1 1.2 R Version
 2 1.3 32bit vs. 64bit
 2 1.4 Installing
 2 1.5 Revolution R Community Edition
 10 1.6 Conclusion
 11
 Chapter 2: The R Environment
 13 2.1 Command Line Interface
 14 2.2 RStudio
 15 2.3 Revolution Analytics RPE
 26 2.4 Conclusion
 27
 Chapter 3: R Packages
 29 3.1 Installing Packages
 29 3.2 Loading Packages
 32 3.3 Building a Package
 33 3.4 Conclusion
 33
 Chapter 4: Basics of R
 35 4.1 Basic Math
 35 4.2 Variables
 36 4.3 Data Types
 38 4.4 Vectors
 43 4.5 Calling Functions
 49 4.6 Function Documentation
 49 4.7 Missing Data
 50 4.8 Conclusion
 51
 Chapter 5: Advanced Data Structures
 53 5.1 data.frames
 53 5.2 Lists
 61 5.3 Matrices
 68 5.4 Arrays
 71 5.5 Conclusion
 72
 Chapter 6: Reading Data into R
 73 6.1 Reading CSVs
 73 6.2 Excel Data
 74 6.3 Reading from Databases
 75 6.4 Data from Other Statistical Tools
 77 6.5 R Binary Files
 77 6.6 Data Included with R
 79 6.7 Extract Data from Web Sites
 80 6.8 Conclusion
 81
 Chapter 7: Statistical Graphics
 83 7.1 Base Graphics
 83 7.2 ggplot2
 86 7.3 Conclusion
 98
 Chapter 8: Writing R Functions
 99 8.1 Hello, World!
 99 8.2 Function Arguments
 100 8.3 Return Values
 103 8.4 do.call
 104 8.5 Conclusion
 104
 Chapter 9: Control Statements
 105 9.1 if and else
 105 9.2 switch
 108 9.3 ifelse
 109 9.4 Compound Tests
 111 9.5 Conclusion
 112
 Chapter 10: Loops, the UnR Way to Iterate
 113 10.1 for Loops
 113 10.2 while Loops
 115 10.3 Controlling Loops
 115 10.4 Conclusion
 116
 Chapter 11: Group Manipulation
 117 11.1 Apply Family
 117 11.2 aggregate
 120 11.3 plyr
 124 11.4 data.table
 129 11.5 Conclusion
 139
 Chapter 12: Data Reshaping
 141 12.1 cbind and rbind
 141 12.2 Joins
 142 12.3 reshape2
 149 12.4 Conclusion
 153
 Chapter 13: Manipulating Strings
 155 13.1 paste
 155 13.2 sprintf
 156 13.3 Extracting Text
 157 13.4 Regular Expressions
 161 13.5 Conclusion
 169
 Chapter 14: Probability Distributions
 171 14.1 Normal Distribution
 171 14.2 Binomial Distribution
 176 14.3 Poisson Distribution
 182 14.4 Other Distributions
 185 14.5 Conclusion
 186
 Chapter 15: Basic Statistics
 187 15.1 Summary Statistics
 187 15.2 Correlation and Covariance
 191 15.3 TTests
 200 15.4 ANOVA
 207 15.5 Conclusion
 210
 Chapter 16: Linear Models
 211 16.1 Simple Linear Regression
 211 16.2 Multiple Regression
 216 16.3 Conclusion
 232
 Chapter 17: Generalized Linear Models
 233 17.1 Logistic Regression
 233 17.2 Poisson Regression
 237 17.3 Other Generalized Linear Models
 240 17.4 Survival Analysis
 240 17.5 Conclusion
 245
 Chapter 18: Model Diagnostics
 247 18.1 Residuals
 247 18.2 Comparing Models
 253 18.3 CrossValidation
 257 18.4 Bootstrap
 262 18.5 Stepwise Variable Selection
 265 18.6 Conclusion
 269
 Chapter 19: Regularization and Shrinkage
 271 19.1 Elastic Net
 271 19.2 Bayesian Shrinkage
 290 19.3 Conclusion
 295
 Chapter 20: Nonlinear Models
 297 20.1 Nonlinear Least Squares
 297 20.2 Splines
 300 20.3 Generalized Additive Models
 304 20.4 Decision Trees
 310 20.5 Random Forests
 312 20.6 Conclusion
 313
 Chapter 21: Time Series and Autocorrelation
 315 21.1 Autoregressive Moving Average
 315 21.2 VAR
 322 21.3 GARCH
 327 21.4 Conclusion
 336
 Chapter 22: Clustering
 337 22.1 Kmeans
 337 22.2 PAM
 345 22.3 Hierarchical Clustering
 352 22.4 Conclusion
 357
 Chapter 23: Reproducibility, Reports and Slide Shows with knitr
 359 23.1 Installing a LATEX Program
 359 23.2 LATEX Primer
 360 23.3 Using knitr with LATEX
 362 23.4 Markdown Tips
 367 23.5 Using knitr and Markdown
 368 23.6 pandoc
 369 23.7 Conclusion
 371
 Chapter 24: Building R Packages
 373 24.1 Folder Structure
 373 24.2 Package Files
 373 24.3 Package Documentation
 380 24.4 Checking, Building and Installing
 383 24.5 Submitting to CRAN
 384 24.6 C++ Code
 384 24.7 Conclusion
 390
 Appendix A: RealLife Resources
 391 A.1 Meetups
 391 A.2 Stackoverflow
 392 A.3 Twitter
 393 A.4 Conferences
 393 A.5 Web Sites
 393 A.6 Documents
 394 A.7 Books
 394 A.8 Conclusion
 394
 Appendix B: Glossary
 395
 List of Figures
 409 List of Tables
 417 General Index
 419 Index of Functions
 429 Index of Packages
 433 Index of People
 435
 Data Index 437.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
15. R for data science [2014]
 Toomey, Dan, author.
 Birmingham, UK : Packt Publishing, 2014.
 Description
 Book — 1 online resource (1 volume) : illustrations. Digital: text file.
 Summary

 Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface;
 Chapter 1: Data Mining Patterns; Cluster analysis; Kmeans clustering; Usage; Example; Kmedoids clustering; Usage; Example; Hierarchical clustering; Usage; Example; Expectationmaximization; Usage; List of model names; Example; Density estimation; Usage; Example; Anomaly detection; Show outliers; Example; Example; Another anomaly detection example; Calculating anomalies; Usage; Example 1; Example 2; Association rules; Mine for associations; Usage; Example; Questions; Summary.
 Chapter 2: Data Mining SequencesPatterns; Eclat; Usage; Using eclat to find similarities in adult behavior; Finding frequent items in a dataset; An example focusing on highest frequency; arulesNBMiner; Usage; Mining the Agrawal data for frequent sets; Apriori; Usage; Evaluating associations in a shopping basket; Determining sequences using TraMineR; Usage; Determining sequences in training and careers; Similarities in the sequence; Sequence metrics; Usage; Example; Questions; Summary;
 Chapter 3: Text Mining; Packages; Text processing; Example; Creating a corpus; Text clusters; Word graphics.
 Analyzing the XML textQuestions; Summary;
 Chapter 4: Data Analysis
 Regression Analysis; Packages; Simple regression; Multiple regression; Multivariate regression analysis; Robust regression; Questions; Summary;
 Chapter 5: Data Analysis
 Correlation; Packages; Correlation; Example; Visualizing correlations; Covariance; Pearson correlation; Polychoric correlation; Tetrachoric correlation; A heterogeneous correlation matrix; Partial correlation; Questions; Summary;
 Chapter 6: Data Analysis
 Clustering; Packages; Kmeans clustering; Example; Optimal number of clusters; Medoids clusters.
 The cascadeKM functionSelecting clusters based on Bayesian information; Affinity propagation clustering; Gap statistic to estimate the number of clusters; Hierarchical clustering; Questions; Summary;
 Chapter 7: Data Visualization
 R Graphics; Packages; Interactive graphics; The latticist package; Bivariate binning display; Mapping; Plotting points on a map; Plotting points on a world map; Google Maps; The ggplot2 package; Questions; Summary;
 Chapter 8: Data Visualization
 Plotting; Packages; Scatter plots; Regression line; A lowess line; scatterplot; Scatterplot matrices.
 Splom
 display matrix datacpairs
 plot matrix data; Density scatter plots; Bar charts and plots; Bar plot; Usage; Bar chart; ggplot2; Word cloud; Questions; Summary;
 Chapter 9: Data Visualization
 3D; Packages; Generating 3D graphics; Lattice Cloud
 3D scatterplot; scatterplot3d; scatter3d; cloud3d; RgoogleMaps; vrmlgenbar3D; Big Data; pbdR; bigmemory; Research areas; Rcpp; parallel; microbenchmark; pqR; SAP integration; roxygen2; bioconductor; swirl; pipes; Questions; Summary;
 Chapter 10: Machine Learning in Action; Packages; Dataset; Data partitioning; Model; Linear model; Prediction.
(source: Nielsen Book Data)
 Warne, Russell T., 1983 author.
 Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2018.
 Description
 Book — xviii, 579 pages ; 26 cm
 Summary

 Preface Acknowledgements List of examples
 1. Statistics and models
 2. Levels of data
 3. Visual models
 4. Central tendency and variability
 5. Linear transformations and zscores
 6. Probability and CLT
 7. NHSST and ztests
 8. Onesample ttests
 9. Paired samples ttests
 10. Unpaired twosample ttests
 11. Analysis of variance
 12. Correlation
 13. Regression
 14. Chisquared test
 15. Advanced methods Appendices Glossary Answer key References Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA276 .W367 2018  Unknown 
 Workshop on Industry Practices for Forecasting (2nd : 2013 : Paris, France)
 Cham ; New York : Springer, [2015]
 Description
 Book — x, 339 pages : illustrations (some color) ; 23 cm.
 Summary

 1 Short Term Load Forecasting in the Industry for Establishing Consumption Baselines: A French Case.
 2 Confidence intervals and tests for highdimensional models: a compact review.
 3 Modelling and forecasting daily electricity load via curve linear regression.
 4 Constructing Graphical Models via the Focused Information Criterion.
 5 Nonparametric short term Forecasting electricity consumption with IBR.
 6 Forecasting the electricity consumption by aggregating experts.
 7 Flexible and dynamic modeling of dependencies via copulas.
 8 Operational and online residential baseline estimation.
 9 Forecasting intra day load curves using sparse functional regression.
 10 Modelling and Prediction of Time Series Arising on a Graph.
 11 GAM model based large scale electrical load simulation for smart grids.
 12 Spot volatility estimation for highfrequency data: adaptive estimation in practice.13 Time series prediction via aggregation: an oracle bound including numerical cost.14 Spacetime trajectories of wind power generation: Parametrized precision matrices under a Gaussian copula approach.15 Gametheoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts.
 16 The BAGIDIS distance: about a fractal topology, with applications to functional classification and prediction.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA280 .W67 2013  Unknown 
 Forsyth, David, author.
 Cham, Switzerland : Springer, [2018]
 Description
 Book — xxiv, 367 pages : illustrations (some color) ; 28 cm
 Summary

 First Tools for Looking at Data
 Looking at Relationships
 Basic ideas in probability
 Random Variables and Expectations
 Useful Probability Distributions
 Samples and Populations
 The Significance of Evidence
 Experiments
 Inferring Probability Models from Data
 Extracting Important Relationships in High Dimensions
 Learning to Classify
 Clustering: Models of High Dimensional Data
 Regression
 Markov Chains and Hidden Markov Models
 Resources and Extras.
(source: Nielsen Book Data)
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA76.9 .M35 F67 2018  Unknown 
 Milwaukee, Wisonsin : ASQ Quality Press, [2016]
 Description
 Book — xii, 467 pages : illustrations ; 26 cm
 Online
Education Library (Cubberley)
Education Library (Cubberley)  Status 

Stacks  
QA279.4 .S747 2016  Unknown 
 Berry, Kenneth J., author.
 Cham ; New York : Springer, [2014]
 Description
 Book — xix, 517 pages : illustrations ; 25 cm
 Summary

 Preface. 1.Introduction. 2.19201939. 2.1.Overview of Chapter 2. 2.2.NeymanFisherGeary and the Beginning. 2.3.Fisher and the Varianceratio Statistic. 2.4.EdenYates and Nonnormal Data. 2.5.Fisher and
 2 by
 2 Contingency Tables. 2.6 Yates and the Chisquared Test for Small Samples. 2.7.Irwin and Fourfold Contingency Tables. 2.8.The Rothamsted Manorial Estate. 2.9.Fisher and the Analysis of Darwin's Zea mays Data. 2.10.Fisher and the Coefficient of Racial Likeness. 2.11.HotellingPabst and Simple Bivariate Correlation. 2.12.Friedman and Analysis of Variance for Ranks. 2.13.Welch's Randomized Blocks and Latin Squares. 2.14.Egon Pearson on Randomization. 2.15.Pitman and Three Seminal Articles. 2.16.Welch and the Correlation Ratio. 2.17.Olds and Rankorder Correlation. 2.18.Kendall and Rank Correlation. 2.19.McCarthy and Randomized Blocks. 2.20.Computing and Calculators. 2.21.Looking Ahead. 3.19401959. 3.1.Overview of Chapter 3. 3.2.Development of Computing. 3.3.KendallBabington Smith and Paired Comparisons. 3.4.Dixon and a Twosample Rank Test. 3.5.SwedEisenhart and Tables for the Runs Test. 3.6.Scheff'e and Nonparametric Statistical Inference. 3.7.WaldWolfowitz and Serial Correlation. 3.8.Mann and a Test of Randomness Against Trend. 3.9.Barnard and
 2 by
 2 Contingency Tables. 3.10.Wilcoxon and the Twosample Ranksum Test. 3.11.Festinger and the Twosample Ranksum Test. 3.12.MannWhitney and a Twosample Ranksum Test. 3.13.Whitfield and a Measure of Ranked Correlation. 3.14.OlmsteadTukey and the Quadrantsum Test. 3.15.HaldaneSmith and a Test for Birthorder Effects. 3.16.Finney and the FisherYates Test for
 2 by
 2 Tables. 3.17.LehmannStein and Nonparametric Tests. 3.18 Rankorder Statistics. 3.19.van der Reyden and a Twosample Ranksum Test.3.20.White and Tables for the Ranksum Test. 3.21.Other Results for the Twosample Ranksum Test. 3.22.DavidKendallStuart and Rankorder Correlation. 3.23.FreemanHalton and an Exact Test of Contingency. 3.24.KruskalWallis and the Csample Ranksum Test. 3.25.BoxAndersen and Permutation Theory. 3.26.Leslie and Small Contingency Tables. 3.27.A Twosample Rank Test for Dispersion. 3.28.Dwass and Modified Randomization Tests. 3.29.Looking Ahead. 4.19601979. 4.1.Overview of Chapter 4. 4.2.Development of Computing. 4.3 Permutation Algorithms and Programs. 4.4.Ghent and the FisherYates Exact Test. 4.5.Programs for Contingency Table Analysis. 4.6.SiegelTukey and Tables for the Test of Variability. 4.7 .Other Tables of Critical Values. 4.8.Edgington and Randomization Tests. 4.9.The Matrix Occupancy Problem. 4.10.Kempthorne and Experimental Inference. 4.11.BakerCollier and Permutation F Tests 4.12.Permutation Tests in the 1970s. 4.13.Feinstein and Randomization. 4.14.The MannWhitney, Pitman, and Cochran Tests. 4.15.MielkeBerryJohnson and MRPP. 4.16.Determining the Number of Contingency Tables. 4.17.Soms and the Fisher Exact Permutation Test. 4.18.BakerHubert and Ordering Theory. 4.19.Green and Two Permutation Tests for Location. 4.20.AgrestiWackerlyBoyett and Approximate Tests. 4.21.Boyett and Random R by C Tables. 4.22.Looking Ahead. 5.19802000. 5.1.Overview of Chapter 5. 5.2.Development of Computing. 5.3.Permutation Methods and Contingency Tables. 5.4.Yates and
 2 by
 2 Contingency Tables. 5.5.MehtaPatel and a Network Algorithm. 5.6.MRPP and the Pearson type III Distribution. 5.7.MRPP and Commensuration. 5.8.Tukey and Re randomization. 5.9.Matchedpairs Permutation Analysis. 5.10.Subroutine PERMUT. 5.11.Moment Approximations and the F Test. 5.12.MielkeIyer and MRBP. 5.13.Relationships of MRBP to Other Tests. 5.14.Kappa and the Measurement of Agreement. 5.15.Basu and the Fisher Randomization Test. 5.16.StillWhite and Permutation Analysis of Variance. 5.17.Walters and the Utility of Resampling Methods. 5.18.ConoverIman and Rank Transformations. 5.19.Green and Randomization Tests. 5.20.GabrielHall and Re randomization Inference. 5.21.PaganoTritchler and Polynomialtime Algorithms. 5.22.Welch and a Median Permutation Test. 5.23.Boik and the FisherPitman Permutation Test. 5.24.MielkeYao Empirical Coverage Tests. 5.25.Randomization in Clinical Trials. 5.26.The Period From
 1990 to 2000. 5.27.Algorithms and Programs. 5.28.PageBrin and Google. 5.29.SpinoPagano and Trimmed/Winsorized Means. 5.30.MayHunter and Advantages of Permutation Tests. 5.31.MielkeBerry and Tests for Common Locations. 5.32.KennedyCade and Multiple Regression. 5.33.Blair et al. and Hotelling's T2 Test. 5.34.MielkeBerryNeidt and Hotelling's T2 Test. 5.35.CadeRichards and Tests for LAD Regression. 5.36.WalkerLoftisMielke and Spatial Dependence. 5.37.Frick on Processbased Testing. 5.38.LudbrookDudley and Biomedical Research. 5.39.The Fisher Z Transformation. 5.40.Looking Ahead. 6.Beyond 2000. 6.1.Overview of Chapter 6. 6.2.Computing After Year 2000. 6.3.Books on Permutation Methods. 6.4.A Summary of Contributions by Publication Year. 6.5.Agresti and Exact Inference for Categorical Data. 6.6.The Unweighted Kappa Measure of Agreement. 6.7.Mielke et al. and Combining Probability Values. 6.8.Legendre and Kendall's Coefficient of Concordance. 6.9.The Weighted Kappa Measure of Agreement. 6.10.Berry et al. and Measures of Ordinal Association. 6.11.Resampling for Multiway Contingency Tables. 6.12.MielkeBerry and a Multivariate Similarity Test. 6.13.Cohen's Weighted Kappa With Multiple Raters. 6.14.Exact Variance of Weighted Kappa. 6.15.Campbell and Twobytwo Contingency Tables. 6.16.Permutation Tests and Robustness. 6.17.Advantages of the Median for Analyzing Data. 6.18.Consideration of Statistical Outliers. 6.19.Multivariate Multiple Regression Analysis. 6.20.O'Gorman and Multiple Linear Regression. 6.21.BruscoStahlSteinley and Weighted Kappa. 6.22.Mielke et al. and Ridit Analysis. 6.23.Knijnenburg et al. and Probability Values. 6.24.Reiss et al. and Multivariate Analysis of Variance. 6.25.A Permutation Analysis of Trend. 6.26.CurranEverett and Permutation Methods. Epilogue. References. Acronyms. Name Index. Subject Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA165 .B47 2014  Unknown 
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