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 Dehmer, Matthias, 1968
 Hoboken, N.J. : Wiley, 2012.
 Description
 Book — 1 online resource.
 Summary

 Chapter 1. A Survey of Computational Approaches to Reconstruct and Partition Biological Networks Acharya et al.
 Chapter 2. Introduction to Complex Networks: Measures, Statistical Properties, and Models Takemoto et al.
 Chapter 3. Modeling for Evolving Biological Networks Takemoto et al.
 Chapter 4. Modularity Configurations in Biological Networks with Embedded Dynamics Capobianco et al.
 Chapter 5. Influence of Statistical Estimators on the Large Scale Causal Inference of Regulatory Networks Matos de Simoes and EmmertStreib
 Chapter 6. Weighted Spectral Distribution: A Metric for Structural Analysis of Networks Fay, Haddadi et al.
 Chapter 7. The Structure of an Evolving Random Bipartite Graph Kutzelnigg
 Chapter 8. Graph Kernels Rupp
 Chapter 9. Networkbased information synergy analysis for Alzheimer disease Wang, Geekiyanage and Chan
 Chapter 10. DensityBased Set Enumeration in Structured Data Georgii and Tsuda
 Chapter 11. Hyponym Extraction Employing a Weighted Graph Kernel Vor der Bruck.
 (source: Nielsen Book Data)
 Preface ix Contributors xi
 1 A Survey of Computational Approaches to Reconstruct and Partition Biological Networks
 1 Lipi Acharya, Thair Judeh, and Dongxiao Zhu
 2 Introduction to Complex Networks: Measures, Statistical Properties, and Models
 45 Kazuhiro Takemoto and Chikoo Oosawa
 3 Modeling for Evolving Biological Networks
 77 Kazuhiro Takemoto and Chikoo Oosawa
 4 Modularity Configurations in Biological Networks with Embedded Dynamics
 109 Enrico Capobianco, Antonella Travaglione, and Elisabetta Marras
 5 Influence of Statistical Estimators on the LargeScale Causal Inference of Regulatory Networks
 131 Ricardo de Matos Simoes and Frank EmmertStreib
 6 Weighted Spectral Distribution: A Metric for Structural Analysis of Networks
 153 Damien Fay, Hamed Haddadi, Andrew W. Moore, Richard Mortier, Andrew G. Thomason, and Steve Uhlig
 7 The Structure of an Evolving Random Bipartite Graph
 191 Reinhard Kutzelnigg
 8 Graph Kernels
 217 Matthias Rupp
 9 NetworkBased Information Synergy Analysis for Alzheimer Disease
 245 Xuewei Wang, Hirosha Geekiyanage, and Christina Chan
 10 DensityBased Set Enumeration in Structured Data
 261 Elisabeth Georgii and Koji Tsuda
 11 Hyponym Extraction Employing a Weighted Graph Kernel
 303 Tim vor der Br¨uck Index 327.
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Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networksmeasures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Densitybased enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduatelevel, crossdisciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.
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 Sahu, Pradip Kumar.
 New Delhi ; New York : Springer, c2013.
 Description
 Book — 1 online resource.
 Online

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 Boddy, Richard, 1939
 Chichester, West Sussex, U.K. : Wiley, 2009.
 Description
 Book — xii, 236 p. : ill. ; 24 cm.
 Summary

 Preface.
 1 Samples and populations. Introduction. What a lottery! No can do. Nobody is listening to me. How clean is my river? Discussion.
 2 What is the true mean? Introduction. Presenting data. Averages. Measures of variability. Relative standard deviation . Degrees of freedom. Confidence interval for the population mean. Sample sizes. How much moisture is in the raw material? Problems.
 3 Exploratory data analysis. Introduction. Histograms: is the process capable of meeting specifications? Box plots: how long before the lights go out? The box plot in practice. Problems.
 4 Significance testing. Introduction. The onesample t test. The significance testing procedure. Confidence intervals as an alternative to significance testing. Confidence interval for the population standard deviation. Ftest for ratio of standard deviations. Problems.
 5 The normal distribution. Introduction. Properties of the normal distribution. Example. Setting the process mean. Checking for normality. Uses of the normal distribution. Problems.
 6 Tolerance intervals. Introduction. Example. Confidence intervals and tolerance intervals.
 7 Outliers. Introduction. Grubbs' test. Warning.
 8 Significance tests for comparing two means. Introduction. Example: watching paint lose its gloss. The twosample t test for independent samples. An alternative approach: a confidence intervals for the difference between population means. Sample size to estimate the difference between two means. A production example. Confidence intervals for the difference between the two suppliers. Sample size to estimate the difference between two means. Conclusions. Problems.
 9 Significance tests for comparing paired measurements. Introduction. Comparing two fabrics. The wrong way. The paired sample t test. Presenting the results of significance tests. Onesided significance tests. Problems.
 10 Regression and correlation. Introduction. Obtaining the best straight line. Confidence intervals for the regression statistics. Extrapolation of the regression line. Correlation coefficient. Is there a significant relationship between the variables? How good a fit is the line to the data? Assumptions. Problems.
 11 The binomial distribution. Introduction. Example. An exact binomial test. A quality assurance example. What is the effect of the batch size? Problems.
 12 The Poisson distribution. Introduction. Fitting a Poisson distribution. Are the defects random? The Poisson distribution. Poisson dispersion test. Confidence intervals for a Poisson count. A significance test for two Poisson counts. How many black specks are in the batch? How many pathogens are there in the batch? Problems.
 13 The chisquared test for contingency tables. Introduction. Twosample test for percentages. Comparing several percentages. Where are the differences? Assumptions. Problems.
 14 Nonparametric statistics. Introduction. Descriptive statistics. A test for two independent samples: WilcoxonMannWhitney test. A test for paired data: Wilcoxon matchedpairs sign test. What type of data can be used? Example: cracking shoes. Problems.
 15 Analysis of variance: Components of variability. Introduction. Overall variability. Analysis of variance. A practical example. Terminology. Calculations. Significance test. Variation less than chance? When should the above methods not be used? Between and withinbatch variability. How many batches and how many prawns should be sampled? Problems.
 16 Cusum analysis for detecting process changes. Introduction. Analysing past data. Intensity. Localised standard deviation. Significance test. Yield. Conclusions from the analysis. Problem.
 17 Rounding of results. Introduction. Choosing the rounding scale. Reporting purposes: deciding the amount of rounding. Reporting purposes: rounding of means and standard deviations. Recording the original data and using means and standard deviations in statistical analysis. References. Solutions to Problems. Statistical Tables. Index.
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Q180.55 .S7 B63 2009  Unknown 
 McPherson, Glen.
 2nd ed.  New York : Springer, c2001.
 Description
 Book — xxviii, 640 p. : ill. ; 24 cm.
 Summary

 The Importance of Statistics in an Informatin Based World. Data: The Factual Information. Statistical Models: The Experimenter's View. Comparing Model and Data. Probability: A Fundamental Tool of Statistics. Some Widely Used Statistical Models. Some Important Statistics and Their Sampling Distributions. Statistical Analysis: The Statisticians' View. Examining Proportions and Success Rates. Model and Data Chekcking. Questions About the Average Value. Comparing Two Groups, Treatments or Processes. Comparative Studies, Surveys and Designed Experiments. Comparing More Than Two Treatment or Groups. Comparing Mean Response When There Are Three or More Treatments. Comparing Patterns of Response: Frequency Tables. Studying Relations Between Variables. Prediction and Estimation: The Role of Explanatory Variables. Questions About Variability.  Cause and Effect: Statistical Perspectives. Studying Changes in Response Over Time.
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Q180.55 .S7 M36 2001  Unknown 
 McPherson, Glen.
 New York : SpringerVerlag, c1990.
 Description
 Book — xxvi, 666 p. : ill. ; 25 cm.
 Summary

This volume examines the use of statistics in scientific investigation.
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Q180.55 .S7 M36 1990  Available 
 Gassiat, Élisabeth, author.
 Paris, France : Société Mathématique de France, 2014.
 Description
 Book — viii, 142 pages ; 25 cm.
 Online
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Q386 .G37 2014  Unknown 
 Lee, Hang, author.
 Cham : Springer, [2014].
 Description
 Book — x, 161 pages : illustrations ; 24 cm
 Summary

 Warming UpDescriptive Statistics and Essential Probability Models. Statistical Inference Focusing on a Single Mean or Proportion. Inference Using ttests for Comparing Two Means. Inference Using Analysis of Variance for Comparing Multiple Means. Inference Using Correlation and Regression. Normal Distribution Assumption Free NonParametric Inference. Methods for Censored Survival Time Data Analysis and Inference. Sample Size Determination for Inference. Review Exercise Problems. Probability of Standard Normal Distribution. Percentiles of tDistributions. Upper 95th and 99th Percentiles of Chisquare Distributions. Upper 95th Percentiles of FDistributions. Upper 99th Percentiles of FDistributions. Sample Sizes for Independent Samples ttests (normal approximation). Index.
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Q180.55 .S7 L44 2014  Unknown 
 Beachwood, Ohio : Institute of Mathematical Statistics, c2003.
 Description
 Book — ix, 435 p. : ill. (some col.), port. ; 26 cm.
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Q180.55 .S7 S726 2003  Available 
 Basel ; Boston : Birkhauser Verlag, c2001.
 Description
 Book — xiv, 183 p. : ill. ; 24 cm.
 Summary

 Statistical interaction with quantitative geneticists to enhance impact from plant breeding programmes, Kaye E. Basford outlier resistance, standardization and modelling issues for DNA microarray data, Dhammika Amaratunga and Javier Cabrera variance components estimation with uncertainty, Xiaoming Sheng and Chris Field robust estimation for chemical concentration data subject to detection limits, Leo R. Korn and David E. Tyler risk assessment of low dose exposure to carcinogens, Mendel Fygenson a stochastic model of carcinogenesis, Pablo Herrero et al statistical modelling to answer key questions in atmospheric chemistry  three case studies, Johannes Staehelin and Werner A. Stahel space debris  flux in a two dimensional orbit, David R. Brillinger a robust approach to common principal components, Graciela Boente and Liliana Orellana a robustified version of sliced inverse regression, Ursula Gather et al similarities between location depth and regression depth, Mia Hubert et al approximate testimates for linear regression based on subsampling of elemental sets, Jorge Adrover et al.
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Q180.55 .S7 S73 2001  Available 
10. Statistical inference in science [2000]
 Sprott, D. A.
 New York : Springer, c2000.
 Description
 Book — xv, 245 p. : ill. ; 25 cm.
 Summary

 Introduction. The Likelihood Function. Division of Sample Information I. Division of Sample Information II. Estimation Statements. Tests of Significance. The LocationScale Pivotal Model. The Gauss Linear Model. Maximum Likelihood Estimation. Controlled Experiments. Problems.
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Q180.55 .S7 S67 2000  Unknown 
 Sprott, D. A.
 New York : Springer, c2000.
 Description
 Book — xv, 245 p. : ill.
 Schalkoff, Robert J.
 New York : J. Wiley, c1992.
 Description
 Book — xix, 364 p. : ill. ; 25 cm.
 Summary

 STATISTICAL PATTERN RECOGNITION (StatPR). Supervised Learning (Training) Using Parametric and Nonparametric Approaches. Linear Discriminant Functions and the Discrete and Binary Feature Cases. Unsupervised Learning and Clustering. SYNTACTIC PATTERN RECOGNITION (SyntPR). Overview. Syntactic Recognition via Parsing and Other Grammars. Graphical Approaches to SyntPR. Learning via Grammatical Inference. NEURAL PATTERN RECOGNITION (NeurPR). Introduction to Neural Networks. Introduction to Neural Pattern Associators and Matrix Approaches. Feedforward Networks and Training by Backpropagation. Content Addressable Memory Approaches and Unsupervised Learning in NeurPR. Appendices. References. Permission Source Notes. Index.
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Q327 .S27 1992  Available 
 Kraemer, Helena Chmura.
 Newbury Park, Calif. : Sage, c1987.
 Description
 Book — 120 p. ; 23 cm.
 Summary

 CHAPTER 1 Introduction
 CHAPTER 2 General Concepts
 CHAPTER 3 The Pivotal Case Intraclass Correlation
 CHAPTER 4 Equality of News Z and T tests, Balanced ANOVA
 CHAPTER 5 Correlation Coefficients
 CHAPTER 6 Linear Regression
 CHAPTER 7 Homogeneity of Variance Tests
 CHAPTER 8 Binomial Tests
 CHAPTER 9 Contingency Table Analysis
 CHAPTER 10 Conclusions.
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Q180.55 .S7 K73 1987  Unknown 
Find it Velma Denning Room (Social Science Data and Software)  
Q180.55 .S7 K73 1987  Inlibrary use 
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Q180.55 .S7 K73 1987  Unavailable Checked out  Overdue Request 
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Q180.55 .S7 K73 1987  Available 
14. Pattern recognition : a statistical approach [1982]
 Devijver, Pierre A., 1936
 Englewood Cliffs, N.J. : Prentice/Hall International, c1982.
 Description
 Book — xiv, 448 p. : ill. ; 25 cm.
 Online
15. Probability and statistics in systems work [1961]
 Reed, I. S.
 Santa Monica, Calif. : Rand Corporation, 1961.
 Description
 Book — i, 36 p. : ill. ; 29 cm.
 Online
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Q180 .A1 R361 P2407  Available 
16. Data analysis for the life sciences with R [2017]
 Irizarry, Rafael A.
 Boca Raton, Fla. : CRC Press, 2017.
 Description
 Book — 1 online resource ( xxi, 353 pages) : illustrations (some color).
 Summary

 Introduction. Getting started. Inference. Exploratory data analysis. Robust summaries. Matrix algebra. Linear models. Inference for high dimensional data. Statistical models. Distance and dimension reduction. Statistical models. Distance and dimension reduction. Basic machine learning. Batch effects.
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17. Implementing reproducible research [2014]
 Boca Raton : CRC Press, Taylor & Francis Group, [2014]
 Description
 Book — xix, 428 pages : illustrations ; 25 cm.
 Summary

 Tools knitr: A Comprehensive Tool for Reproducible Research in R Yihui Xie Reproducibility Using VisTrails Juliana Freire, David Koop, Fernando Chirigati, and Claudio T. Silva Sumatra: A Toolkit for Reproducible Research Andrew P. Davison, Michele Mattioni, Dmitry Samarkanov, and Bartosz Telenczuk CDE: Automatically Package and Reproduce Computational Experiments Philip J. Guo Reproducible Physical Science and the Declaratron Peter MurrayRust and Dave MurrayRust
 Practices and Guidelines Developing OpenSource Scientific Practice K. Jarrod Millman and Fernando Perez Reproducible Bioinformatics Research for Biologists Likit Preeyanon, Alexis Black Pyrkosz, and C. Titus Brown Reproducible Research for LargeScale Data Analysis Holger Hoefling and Anthony Rossini Practicing Open Science Luis Ibanez, William J. Schroeder, and Marcus D. Hanwell Reproducibility, Virtual Appliances, and Cloud Computing Bill Howe The Reproducibility Project: A Model of LargeScale Collaboration for Empirical Research on Reproducibility Open Science Collaboration What Computational Scientists Need to Know about Intellectual Property Law: A Primer Victoria Stodden
 Platforms Open Science in Machine Learning Mikio L. Braun and Cheng Soon Ong RunMyCode.org: A ResearchReproducibility Tool for Computational Sciences Christophe Hurlin, Christophe Perignon, and Victoria Stodden Open Science and the Role of Publishers in Reproducible Research Iain Hrynaszkiewicz, Peter Li, and Scott Edmunds
 Index.
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 Ellis, Paul D., 1969
 Cambridge, UK ; New York : Cambridge University Press, 2010.
 Description
 Book — xvii, 173 p. : ill. ; 26 cm.
 Summary

 List of figures List of tables List of boxes Introduction Part I. Effect Sizes and the Interpretation of Results:
 1. Introduction to effect sizes
 2. Interpreting effects Part II. The Analysis of Statistical Power:
 3. Power analysis and the detection of effects
 4. The painful lessons of power research Part III. MetaAnalysis:
 5. Drawing conclusions using metaanalysis
 6. Minimizing bias in metaanalysis Last word: thirty recommendations for researchers Appendices:
 1. Minimum sample sizes
 2. Alternative methods for metaanalysis Bibliography Index.
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Q180.55 .S7 E45 2010  Unknown 
 Mandel, John, 19142007.
 New York : Dover, 1984, ©1964.
 Description
 Book — 1 online resource (xi, 410 pages) : illustrations.
 Online
 Quirk, Thomas J. author.
 Switzerland : Springer, 2016.
 Description
 Book — 1 online resource (xv, 246 pages) : illustrations (some color).
 Summary

This book shows the capabilities of Microsoft Excel in teaching physical science statistics effectively. Similar to the previously published Excel 2013 for Physical Sciences Statistics, this book is a stepbystep exercisedriven guide for students and practitioners who need to master Excel to solve practical physical science problems. If understanding statistics isn't the reader's strongest suit, the reader is not mathematically inclined, or if the reader is new to computers or to Excel, this is the book to start off with. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in physical science courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2016 for Physical Sciences Statistics: A Guide to Solving Practical Problems capitalizes on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work. Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easytounderstand physical science problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned.
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