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1. The analysis of biological data [2015]
 Whitlock, Michael, author.
 Second edition.  New York, New York : W.H. Freeman and Company, [2015]
 Description
 Book — xxxiii, 818 pages : illustrations (some color) ; 25 cm
 Summary

 PART 1. INTRODUCTION TO STATISTICS
 1. Statistics and samples INTERLEAF
 1 Biology and the history of statistics
 2. Displaying data
 3. Describing data
 4. Estimating with uncertainty INTERLEAF
 2 Pseudoreplication
 5. Probability
 6. Hypothesis testing INTERLEAF
 3 Why statistical significance is not the same as biological importance
 PART 2. PROPORTIONS AND FREQUENCIES
 7. Analyzing proportions INTERLEAF
 4 Correlation does not require causation
 8. Fitting probability models to frequency data INTERLEAF
 5 Making a plan
 9. Contingency analysis: associations between categorical variables
 PART 3. COMPARING NUMERICAL VALUES
 10. The normal distribution INTERLEAF
 6 Controls in medical studies
 11. Inference for a normal population
 12. Comparing two means INTERLEAF
 7 Which test should I use?
 13. Handling violations of assumptions
 14. Designing experiments INTERLEAF
 8 Data dredging
 15. Comparing means of more than two groups INTERLEAF
 9 Experimental and statistical mistakes
 PART 4. REGRESSION AND CORRELATION
 16. Correlation between numerical variables INTERLEAF
 10 Publication bias
 17. Regression INTERLEAF
 11 Using species as data points
 PART 5. MODERN STATISTICAL METHODS
 18. Multiple explanatory variables
 19. Computerintensive methods
 20. Likelihood
 21. Metaanalysis: combining information from multiple studies.
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QH323.5 .W48 2015  Unknown 
 Feldman, Marcus W., author.
 [Stanford, Calif.] : Morrison Institute for Population and Resource Studies, [2014]
 Description
 Book — 48, [4], 8 pages : illustrations (some color) ; 28 cm.
 Online
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HB848 .W625 NO.130  Unknown 
Special Collections  Status 

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HB848 .W625 NO.130  Unavailable In process 
 New York : Humana Press, [2016]
 Description
 Book — xi, 418 pages : illustrations (some color) ; 26 cm.
 Summary

 Overview of sequence data formats / Hongen Zhang
 Integrative exploratory analysis of two or more genomic datasets / Chen Meng and Aedin Culhane
 Study design for sequencing studies / Loren A. Honaas, Naomi S. Altman, and Martin Krzywinski
 Genomic annotation resources in R/Bioconductor / Marc R.J. Carlson, Hervé Pagès, Sonali Arora, Valerie Obenchain, and Martin Morgan
 The gene expression omnibus database / Emily Clough and Tanya Barrett
 A practical guide to The Cancer Genome Atlas (TCGA) / Zhining Wang, Mark A. Jensen, and Jean Claude Zenklusen
 Working with oligonucleotide arrays / Benilton S. Carvalho
 Metaanalysis in gene expression studies / Levi Waldron and Markus Riester
 Practical analysis of genome contact interaction experiments / Mark A Carty and Olivier Elemento
 Quantitative comparison of largescale DNA enrichment sequencing data / Matthias Lienhard and Lukas Chavez
 Variant calling from next generation sequence data / Nancy F. Hansen
 Genomescale analysis of cellspecific regulatory codes using nuclear enzymes / Songjoon Baek and MyongHee Sung
 NGSQC generator : a quality control system for ChIPseq and related deep sequencinggenerated datasets / Marco Antonio MendozaParra, MohamedAshick M. Saleem, Matthias Blum, PierreEtienne Cholley, and Hinrich Gronemeyer
 Operating on genomic ranges using BEDOPS / Shane Neph, Alex P. Reynolds, M. Scott Kuehn, and John A. Stamatoyannopoulos
 GMAP and GSNAP for genomic sequence alignment : enhancements to speed, accuracy, and functionality / Thomas D. Wu, Jens Reeder, Michael Lawrence, Gabe Becker, and Matthew J. Brauer
 Visualizing genomic data using Gviz and Bioconductor / Florian Hahne and Robert Ivanek
 Introducing machine learning concepts with WEKA / Tony C. Smith and Eibe Frank
 Experimental design and power calculation for RNAseq experiments / Zhijin Wu and Hao Wu
 It's DElicious : a recipe for differential expression analyses of RNAseq experiments using quasilikelihood methods in edgeR / Aaron T.L. Lun, Yunshun Chen, and Gordon K. Smyth.
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QH506 .M45 V.1418  Unknown 
 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.
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QA276 .W367 2018  Unknown 
 Šmilauer, Petr, 1967 author.
 Second edition.  Cambridge : Cambridge University Press, 2014.
 Description
 Book — xii, 362 pages : illustrations ; 25 cm
 Summary

 Preface
 1. Introduction and data types
 2. Using Canoco 5
 3. Experimental design
 4. Basics of gradient analysis
 5. Permutation tests and variation partitioning
 6. Similarity measures and similaritybased methods
 7. Classification methods
 8. Regression methods
 9. Interpreting community composition with functional traits
 10. Advanced use of ordination
 11. Visualising multivariate data
 12. Case study
 1: variation in forest bird assemblages
 13. Case study
 2: search for community composition patterns and their environmental correlates: vegetation of spring meadows
 14. Case study
 3: separating the effects of explanatory variables
 15. Case study
 4: evaluation of experiments in randomised complete blocks
 16. Case study
 5: analysis of repeated observations of species composition from a factorial experiment
 17. Case study
 6: hierarchical analysis of crayfish community variation
 18. Case study
 7: analysis of taxonomic data with linear discriminant analysis and distancebased ordination methods
 19. Case study
 8: separating effects of space and environment on oribatid community with PCNM
 20. Case study
 9: performing linear regression with redundancy analysis Appendix A. Glossary Appendix B. Sample data sets and projects Appendix C. Access to Canoco and overview of other software Appendix D. Working with R References Index to useful tasks in Canoco 5 Index.
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QH541.15 .S72 L47 2014  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.
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QA280 .W67 2013  Unknown 
 Privitera, Gregory J., author.
 Second edition.  Thousand Oaks, California : SAGE Publications, Inc., [2019]
 Description
 Book — xli, 596 pages ; 26 cm
 Summary

 Acknowledgments
 Introduction and descriptive statistics
 Introduction to statistics
 Summarizing data : frequency distributions in tables and graphs
 Summarizing data : central tendency
 Summarizing data: variability
 Probability and the foundations of inferential statistics
 Probability, normal distributions, and z scores
 Characteristics of the sample mean
 Hypothesis testing : significance, effect size, and power
 Making inferences about one or two means
 Testing means : onesample t test with confidence intervals
 Testing means : twoindependentsample t test with confidence intervals
 Testing means : relatedsamples t test with confidence intervals
 Making inferences about the variability of two or more means
 Oneway analysis of variance : betweensubjects and withinsubjects (repeatedmeasures) designs
 Twoway analysis of variance : betweensubjects factorial design
 Making inferences about patterns, prediction, and nonparametric tests
 Correlation and linear regression
 Chisquare tests : goodnessoffit and the test for independence
 Afterword
 Appendix A: Basic math review and summation notation
 Appendix B: SPSS general instruction guide
 Appendix C: Statistical tables
 Appendix D: Chapter solutions for evennumbered problems
 Glossary
 References
 Index.
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HA29 .P75 2019  Unknown 
8. Statistics for the behavioral sciences [2018]
 Privitera, Gregory J., author.
 Third edition.  Thousand Oaks, California : SAGE Publications, Inc., [2018]
 Description
 Book — xliii, 650 pages, 114 variously numbered pages ; 26 cm
 Summary

 About the author
 Acknowledgments
 Preface to the instructor
 To the student : how to use spss with this book
 Introduction and descriptive statistics
 Introduction to statistics
 Summarizing data: frequency distributions in tables and graphs
 Summarizing data: central tendency
 Summarizing data: variability
 Probability and the foundations of inferential statistics
 Probability
 Probability, normal distributions, and Z scores
 Probability and sampling distributions
 Making inferences about one or two means
 Hypothesis testing: significance, effect size, and power
 Testing means : onesample and twoindependentsample t tests
 Testing means : the relatedsamples T test
 Estimation and confidence intervals
 Making inferences about the variability of two or more means
 Analysis of variance: oneway betweensubjects design
 Analysis of variance : oneway withinsubjects (repeatedmeasures) design
 Analysis of variance: twoway betweensubjects factorial design
 Making inferences about patterns, frequencies, and ordinal data
 Correlation
 Linear regression and multiple regression
 Nonparametric tests: chisquare tests
 Nonparametric tests: tests for ordinal data
 Afterword
 Appendix A. Basic math review and summation notation
 Appendix B. SPSSs general instructions guide
 Appendix C. Statistical tables
 Appendix D. Chapter solutions for evennumbered problems
 Glossary
 References.
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HA29 .P755 2018  Unknown 
 Wilcox, Rand R., author.
 Second edition.  Boca Raton, FL : CRC Press, Taylor & Francis Group, [2017]
 Description
 Book — xxiii, 706 pages ; 29 cm
 Summary

Requiring no prior training, Modern Statistics for the Social and Behavioral Sciences provides a twosemester, graduatelevel introduction to basic statistical techniques that takes into account recent advances and insights that are typically ignored in an introductory course. Hundreds of journal articles make it clear that basic techniques, routinely taught and used, can perform poorly when dealing with skewed distributions, outliers, heteroscedasticity (unequal variances) and curvature. Methods for dealing with these concerns have been derived and can provide a deeper, more accurate and more nuanced understanding of data. A conceptual basis is provided for understanding when and why standard methods can have poor power and yield misleading measures of effect size. Modern techniques for dealing with known concerns are described and illustrated. Features: * Presents an indepth description of both classic and modern methods * Explains and illustrates why recent advances can provide more power and a deeper understanding of data * Provides numerous illustrations using the software R * Includes an R package with over 1300 functions * Includes a solution manual giving detailed answers to all of the exercises This second edition describes many recent advances relevant to basic techniques. For example, a vast array of new and improved methods is now available for dealing with regression, including substantially improved ANCOVA techniques. The coverage of multiple comparison procedures has been expanded and new ANOVA techniques are described. Rand Wilcox is a professor of psychology at the University of Southern California. He is the author of 13 other statistics books and the creator of the R package WRS. He currently serves as an associate editor for five statistics journals. He is a fellow of the Association for Psychological Science and an elected member of the International Statistical Institute.
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HA29 .W51367 2017  Unknown 
 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 
12. Biostatistics for animal science [2017]
 Kaps, Miroslav, author.
 3rd edition.  Wallingford, Oxfordshire, UK ; Boston, MA : CABI, [2017]
 Description
 Book — xiv, 547 pages : illustrations ; 25 cm
 Summary

 1: Presenting and Summarizing Data2: Probability3: Random Variables and their Distributions4: Population and Sample5: Estimation of Parameters6: Hypothesis Testing7: Simple Linear Regression8: Correlation9: Multiple Linear Regression10: Curvilinear Regression11: Fixed Effects Oneway Analysis of Variance12: Random Effects Oneway Analysis of Variance13: Mixed Models14: Concepts of Experimental Design15: Blocking16: Changeover Designs17: Factorial Experiments18: Hierarchical or Nested Design19: Splitplot Design20: Analysis of Covariance21: Repeated Measures22: Analysis of Numerical Treatment Levels23: Discrete, Categorical and other Nonnormal Dependent Variables: Solutions of ExercisesAppendix A: Vectors and MatricesAppendix B: Statistical Tables.
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SF140 .S72 K37 2017  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.
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QA76.9 .M35 F67 2018  Unknown 
 Second edition.  New York, NY : Humana Press, [2017]
 Description
 Book — xii, 689 pages : illustrations (some color) ; 27 cm.
 Summary

 Statistical genetic terminology / Robert C. Elston, Jaya Satagopan, and Shuying Sun
 Identification of genotype errors / Jeffery O'Connell and Yin Yao
 Detecting pedigree relationship errors / Lei Sun
 Identifying cryptic relationships / Lei Sun, Apostolos Dimitromanolakis, and WeiMin Chen
 Estimating allele frequencies / Indra Adrianto and Courtney Montgomery
 Testing departure from hardyweinberg proportions / Jian Wang and Sanjay Shete
 Estimating disequilibrium coefficients / Maren Vens and Andreas Ziegler
 Detecting familial aggregation / Adam C. Naj and Terri H. Beaty
 Estimating heritability from twin studies / Katrina L. Grasby, Karin J.H. Verweij, Miriam A. Mosing, Brendan P. Zietsch, and Sarah E. Medland
 Estimating heritability from nuclear family and pedigree data / Murielle Bochud
 Correcting for ascertainment / Warren Ewens and Robert C. Elston
 Segregation analysis using the unified model / Xiangqing Sun
 Design considerations for genetic linkage and association studies / Jeremie Nsengimana and D. Timothy Bishop
 Modelbased linkage analysis of a quantitative trait / Yeunjoo E. Song, Sunah Song, and Audrey H. Schnell
 Modelbased linkage analysis of a binary trait / Rita M. Cantor
 Modelfree linkage analysis of a quantitative trait / Nathan J. Morris and Catherine M. Stein
 Modelfree linkage analysis of a binary trait / Wei Xu, Jin Ma, Celia M.T. Greenwood, Andrew D. Paterson, and Shelley B. Bull
 Single marker association analysis for unrelated samples / Gang Zheng, Ao Yuan, Qizhai Li, and Joseph L. Gastwirth
 Single marker familybased association analysis conditional on parental information / RenHua Chung, Daniel D. Kinnamon, and Eden R. Martin
 Single marker familybased association analysis not conditional on parental information / Junghyun Namkung and Sungho Won
 Calibrating population stratification in association analysis / Huaizhen Qin and Xiaofeng Zhu
 Crossphenotype association analysis using summary statistics from GWAS / Xiaoyin Li and Xiaofeng Zhu
 Haplotype inference / Sunah Song, Xin Li, and Jing Li
 MultiSNP haplotype analysis methods for association analysis / Daniel O. Stram
 Analysis of ethnic mixtures / Xiaofeng Zhu and Heming Wang
 Detecting multiethnic rare variants / Weiwei Ouyang, Xiaofeng Zhu, and Huaizhen Qin
 Identifying gene interaction networks / Danica Wiredja and Gurkan Bebek
 Structural equation modeling / Catherine M. Stein, Nathan J. Morris, Noemi B. Hall, and Nora L. Nock
 Mendelian randomization / Sandeep Grover, Fabiola Del Greco M., Catherine M. Stein, and Andreas Ziegler
 Preprocessing and quality control for wholegenome sequences from the illumina HiSeq X platform / Marvin N. Wright, Damian Gola, and Andreas Ziegler
 Processing and analyzing human microbiome data / Xuan Zhu, Jian Wang, Cielito ReyesGibby, and Sanjay Shete.
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QH506 .M45 V.1666  Unknown 
15. Statistical analysis in proteomics [2016]
 New York : Humana Press, [2016]
 Description
 Book — x, 313 pages : illustrations (some color) ; 27 cm.
 Summary

 Introduction to proteomics technologies / Christof Lenz and Hassan Dihazi
 Topics in study design and analysis for multistage clinical proteomics studies / Irene Sui Lan Zeng
 Preprocessing and analysis of LCMSbased proteomic data / TsungHeng Tsai, Minkun Wang, and Habtom W. Ressom
 Normalization of reverse phase protein microarray data : choosing the best normalization analyte / Antonella Chiechi
 Outlier detection for mass spectrometric data / HyungJun Cho and SooHeang Eo
 Visualization and differential analysis of protein expression data using R / Tomé S. Silva and Nadège Richard
 False discovery rate estimation in proteomics / Suruchi Aggarwal and Amit Kumar Yadav
 Nonparametric bayesian model for nested clustering / Juhee Lee [and others]
 Setbased test procedures for the functional analysis of protein lists from differential analysis / Jochen Kruppa and Klaus Jung
 Classification of samples with orderrestricted discriminant rules / David Conde [and others]
 Application of discriminant analysis and crossvalidation on proteomics data / Julia Kuligowski, David PérezGuaita, and Guillermo Quintás
 Protein sequence analysis by proximities / FrankMichael Schleif
 Statistical method for integrative platform analysis : application to integration of proteomic and microarray data / Xin Gao
 Data fusion in metabolomics and proteomics for biomarker discovery / Lionel Blanchet and Agnieszka Smolinska
 Reconstruction of protein networks using reversephase protein array data / Silvia von der Heyde [and others]
 Detection of unknown amino acid Substitutions using errortolerant database search / Sven H. Giese, Franziska Zickmann, and Bernhard Y. Renard
 Data analysis strategies for protein modification identification / Yan Fu
 Dissecting the iTRAQ data analysis / Suruchi Aggarwal and Amit Kumar Yadav
 Statistical aspects in proteomic biomarker discovery / Klaus Jung.
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QH506 .M45 V.1362  Unknown 
 Thompson, Julie D., author.
 London, UK : ISTE Press : Elsevier, 2016.
 Description
 Book — xi, 134 pages : illustrations ; 24 cm
 Summary

 PART I: Fundamental concepts
 1. Introduction
 2. Multiple sequence applications PART II: Traditional multiple sequence alignment methods
 3. Heuristic approaches
 4. Statistical approaches
 5. Objective functions
 6. Alignment benchmarks PART III: Largescale multiple sequence alignment methods
 1. Efficient methods for multiple alignment of complete genome sequences
 2. Efficient methods for multiple alignment of 1,000's of sequences
 3. HPC implementations
 4. Alignment quality analysis.
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QH324.2 .T466 2016  Unknown 
 Looney, Stephen W., author.
 Hoboken, New Jersey : John Wiley & Sons, Inc., [2015]
 Description
 Book — xvii, 405 pages : illustrations ; 25 cm
 Summary

 Designing biomarker studies
 Elementary statistical methods for analyzing biomarker data
 Frequently encountered challenges in analyzing biomarker data and how to deal with them
 Validation of biomarkers.
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R857 .B54 L66 2015  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 
 Gassiat, Élisabeth, author.
 Paris, France : Société Mathématique de France, 2014.
 Description
 Book — viii, 142 pages ; 25 cm.
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
Q386 .G37 2014  Unknown 
20. Foundations of applied statistical methods [2014]
 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.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
Q180.55 .S7 L44 2014  Unknown 
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