09462nam 2200553 450 991055481680332120211215184140.01-5231-4320-71-119-57818-31-119-57820-51-119-57817-5(CKB)4100000011920518(MiAaPQ)EBC6579924(Au-PeEL)EBL6579924(OCoLC)1250084608(EXLCZ)99410000001192051820211215d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierApplied univariate, bivariate, and multivariate statistics using Python /Daniel J. DenisHoboken, New Jersey :John Wiley & Sons,[2021]©20211 online resource (300 pages)1-119-57814-0 Includes bibliographical references and index.Cover -- Title Page -- Copyright Page -- Contents -- Preface -- 1. A Brief Introduction and Overview of Applied Statistics -- 1.1 How Statistical Inference Works -- 1.2 Statistics and Decision-Making -- 1.3 Quantifying Error Rates in Decision-Making: Type I and Type II Errors -- 1.4 Estimation of Parameters -- 1.5 Essential Philosophical Principles for Applied Statistics -- 1.6 Continuous vs. Discrete Variables -- 1.6.1 Continuity Is Not Always Clear-Cut -- 1.7 Using Abstract Systems to Describe Physical Phenomena: Understanding Numerical vs. Physical Differences -- 1.8 Data Analysis, Data Science, Machine Learning, Big Data -- 1.9 "Training" and "Testing" Models: What "Statistical Learning" Means in the Age of Machine Learning and Data Science -- 1.10 Where We Are Going From Here: How to Use This Book -- Review Exercises -- 2. Introduction to Python and the Field of Computational Statistics -- 2.1 The Importance of Specializing in Statistics and Research, Not Python: Advice for Prioritizing Your Hierarchy -- 2.2 How to Obtain Python -- 2.3 Python Packages -- 2.4 Installing a New Package in Python -- 2.5 Computing z-Scores in Python -- 2.6 Building a Dataframe in Python: And Computing Some Statistical Functions -- 2.7 Importing a .txt or .csv File -- 2.8 Loading Data into Python -- 2.9 Creating Random Data in Python -- 2.10 Exploring Mathematics in Python -- 2.11 Linear and Matrix Algebra in Python: Mechanics of Statistical Analyses -- 2.11.1 Operations on Matrices -- 2.11.2 Eigenvalues and Eigenvectors -- Review Exercises -- 3. Visualization in Python: Introduction to Graphs and Plots -- 3.1 Aim for Simplicity and Clarity in Tables and Graphs: Complexity is for Fools! -- 3.2 State Population Change Data -- 3.3 What Do the Numbers Tell Us? Clues to Substantive Theory -- 3.4 The Scatterplot -- 3.5 Correlograms -- 3.6 Histograms and Bar Graphs.3.7 Plotting Side-by-Side Histograms -- 3.8 Bubble Plots -- 3.9 Pie Plots -- 3.10 Heatmaps -- 3.11 Line Charts -- 3.12 Closing Thoughts -- Review Exercises -- 4. Simple Statistical Techniques for Univariate and Bivariate Analyses -- 4.1 Pearson Product-Moment Correlation -- 4.2 A Pearson Correlation Does Not (Necessarily) Imply Zero Relationship -- 4.3 Spearman's Rho -- 4.4 More General Comments on Correlation: Don't Let a Correlation Impress You Too Much! -- 4.5 Computing Correlation in Python -- 4.6 T-Tests for Comparing Means -- 4.7 Paired-Samples t-Test in Python -- 4.8 Binomial Test -- 4.9 The Chi-Squared Distribution and Goodness-of-Fit Test -- 4.10 Contingency Tables -- Review Exercises -- 5. Power, Effect Size, P-Values, and Estimating Required Sample Size Using Python -- 5.1 What Determines the Size of a P-Value? -- 5.2 How P-Values Are a Function of Sample Size -- 5.3 What is Effect Size? -- 5.4 Understanding Population Variability in the Context of Experimental Design -- 5.5 Where Does Power Fit into All of This? -- 5.6 Can You Have Too Much Power? Can a Sample Be Too Large? -- 5.7 Demonstrating Power Principles in Python: Estimating Power or Sample Size -- 5.8 Demonstrating the Influence of Effect Size -- 5.9 The Influence of Significance Levels on Statistical Power -- 5.10 What About Power and Hypothesis Testing in the Age of "Big Data"? -- 5.11 Concluding Comments on Power, Effect Size, and Significance Testing -- Review Exercises -- 6. Analysis of Variance -- 6.1 T-Tests for Means as a "Special Case" of ANOVA -- 6.2 Why Not Do Several t-Tests? -- 6.3 Understanding ANOVA Through an Example -- 6.4 Evaluating Assumptions in ANOVA -- 6.5 ANOVA in Python -- 6.6 Effect Size for Teacher -- 6.7 Post-Hoc Tests Following the ANOVA F-Test -- 6.8 A Myriad of Post-Hoc Tests -- 6.9 Factorial ANOVA -- 6.10 Statistical Interactions.6.11 Interactions in the Sample Are a Virtual Guarantee: Interactions in the Population Are Not -- 6.12 Modeling the Interaction Term -- 6.13 Plotting Residuals -- 6.14 Randomized Block Designs and Repeated Measures -- 6.15 Nonparametric Alternatives -- 6.15.1 Revisiting What "Satisfying Assumptions" Means: A Brief Discussion and Suggestion of How to Approach the Decision Regarding Nonparametrics -- 6.15.2 Your Experience in the Area Counts -- 6.15.3 What If Assumptions Are Truly Violated? -- 6.15.4 Mann-Whitney U Test -- 6.15.5 Kruskal-Wallis Test as a Nonparametric Alternative to ANOVA -- Review Exercises -- 7. Simple and Multiple Linear Regression -- 7.1 Why Use Regression? -- 7.2 The Least-Squares Principle -- 7.3 Regression as a "New" Least-Squares Line -- 7.4 The Population Least-Squares Regression Line -- 7.5 How to Estimate Parameters in Regression -- 7.6 How to Assess Goodness of Fit? -- 7.7 R2 - Coefficient of Determination -- 7.8 Adjusted R2 -- 7.9 Regression in Python -- 7.10 Multiple Linear Regression -- 7.11 Defining the Multiple Regression Model -- 7.12 Model Specification Error -- 7.13 Multiple Regression in Python -- 7.14 Model-Building Strategies: Forward, Backward, Stepwise -- 7.15 Computer-Intensive "Algorithmic" Approaches -- 7.16 Which Approach Should You Adopt? -- 7.17 Concluding Remarks and Further Directions: Polynomial Regression -- Review Exercises -- 8. Logistic Regression and the Generalized Linear Model -- 8.1 How Are Variables Best Measured? Are There Ideal Scales on Which a Construct Should Be Targeted? -- 8.2 The Generalized Linear Model -- 8.3 Logistic Regression for Binary Responses: A Special Subclass of the Generalized Linear Model -- 8.4 Logistic Regression in Python -- 8.5 Multiple Logistic Regression -- 8.5.1 A Model with Only Lag1 -- 8.6 Further Directions -- Review Exercises.9. Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis -- 9.1 Why Technically Most Univariate Models are Actually Multivariate -- 9.2 Should I Be Running a Multivariate Model? -- 9.3 The Discriminant Function -- 9.4 Multivariate Tests of Significance: Why They Are Different from the F-Ratio -- 9.4.1 Wilks' Lambda -- 9.4.2 Pillai's Trace -- 9.4.3 Roy's Largest Root -- 9.4.4 Lawley-Hotelling's Trace -- 9.5 Which Multivariate Test to Use? -- 9.6 Performing MANOVA in Python -- 9.7 Effect Size for MANOVA -- 9.8 Linear Discriminant Function Analysis -- 9.9 How Many Discriminant Functions Does One Require? -- 9.10 Discriminant Analysis in Python: Binary Response -- 9.11 Another Example of Discriminant Analysis: Polytomous Classification -- 9.12 Bird's Eye View of MANOVA, ANOVA, Discriminant Analysis, and Regression: A Partial Conceptual Unification -- 9.13 Models "Subsumed" Under the Canonical Correlation Framework -- Review Exercises -- 10. Principal Components Analysis -- 10.1 What Is Principal Components Analysis? -- 10.2 Principal Components as Eigen Decomposition -- 10.3 PCA on Correlation Matrix -- 10.4 Why Icebergs Are Not Good Analogies for PCA -- 10.5 PCA in Python -- 10.6 Loadings in PCA: Making Substantive Sense Out of an Abstract Mathematical Entity -- 10.7 Naming Components Using Loadings: A Few Issues -- 10.8 Principal Components Analysis on USA Arrests Data -- 10.9 Plotting the Components -- Review Exercises -- 11. Exploratory Factor Analysis -- 11.1 The Common Factor Analysis Model -- 11.2 Factor Analysis as a Reproduction of the Covariance Matrix -- 11.3 Observed vs. Latent Variables: Philosophical Considerations -- 11.4 So, Why is Factor Analysis Controversial? The Philosophical Pitfalls of Factor Analysis -- 11.5 Exploratory Factor Analysis in Python -- 11.6 Exploratory Factor Analysis on USA Arrests Data.Review Exercises -- 12. Cluster Analysis -- 12.1 Cluster Analysis vs. ANOVA vs. Discriminant Analysis -- 12.2 How Cluster Analysis Defines "Proximity" -- 12.2.1 Euclidean Distance -- 12.3 K-Means Clustering Algorithm -- 12.4 To Standardize or Not? -- 12.5 Cluster Analysis in Python -- 12.6 Hierarchical Clustering -- 12.7 Hierarchical Clustering in Python -- Review Exercises -- References -- Index -- EULA.StatisticsSoftwareMultivariate analysisPython (Computer program language)Electronic books.StatisticsMultivariate analysis.Python (Computer program language)519.5302855133Denis Daniel J.1974-1146666MiAaPQMiAaPQMiAaPQBOOK9910554816803321Applied univariate, bivariate, and multivariate statistics using Python2818659UNINA03342nam 22005655 450 99641831240331620230330060405.03-030-61588-X10.1007/978-3-030-61588-8(CKB)4100000011515518(DE-He213)978-3-030-61588-8(MiAaPQ)EBC6380835(PPN)254727093(EXLCZ)99410000001151551820201017d2020 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierCellular Automata and Discrete Complex Systems[electronic resource] 26th IFIP WG 1.5 International Workshop, AUTOMATA 2020, Stockholm, Sweden, August 10–12, 2020, Proceedings /edited by Hector Zenil1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (XXV, 153 p. 64 illus., 5 illus. in color.) Theoretical Computer Science and General Issues,2512-2029 ;122863-030-61587-1 Exploring Millions of 6-State FSSP Solutions: the Formal Notion of Local CA Simulation -- Non-maximal sensitivity to synchronism in periodic elementary cellular automata: exact asymptotic measures -- Cycle based Clustering using Reversible Cellular Automata -- Commutative automata networks -- Cellular String Generators -- Everywhere Zero Pointwise Lyapunov Exponents for Sensitive Cellular Automata -- Self-Stabilizing Distributed Algorithms by Gellular Automata -- A characterization of amenable groups with Besicovitch pseudodistances -- Four heads are better than three -- Complexity of Generic Limit Sets of Cellular Automata -- Latin Hypercubes and Cellular Automata.This volume constitutes the refereed post-conference proceedings of the 26th IFIP WG 1.5 International Workshop on Cellular Automata and Discrete Complex Systems, AUTOMATA 2020, held in Stockholm, Sweden, in August 2020. The workshop was held virtually. The 11 full papers presented in this book were carefully reviewed and selected from a total of 21 submissions. The topics of the conference include dynamical, topological, ergodic and algebraic aspects of CA and DCS, algorithmic and complexity issues, emergent properties, formal languages, symbolic dynamics, tilings, models of parallelism and distributed systems, timing schemes, synchronous versus asynchronous models, phenomenological descriptions, scientific modeling, and practical applications.Theoretical Computer Science and General Issues,2512-2029 ;12286Computer scienceArtificial intelligenceDatabase managementTheory of ComputationArtificial IntelligenceDatabase ManagementComputer science.Artificial intelligence.Database management.Theory of Computation.Artificial Intelligence.Database Management.511.3Zenil HectorMiAaPQMiAaPQMiAaPQBOOK996418312403316Cellular automata and discrete complex systems2126326UNISA