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1. Advanced R statistical programming and data models : analysis, machine learning, and visualization [2019]
 Wiley, Matt, author.
 [California, CA] : Apress, [2019] New York : Distributed to the book trade worldwide by Springer Science+Business Media New York, [2019]
 Description
 Book — 1 online resource (1 volume) : illustrations
 Dörre, Achim, author.
 Singapore : Springer, [2019]
 Description
 Book — 1 online resource
 Summary

 Chapter 1: Introduction to doubletruncation.
 Chapter 2: Parametric inference under special exponential family.
 Chapter 3: Parametric inference under locationscale family.
 Chapter 4: Bayes inference.
 Chapter 5: Nonparametric inference.
 Chapter 6: Linear regression. Appendix A: Data (if German company data are available). Appendix B: R codes for inference under exponential family. Appendix C: R codes for inference under locationscale family. Appendix D: R codes for Bayes inference. Appendix E: R codes for linear regression.
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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 
 Popping, R. (Roel), author.
 Cham, Switzerland : Springer, [2019]
 Description
 Book — 1 online resource (156 p.)
 Summary

 Introduction. Reliability and Validity. Interrater Agreement. Indices. References. Index. Notation.
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5. 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 
6. 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 
 Chihara, Laura, 1957 author.
 Second edition.  Hoboken, NJ : John Wiley & Sons, Inc., 2019.
 Description
 Book — 1 online resource (xv, 537 pages)
 Summary

 Preface xiii
 1 Data and Case Studies
 1 1.1 Case Study: Flight Delays
 1 1.2 Case Study: BirthWeights of Babies
 2 1.3 Case Study: Verizon Repair Times
 3 1.4 Case Study: Iowa Recidivism
 4 1.5 Sampling
 5 1.6 Parameters and Statistics
 6 1.7 Case Study: General Social Survey
 7 1.8 Sample Surveys
 8 1.9 Case Study: Beer and HotWings
 9 1.10 Case Study: Black Spruce Seedlings
 10 1.11 Studies
 10 1.12 Google Interview Question: Mobile Ads Optimization
 12 Exercises
 16
 2 Exploratory Data Analysis
 21 2.1 Basic Plots
 21 2.2 Numeric Summaries
 25 2.2.1 Center
 25 2.2.2 Spread
 26 2.2.3 Shape
 27 2.3 Boxplots
 28 2.4 Quantiles and Normal Quantile Plots
 29 2.5 Empirical Cumulative Distribution Functions
 35 2.6 Scatter Plots
 38 2.7 Skewness and Kurtosis
 40 Exercises
 42
 3 Introduction to Hypothesis Testing: Permutation Tests
 47 3.1 Introduction to Hypothesis Testing
 47 3.2 Hypotheses
 48 3.3 Permutation Tests
 50 3.3.1 Implementation Issues
 55 3.3.2 Onesided and Twosided Tests
 61 3.3.3 Other Statistics
 62 3.3.4 Assumptions
 64 3.3.5 Remark on Terminology
 68 3.4 Matched Pairs
 68 Exercises
 70
 4 Sampling Distributions
 75 4.1 Sampling Distributions
 75 4.2 Calculating Sampling Distributions
 80 4.3 The Central LimitTheorem
 84 4.3.1 CLT for Binomial Data
 86 4.3.2 Continuity Correction for Discrete Random Variables
 89 4.3.3 Accuracy of the Central Limit Theorem
 91 4.3.4 CLT for SamplingWithout Replacement
 92 Exercises
 93
 5 Introduction to Confidence Intervals: The Bootstrap
 103 5.1 Introduction to the Bootstrap
 103 5.2 The Plugin Principle
 110 5.2.1 Estimating the Population Distribution
 112 5.2.2 How Useful Is the Bootstrap Distribution?
 113 5.3 Bootstrap Percentile Intervals
 118 5.4 TwoSample Bootstrap
 119 5.4.1 Matched Pairs
 124 5.5 Other Statistics
 128 5.6 Bias
 131 5.7 Monte Carlo Sampling: The "Second Bootstrap Principle"
 134 5.8 Accuracy of Bootstrap Distributions
 135 5.8.1 Sample Mean: Large Sample Size
 135 5.8.2 Sample Mean: Small Sample Size
 137 5.8.3 Sample Median
 138 5.8.4 MeanVariance Relationship
 138 5.9 HowMany Bootstrap Samples Are Needed?
 140 Exercises
 141
 6 Estimation
 149 6.1 Maximum Likelihood Estimation
 149 6.1.1 Maximum Likelihood for Discrete Distributions
 150 6.1.2 Maximum Likelihood for Continuous Distributions
 153 6.1.3 Maximum Likelihood for Multiple Parameters
 157 6.2 Method of Moments
 161 6.3 Properties of Estimators
 163 6.3.1 Unbiasedness
 164 6.3.2 Efficiency
 167 6.3.3 Mean Square Error
 171 6.3.4 Consistency
 173 6.3.5 Transformation Invariance
 175 6.3.6 Asymptotic Normality of MLE
 177 6.4 Statistical Practice
 178 6.4.1 Are You Asking the Right Question?
 179 6.4.2 Weights
 179 Exercises
 180
 7 More Confidence Intervals
 187 7.1 Confidence Intervals for Means
 187 7.1.1 Confidence Intervals for a Mean, Variance Known
 187 7.1.2 Confidence Intervals for a Mean, Variance Unknown
 192 7.1.3 Confidence Intervals for a Difference in Means
 198 7.1.4 Matched Pairs, Revisited
 204 7.2 Confidence Intervals in General
 204 7.2.1 Location and Scale Parameters
 208 7.3 Onesided Confidence Intervals
 212 7.4 Confidence Intervals for Proportions
 214 7.4.1 AgrestiCoull Intervals for a Proportion
 217 7.4.2 Confidence Intervals for a Difference of Proportions
 218 7.5 Bootstrap Confidence Intervals
 219 7.5.1 t Confidence Intervals Using Bootstrap Standard Errors
 219 7.5.2 Bootstrap t Confidence Intervals
 220 7.5.3 Comparing Bootstrap t and Formula t Confidence Intervals
 224 7.6 Confidence Interval Properties
 226 7.6.1 Confidence Interval Accuracy
 226 7.6.2 Confidence Interval Length
 227 7.6.3 Transformation Invariance
 227 7.6.4 Ease of Use and Interpretation
 227 7.6.5 Research Needed
 228 Exercises
 228
 8 More Hypothesis Testing
 241 8.1 Hypothesis Tests for Means and Proportions: One Population
 241 8.1.1 A Single Mean
 241 8.1.2 One Proportion
 244 8.2 Bootstrap tTests
 246 8.3 Hypothesis Tests for Means and Proportions: Two Populations
 248 8.3.1 Comparing Two Means
 248 8.3.2 Comparing Two Proportions
 251 8.3.3 Matched Pairs for Proportions
 254 8.4 Type I and Type II Errors
 255 8.4.1 Type I Errors
 257 8.4.2 Type II Errors and Power
 261 8.4.3 PValues Versus Critical Regions
 266 8.5 Interpreting Test Results
 267 8.5.1 PValues
 267 8.5.2 On Significance
 268 8.5.3 Adjustments for Multiple Testing
 269 8.6 Likelihood Ratio Tests
 271 8.6.1 Simple Hypotheses and the NeymanPearson Lemma
 271 8.6.2 Likelihood Ratio Tests for Composite Hypotheses
 275 8.7 Statistical Practice
 279 8.7.1 More Campaigns with No Clicks and No Conversions
 284 Exercises
 285
 9 Regression
 297 9.1 Covariance
 297 9.2 Correlation
 301 9.3 LeastSquares Regression
 304 9.3.1 Regression toward the Mean
 308 9.3.2 Variation
 310 9.3.3 Diagnostics
 311 9.3.4 Multiple Regression
 317 9.4 The Simple LinearModel
 317 9.4.1 Inference for 𝛼 and 𝛽
 322 9.4.2 Inference for the Response
 326 9.4.3 Comments about Assumptions for the Linear Model
 330 9.5 Resampling Correlation and Regression
 332 9.5.1 Permutation Tests
 335 9.5.2 Bootstrap Case Study: Bushmeat
 336 9.6 Logistic Regression
 340 9.6.1 Inference for Logistic Regression
 346 Exercises
 350
 10 Categorical Data
 359 10.1 Independence in Contingency Tables
 359 10.2 Permutation Test of Independence
 361 10.3 Chisquare Test of Independence
 365 10.3.1 Model for Chisquare Test of Independence
 366 10.3.2
 2 x
 2 Tables
 368 10.3.3 Fisher's Exact Test
 370 10.3.4 Conditioning
 371 10.4 Chisquare Test of Homogeneity
 372 10.5 Goodnessoffit Tests
 374 10.5.1 All Parameters Known
 374 10.5.2 Some Parameters Estimated
 377 10.6 Chisquare and the Likelihood Ratio
 379 Exercises
 380
 11 Bayesian Methods
 391 11.1 Bayes Theorem
 392 11.2 Binomial Data: Discrete Prior Distributions
 392 11.3 Binomial Data: Continuous Prior Distributions
 400 11.4 Continuous Data
 406 11.5 Sequential Data
 409 Exercises
 414
 12 Oneway ANOVA
 419 12.1 Comparing Three or More Populations
 419 12.1.1 The ANOVA Ftest
 419 12.1.2 A Permutation Test Approach
 428 Exercises
 429
 13 Additional Topics
 433 13.1 Smoothed Bootstrap
 433 13.1.1 Kernel Density Estimate
 435 13.2 Parametric Bootstrap
 437 13.3 The Delta Method
 441 13.4 Stratified Sampling
 445 13.5 Computational Issues in Bayesian Analysis
 446 13.6 Monte Carlo Integration
 448 13.7 Importance Sampling
 452 13.7.1 Ratio Estimate for Importance Sampling
 458 13.7.2 Importance Sampling in Bayesian Applications
 461 13.8 The EM Algorithm
 467 13.8.1 General Background
 469 Exercises
 472 Appendix A Review of Probability
 477 A.1 Basic Probability
 477 A.2 Mean and Variance
 478 A.3 The Normal Distribution
 480 A.4 The Mean of a Sample of RandomVariables
 481 A.5 Sums of Normal Random Variables
 482 A.6 The Law of Averages
 483 A.7 Higher Moments and the Momentgenerating Function
 484 Appendix B Probability Distributions
 487 B.1 The Bernoulli and Binomial Distributions
 487 B.2 The Multinomial Distribution
 488 B.3 The Geometric Distribution
 490 B.4 The Negative Binomial Distribution
 491 B.5 The Hypergeometric Distribution
 492 B.6 The Poisson Distribution
 493 B.7 The Uniform Distribution
 495 B.8 The Exponential Distribution
 495 B.9 The Gamma Distribution
 497 B.10 The Chisquare Distribution
 499 B.11 The Student's t Distribution
 502 B.12 The Beta Distribution
 504 B.13 The F Distribution
 505 Exercises
 507 Appendix C Distributions Quick Reference
 509 Solutions to Selected Exercises
 513 References
 525 Index 531.
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 Conference of the International Society for Nonparametric Statistics (3rd : 2016 : Avignon, France)
 Cham, Switzerland : Springer, 2019.
 Description
 Book — 1 online resource.
 Summary

 Intro; Contents; Contributors; Symmetrizing knn and Mutual knn Smoothers;
 1 Introduction; 1.1 The Statistical Background; 1.2 knn and Mutual knn Smoothers; 1.3 Admissibility; 1.4 Symmetrization;
 2 Symmetrization Procedures for RowStochastic Smoothers; 2.1 Geometric and Arithmetic Mean; 2.2 Cohen's and Zhao's Symmetrization;
 3 Symmetrization of knnType Smoothers; 3.1 Construction of the Symmetrized Estimator; 3.2 Interpretation, Estimation at Any Point x;
 4 Conclusion; Appendix; References; Nonparametric PU Learning of State Estimation in Markov Switching Model;
 1 Introduction
 4 Numerical Examples4.1 Local Study; 4.2 Global Study;
 5 Conclusion;
 6 Proofs; 6.1 Proof of Proposition 1; 6.2 Proof of Lemma 1; 6.3 Proof of Theorem 1; 6.4 Proof of Theorem 2; References; Efficiency of the VFold Model Selection for Localized Bases;
 1 Introduction;
 2 Model Selection Setting;
 3 VFold CrossValidation;
 4 VFold Penalization;
 5 Simulation Study;
 6 Proofs; References; Nonparametric Lower Bounds and Information Functions;
 1 Introduction;
 2 Regularity Conditions and Lower Bounds;
 3 Lower Bounds Based on Continuity Moduli;
 4 On Unbiased Estimation;
 5 On Consistent Estimation
 6 On Uniform ConvergenceReferences; Modification of MomentBased Tail Index Estimator: Sums Versus Maxima;
 1 Introduction and Main Results;
 2 Comparison;
 3 Proofs; References; Constructing Confidence Sets for the Matrix Completion Problem;
 1 Introduction;
 2 Notation, Assumptions, and Some Basic Results;
 3 A Nonasymptotic Confidence Set for the Matrix Completion Problem;
 4 Technical Lemmas; References; A Nonparametric Classification Algorithm Based on Optimized Templates;
 1 Introduction;
 2 Methods; 2.1 Optimization Criterion; 2.2 Optimizing Templates;
 3 Results; 3.1 Description of the Data
 3.2 Locating the Mouth: Initial Results3.3 Optimal Mouth Template;
 4 Discussion;
 5 Conclusion; Reference; PACBayesian Aggregation of Affine Estimators;
 1 Introduction;
 2 Framework and Estimate;
 3 Penalization Strategies and Preliminary Results;
 4 A General Oracle Inequality; References; Light and HeavyTailed Density Estimation by GammaWeibullKernel;
 1 Introduction; 1.1 Gamma Kernel;
 2 GammaWeibull Kernel;
 3 Convergence Rate of the Density Estimator; 3.1 The Optimal Bandwidth Parameters for the Density Estimator;
 4 Conclusion; Appendix; Proof of Lemma 1; Proof of Lemma 2; References
 Denis, Daniel J., 1974 author.
 Hoboken, NJ : Wiley, 2019.
 Description
 Book — 1 online resource (x, 205 pages)
 Summary

 Preface ix
 1 Review of Essential Statistical Principles
 1 1.1 Variables and Types of Data
 2 1.2 Significance Tests and Hypothesis Testing
 3 1.3 Significance Levels and Type I and Type II Errors
 4 1.4 Sample Size and Power
 5 1.5 Model Assumptions
 6
 2 Introduction to SPSS
 9 2.1 How to Communicate with SPSS
 9 2.2 Data View vs. Variable View
 10 2.3 Missing Data in SPSS: Think Twice Before Replacing Data!
 12
 3 Exploratory Data Analysis, Basic Statistics, and Visual Displays
 19 3.1 Frequencies and Descriptives
 19 3.2 The Explore Function
 23 3.3 What Should I Do with Outliers? Delete or Keep Them?
 28 3.4 Data Transformations
 29
 4 Data Management in SPSS
 33 4.1 Computing a New Variable
 33 4.2 Selecting Cases
 34 4.3 Recoding Variables into Same or Different Variables
 36 4.4 Sort Cases
 37 4.5 Transposing Data
 38
 5 Inferential Tests on Correlations, Counts, and Means
 41 5.1 Computing zScores in SPSS
 41 5.2 Correlation Coefficients
 44 5.3 A Measure of Reliability: Cohen's Kappa
 52 5.4 Binomial Tests
 52 5.5 Chisquare Goodnessoffit Test
 54 5.6 Onesample tTest for a Mean
 57 5.7 Twosample tTest for Means
 59
 6 Power Analysis and Estimating Sample Size
 63 6.1 Example Using G*Power: Estimating Required Sample Size for Detecting Population Correlation
 64 6.2 Power for Chisquare Goodness of Fit
 66 6.3 Power for Independentsamples tTest
 66 6.4 Power for Pairedsamples tTest
 67
 7 Analysis of Variance: Fixed and Random Effects
 69 7.1 Performing the ANOVA in SPSS
 70 7.2 The FTest for ANOVA
 73 7.3 Effect Size
 74 7.4 Contrasts and Post Hoc Tests on Teacher
 75 7.5 Alternative Post Hoc Tests and Comparisons
 78 7.6 Random Effects ANOVA
 80 7.7 Fixed Effects Factorial ANOVA and Interactions
 82 7.8 What Would the Absence of an Interaction Look Like?
 86 7.9 Simple Main Effects
 86 7.10 Analysis of Covariance (ANCOVA)
 88 7.11 Power for Analysis of Variance
 90
 8 Repeated Measures ANOVA
 91 8.1 Oneway Repeated Measures
 91 8.2 Twoway Repeated Measures: One Between and One Within Factor
 99
 9 Simple and Multiple Linear Regression
 103 9.1 Example of Simple Linear Regression
 103 9.2 Interpreting a Simple Linear Regression: Overview of Output
 105 9.3 Multiple Regression Analysis
 107 9.4 Scatterplot Matrix
 111 9.5 Running the Multiple Regression
 112 9.6 Approaches to Model Building in Regression
 118 9.7 Forward, Backward, and Stepwise Regression
 120 9.8 Interactions in Multiple Regression
 121 9.9 Residuals and Residual Plots: Evaluating Assumptions
 123 9.10 Homoscedasticity Assumption and Patterns of Residuals
 125 9.11 Detecting Multivariate Outliers and Influential Observations
 126 9.12 Mediation Analysis
 127 9.13 Power for Regression
 129
 10 Logistic Regression
 131 10.1 Example of Logistic Regression
 132 10.2 Multiple Logistic Regression
 138 10.3 Power for Logistic Regression
 139
 11 Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis
 141 11.1 Example of MANOVA
 142 11.2 Effect Sizes
 146 11.3 Box's M Test
 147 11.4 Discriminant Function Analysis
 148 11.5 Equality of Covariance Matrices Assumption
 152 11.6 MANOVA and Discriminant Analysis on Three Populations
 153 11.7 Classification Statistics
 159 11.8 Visualizing Results
 161 11.9 Power Analysis for MANOVA
 162
 12 Principal Components Analysis
 163 12.1 Example of PCA
 163 12.2 Pearson's
 1901 Data
 164 12.3 Component Scores
 166 12.4 Visualizing Principal Components
 167 12.5 PCA of Correlation Matrix
 170
 13 Exploratory Factor Analysis
 175 13.1 The Common Factor Analysis Model
 175 13.2 The Problem with Exploratory Factor Analysis
 176 13.3 Factor Analysis of the PCA Data
 176 13.4 What Do We Conclude from the Factor Analysis?
 179 13.5 Scree Plot
 180 13.6 Rotating the Factor Solution
 181 13.7 Is There Sufficient Correlation to Do the Factor Analysis?
 182 13.8 Reproducing the Correlation Matrix
 183 13.9 Cluster Analysis
 184 13.10 How to Validate Clusters?
 187 13.11 Hierarchical Cluster Analysis
 188
 14 Nonparametric Tests
 191 14.1 Independent samples: MannWhitney U
 192 14.2 Multiple Independentsamples: KruskalWallis Test
 193 14.3 Repeated Measures Data: The Wilcoxon Signedrank Test and Friedman Test
 194 14.4 The Sign Test
 196 Closing Remarks and Next Steps
 199 References
 201 Index 203.
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 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  Unavailable In process Request 
11. 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 
12. 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 
 Brown, Jonathon D., author.
 Cham, Switzerland : Springer, [2018]
 Description
 Book — 1 online resource Digital: text file; PDF.
 Summary

 Linear Equations. Least Squares Estimation. Linear Regression. Eigen Decomposition. Singular Value Decomposition. Generalized Least Squares Estimation. Robust Regression. Model Selection and Biased Estimation. Cubic Splines and Additive Models. Nonlinear Regression and Optimization. Generalized Linear Models. Survival Analysis. Time Series Analysis. Mixed Effects Models.
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 Workshop "Advances in Growth Curve and Structural Equation Modeling" (2017 : Giridh, India)
 Singapore : Springer, [2018]
 Description
 Book — 1 online resource (x, 202 pages) Digital: text file; PDF.
 Summary

 Mahalanobis Distance and Longitudinal Growth. Optimum Designs for Pharmaceutical Experiments with Relational Constraints on the Mixing Components. Evidence from Granger Causality and Cointegration Tests. Growth Curve of Socioeconomic Development in North Eastern Tribes. Growth and Nutritional Status of Preschool Children in India. Bootstrap of Deviation Probabilities with Applications. Mathematical Aptitude and Family Income in North Eastern Tribes.
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 Singapore : Springer, [2018]
 Description
 Book — 1 online resource (xii, 192 pages) Digital: text file; PDF.
 Summary

 Mahalanobis Distance and Longitudinal Growth. Optimum Designs for Pharmaceutical Experiments with Relational Constraints on the Mixing Components. Evidence from Granger Causality and Cointegration Tests. Growth Curve of Socioeconomic Development in North Eastern Tribes. Growth and Nutritional Status of Preschool Children in India. Bootstrap of Deviation Probabilities with Applications. Mathematical Aptitude and Family Income in North Eastern Tribes.
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16. Advances in the Mathematical Sciences : AWM Research Symposium, Los Angeles, CA, April 2017 [2018]
 Association for Women in Mathematics (U.S.). Research Symposium (2017 : Los Angeles, CA)
 Cham : Springer, 2018.
 Description
 Book — 1 online resource. Digital: text file; PDF.
 Summary

 Charney, R: Searching for Hyperbolicity. Im, M. S. and Wu, A.: Generalized Iterated Wreath Products of Cyclic Groups and Rooted Trees Correspondence. Im, M. S. and Wu, A.: Generalized Iterated Wreath Products of Symmetric Groups and Generalized Rooted Trees Correspondence. Baur, K. et al: ConwayCoxeter Friezes and Mutations: a Survey. Buell, C. et al: Orbit Decompositions of Unipotent Elements in the Generalized Symmetric Spaces of SL2(Fq). Vincent, C.: A Characterization of the U(Omega, m) Sets of a Hyperelliptic Curve as Omega and m Vary. Osborne, C. and Tebbe, A.: A First Step Toward Higher Order Chain Rules in Abelian Functor. Jones, G. and Price, C.: DNA Topology Review. Simens, J. et al: Structural Identifiability Analysis of a Labeled Oral Minimal Model for Quantifying Hepatic Insulin Resistance. Riviere, P. and Rangel, L.: Spikefield Coherence and Firing Rate Profiles of CA1 Interneurons During an Associative Memory Task. Malerba, P. and Tsimiring, K. and Bazhenov, M.: Learninginduced Sequence Reactivation During Sharpwave Ripples: a Computational Study. Riviere, B. and Yang, X.: A DG Method for the Simulation of CO2 Storage in Saline Aquifer. Hetrick, B. C.: Regularization Results for Inhomogeneous Illposed Problems in Banach Space. Hauk, S. et al: Research in Collegiate Mathematics Education. Author Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
17. 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.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA276 .S8945 2018  Unknown 
18. Attraction in numerical minimization : iteration mappings, attractors, and basins of attraction [2018]
 Levy, Adam B.
 Cham, Switzerland : Springer, 2018.
 Description
 Book — 1 online resource.
 Summary

 Multisets and multiset mappings
 Iteration mappings
 Equilibria in dynamical systems
 Attractors
 Basin analysis via simulation.
 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.
(source: Nielsen Book Data)
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA278.2 .R67 2018  Unknown 
 Cham, Switzerland : Springer, 2018.
 Description
 Book — 1 online resource (xvii, 242 pages) : illustrations (some color).
 Summary

 Rank Properties for Centred Threeway Arrays  C. Albers (Univ. of Groningen) et al. Principal Component Analysis of Complex Data and Application to Climatology  S. Camiz (La Sapienza Univ. of Rome) et al. Clustering upper level units in multilevel models for ordinal data  L. Grilli (Univ. of Florence) et al. A Multilevel Heckman Model To Investigate Financial Assets Among Old People In Europe  O. Paccagnella (univ. of Padua) et al. Multivariate stochastic downscaling with semicontinuous data  L. Paci (univ. of Bologna) et al. Motivations and expectations of students' mobility abroad: a mapping technique  V. Caviezel (Univ. of Bergamo) et al. Comparing multistep ahead forecasting functions for time series clustering  M. Corduas (Univ. of Naples Federico II) et al. Electre TriMachine Learning Approach to the Record Linkage  V. Minnetti (La Sapienza Univ. of Rome) et al. . MCA Based Community Detection  C. Drago (Univ. of Rome Niccolo Cusano). Classi fying social roles by network structures  S. Gozzo (univ. of Catania) et al. Bayesian Networks For Financial Markets Signals Detection  A. Greppi (univ.of Pavia) et al. Finite sample behaviour of MLE in network autocorrelation models  M. La Rocca (Univ. of Salerno) et al. Classification Models as Tools of Bankruptcy Prediction  Polish Experience  J. Pochiecha (Cracow university) et al. Clustering macroseismic fields by statistical data depth functions  C. Agostinelli (Univ. of Trento). Depth based tests for circular antipodal symmetry  G. Pandolfo (Univ. of Cassino) et al. Estimating The Effect Of Prenatal Care On Birth Outcomes  E. Sironi (Sacro Cuore University) et al. Bifurcations And Sunspots In Continuous Time Optimal Models With Externalities  B.Venturi (Univ. of Cagliari) et al. Enhancing Big Data Exploration with Faceted Browsing  S. Bergamaschi (Univ. of Modena and Reggio Emilia) et al. Big data meet pharmaceutical industry: an application on social media data  C. Liberati (Univ. of Milan Bicocca) et al. From Big Data to information: statistical issues through a case study  S. Signorelli (Univ. of Bergamo) et al. Quality of Classification approaches for the quantitative analysis of international conflict  A.F.X. Wilhelm (Jacobs Univ. Bremen). Psplines based clustering as a general framework: some applications using different clustering algorithms  C. Iorio (Univ. of Naples Federico II) et al. A graphical copulabased tool for detecting tail dependence  R. Pappada (univ. of Trieste) et al. Comparing spatial and spatiotemporal FPCA to impute large continuous gaps in space  M. Ruggeri (Univ. of Palermo) et al. Exploring Italian students' performances in the SNV test: a quantile regression perspective  A. Costanzo (National Institute for the Evaluation of Education and Training  INVALSI) et al.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)