top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Autore Denis Daniel J. <1974->
Edizione [Second edition.]
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021]
Descrizione fisica 1 online resource (576 pages) : illustrations
Disciplina 519.53
Soggetto topico Analysis of variance
Multivariate analysis
Soggetto genere / forma Electronic books.
ISBN 1-119-58301-2
1-119-58300-4
1-119-58302-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto COVER -- TITLE PAGE -- COPYRIGHT PAGE -- CONTENTS -- PREFACE -- ABOUT THE COMPANION WEBSITE -- CHAPTER 1 PRELIMINARY CONSIDERATIONS -- 1.1 THE PHILOSOPHICAL BASES OF KNOWLEDGE: RATIONALISTIC VERSUS EMPIRICIST PURSUITS -- 1.2 WHAT IS A "MODEL"? -- 1.3 SOCIAL SCIENCES VERSUS HARD SCIENCES -- 1.4 IS COMPLEXITY A GOOD DEPICTION OF REALITY? ARE MULTIVARIATE METHODS USEFUL? -- 1.5 CAUSALITY -- 1.6 THE NATURE OF MATHEMATICS: MATHEMATICS AS A REPRESENTATION OF CONCEPTS -- 1.7 AS A SCIENTIST, HOW MUCH MATHEMATICS DO YOU NEED TO KNOW? -- 1.8 STATISTICS AND RELATIVITY -- 1.9 EXPERIMENTAL VERSUS STATISTICAL CONTROL -- 1.10 STATISTICAL VERSUS PHYSICAL EFFECTS -- 1.11 UNDERSTANDING WHAT "APPLIED STATISTICS" MEANS -- REVIEW EXERCISES -- FURTHER DISCUSSION AND ACTIVITIES -- CHAPTER 2 INTRODUCTORY STATISTICS -- 2.1 DENSITIES AND DISTRIBUTIONS -- 2.1.1 Plotting Normal Distributions -- 2.1.2 Binomial Distributions -- 2.1.3 Normal Approximation -- 2.1.4 Joint Probability Densities: Bivariate and Multivariate Distributions -- 2.2 CHI-SQUARE DISTRIBUTIONS AND GOODNESS-OF-FIT TEST -- 2.2.1 Power for Chi-Square Test of Independence -- 2.3 SENSITIVITY AND SPECIFICITY -- 2.4 SCALES OF MEASUREMENT: NOMINAL, ORDINAL, INTERVAL, RATIO -- 2.4.1 Nominal Scale -- 2.4.2 Ordinal Scale -- 2.4.3 Interval Scale -- 2.4.4 Ratio Scale -- 2.5 MATHEMATICAL VARIABLES VERSUS RANDOM VARIABLES -- 2.6 MOMENTS AND EXPECTATIONS -- 2.6.1 Sample and Population Mean Vectors -- 2.7 ESTIMATION AND ESTIMATORS -- 2.8 VARIANCE -- 2.9 DEGREES OF FREEDOM -- 2.10 SKEWNESS AND KURTOSIS -- 2.11 SAMPLING DISTRIBUTIONS -- 2.11.1 Sampling Distribution of the Mean -- 2.12 CENTRAL LIMIT THEOREM -- 2.13 CONFIDENCE INTERVALS -- 2.14 MAXIMUM LIKELIHOOD -- 2.15 AKAIKE'S INFORMATION CRITERIA -- 2.16 COVARIANCE AND CORRELATION -- 2.17 PSYCHOMETRIC VALIDITY, RELIABILITY: A COMMON USE OF CORRELATION COEFFICIENTS.
2.18 COVARIANCE AND CORRELATION MATRICES -- 2.19 OTHER CORRELATION COEFFICIENTS -- 2.20 STUDENT'S t DISTRIBUTION -- 2.20.1 t-Tests for One Sample -- 2.20.2 t-Tests for Two Samples -- 2.20.3 Two-Sample t-Tests in R -- 2.21 STATISTICAL POWER -- 2.21.1 Visualizing Power -- 2.22 POWER ESTIMATION USING R AND G*POWER -- 2.22.1 Estimating Sample Size and Power for Independent Samples t-Test -- 2.23 PAIRED-SAMPLES t-TEST: STATISTICAL TEST FOR MATCHED-PAIRS (ELEMENTARY BLOCKING) DESIGNS -- 2.24 BLOCKING WITH SEVERAL CONDITIONS -- 2.25 COMPOSITE VARIABLES: LINEAR COMBINATIONS -- 2.26 MODELS IN MATRIX FORM -- 2.27 GRAPHICAL APPROACHES -- 2.27.1 Box-and-Whisker Plots -- 2.28 WHAT MAKES A p-VALUE SMALL? A CRITICAL OVERVIEW AND PRACTICAL DEMONSTRATION OF NULL HYPOTHESIS SIGNIFICANCE TESTING -- 2.28.1 Null Hypothesis Significance Testing (NHST): A Legacy of Criticism -- 2.28.2 The Make-Up of a p-Value: A Brief Recap and Summary -- 2.28.3 The Issue of Standardized Testing: Are Students in Your School Achieving More Than the National Average? -- 2.28.4 Other Test Statistics -- 2.28.5 The Solution -- 2.28.6 Statistical Distance: Cohen's d -- 2.28.7 What Does Cohen's d Actually Tell Us? -- 2.28.8 Why and Where the Significance Test Still Makes Sense -- 2.29 CHAPTER SUMMARY AND HIGHLIGHTS -- REVIEW EXERCISES -- FURTHER DISCUSSION AND ACTIVITIES -- CHAPTER 3 ANALYSIS OF VARIANCE: FIXED EFFECTS MODELS -- 3.1 WHAT IS ANALYSIS OF VARIANCE? FIXED VERSUS RANDOM EFFECTS -- 3.1.1 Small Sample Example: Achievement as a Function of Teacher -- 3.1.2 Is Achievement a Function of Teacher? -- 3.2 HOW ANALYSIS OF VARIANCE WORKS: A BIG PICTURE OVERVIEW -- 3.2.1 Is the Observed Difference Likely? ANOVA as a Comparison (Ratio) of Variances -- 3.3 LOGIC AND THEORY OF ANOVA: A DEEPER LOOK -- 3.3.1 Independent-Samples t-Tests Versus Analysis of Variance.
3.3.2 The ANOVA Model: Explaining Variation -- 3.3.3 Breaking Down a Deviation -- 3.3.4 Naming the Deviations -- 3.3.5 The Sums of Squares of ANOVA -- 3.4 FROM SUMS OF SQUARES TO UNBIASED VARIANCE ESTIMATORS: DIVIDING BY DEGREES OF FREEDOM -- 3.5 EXPECTED MEAN SQUARES FOR ONE-WAY FIXED EFFECTS MODEL: DERIVING THE F-RATIO -- 3.6 THE NULL HYPOTHESIS IN ANOVA -- 3.7 FIXED EFFECTS ANOVA: MODEL ASSUMPTIONS -- 3.8 A WORD ON EXPERIMENTAL DESIGN AND RANDOMIZATION -- 3.9 A PREVIEW OF THE CONCEPT OF NESTING -- 3.10 BALANCED VERSUS UNBALANCED DATA IN ANOVA MODELS -- 3.11 MEASURES OF ASSOCIATION AND EFFECT SIZE IN ANOVA: MEASURES OF VARIANCE EXPLAINED -- 3.11.1 .2 Eta-Squared -- 3.11.2 Omega-Squared -- 3.12 THE F-TEST AND THE INDEPENDENT SAMPLES t-TEST -- 3.13 CONTRASTS AND POST-HOCS -- 3.13.1 Independence of Contrasts -- 3.13.2 Independent Samples t-Test as a Linear Contrast -- 3.14 POST-HOC TESTS -- 3.14.1 Newman-Keuls and Tukey HSD -- 3.14.2 Tukey HSD -- 3.14.3 Scheffé Test -- 3.14.4 Other Post-Hoc Tests -- 3.14.5 Contrast versus Post-Hoc? Which Should I Be Doing? -- 3.15 SAMPLE SIZE AND POWER FOR ANOVA: ESTIMATION WITH R AND G*POWER -- 3.15.1 Power for ANOVA in R and G*Power -- 3.15.2 Computing f -- 3.16 FIXED EFFECTS ONE-WAY ANALYSIS OF VARIANCE IN R: MATHEMATICS ACHIEVEMENT AS A FUNCTION OF TEACHER -- 3.16.1 Evaluating Assumptions -- 3.16.2 Post-Hoc Tests on Teacher -- 3.17 ANALYSIS OF VARIANCE VIA R´s lm -- 3.18 KRUSKAL-WALLIS TEST IN R AND THE MOTIVATION BEHIND NONPARAMETRIC TESTS -- 3.19 ANOVA IN SPSS: ACHIEVEMENT AS A FUNCTION OF TEACHER -- 3.20 CHAPTER SUMMARY AND HIGHLIGHTS -- REVIEW EXERCISES -- FURTHER DISCUSSION AND ACTIVITIES -- CHAPTER 4 FACTORIAL ANALYSIS OF VARIANCE -- 4.1 WHAT IS FACTORIAL ANALYSIS OF VARIANCE? -- 4.2 THEORY OF FACTORIAL ANOVA: A DEEPER LOOK -- 4.2.1 Deriving the Model for Two-Way Factorial ANOVA -- 4.2.2 Cell Effects.
4.2.3 Interaction Effects -- 4.2.4 Cell Effects Versus Interaction Effects -- 4.2.5 A Model for the Two-Way Fixed Effects ANOVA -- 4.3 COMPARING ONE-WAY ANOVA TO TWO-WAY ANOVA: CELL EFFECTS IN FACTORIAL ANOVA VERSUS SAMPLE EFFECTS IN ONE-WAY ANOVA -- 4.4 PARTITIONING THE SUMS OF SQUARES FOR FACTORIAL ANOVA: THE CASE OF TWO FACTORS -- 4.4.1 SS Total: A Measure of Total Variation -- 4.4.2 Model Assumptions: Two-Way Factorial Model -- 4.4.3 Expected Mean Squares for Factorial Design -- 4.4.4 Recap of Expected Mean Squares -- 4.5 INTERPRETING MAIN EFFECTS IN THE PRESENCE OF INTERACTIONS -- 4.6 EFFECT SIZE MEASURES -- 4.7 THREE-WAY, FOUR-WAY, AND HIGHER MODELS -- 4.8 SIMPLE MAIN EFFECTS -- 4.9 NESTED DESIGNS -- 4.9.1 Varieties of Nesting: Nesting of Levels Versus Subjects -- 4.10 ACHIEVEMENT AS A FUNCTION OF TEACHER AND TEXTBOOK: EXAMPLE OF FACTORIAL ANOVA IN R -- 4.10.1 Comparing Models Through AIC -- 4.10.2 Visualizing Main Effects and Interaction Effects Simultaneously -- 4.10.3 Simple Main Effects for Achievement Data: Breaking Down Interaction Effects -- 4.11 INTERACTION CONTRASTS -- 4.12 CHAPTER SUMMARY AND HIGHLIGHTS -- REVIEW EXERCISES -- CHAPTER 5 INTRODUCTION TO RANDOM EFFECTS AND MIXED MODELS -- 5.1 WHAT IS RANDOM EFFECTS ANALYSIS OF VARIANCE? -- 5.2 THEORY OF RANDOM EFFECTS MODELS -- 5.3 ESTIMATION IN RANDOM EFFECTS MODELS -- 5.3.1 Transitioning from Fixed Effects to Random Effects -- 5.3.2 Expected Mean Squares for MS Between and MS Within -- 5.4 DEFINING NULL HYPOTHESES IN RANDOM EFFECTS MODELS -- 5.4.1 F-Ratio for Testing H0 -- 5.5 COMPARING NULL HYPOTHESES IN FIXED VERSUS RANDOM EFFECTS MODELS: THE IMPORTANCE OF ASSUMPTIONS -- 5.6 ESTIMATING VARIANCE COMPONENTS IN RANDOM EFFECTS MODELS: ANOVA, ML, REML ESTIMATORS -- 5.6.1 ANOVA Estimators of Variance Components -- 5.6.2 Maximum Likelihood and Restricted Maximum Likelihood.
5.7 IS ACHIEVEMENT A FUNCTION OF TEACHER? ONE-WAY RANDOM EFFECTS MODEL IN R -- 5.7.1 Proportion of Variance Accounted for by Teacher -- 5.8 R ANALYSIS USING REML -- 5.9 ANALYSIS IN SPSS: OBTAINING VARIANCE COMPONENTS -- 5.10 Factorial Random Effects: A Two-Way Model -- 5.11 FIXED EFFECTS VERSUS RANDOM EFFECTS: A WAY OF CONCEPTUALIZING THEIR DIFFERENCES -- 5.12 CONCEPTUALIZING THE TWO-WAY RANDOM EFFECTS MODEL: THE MAKE-UP OF A RANDOMLY CHOSEN OBSERVATION -- 5.13 SUMS OF SQUARES AND EXPECTED MEAN SQUARES FOR RANDOM EFFECTS: THE CONTAMINATING INFLUENCE OF INTERACTION EFFECTS -- 5.13.1 Testing Null Hypotheses -- 5.14 YOU GET WHAT YOU GO IN WITH: THE IMPORTANCE OF MODEL ASSUMPTIONS AND MODEL SELECTION -- 5.15 MIXED MODEL ANALYSIS OF VARIANCE: INCORPORATING FIXED AND RANDOM EFFECTS -- 5.15.1 Mixed Model in R -- 5.16 MIXED MODELS IN MATRICES -- 5.17 MULTILEVEL MODELING AS A SPECIAL CASE OF THE MIXED MODEL: INCORPORATING NESTING AND CLUSTERING -- 5.18 CHAPTER SUMMARY AND HIGHLIGHTS -- REVIEW EXERCISES -- CHAPTER 6 RANDOMIZED BLOCKS AND REPEATED MEASURES -- 6.1 WHAT IS A RANDOMIZED BLOCK DESIGN? -- 6.2 RANDOMIZED BLOCK DESIGNS: SUBJECTS NESTED WITHIN BLOCKS -- 6.3 THEORY OF RANDOMIZED BLOCK DESIGNS -- 6.3.1 Nonadditive Randomized Block Design -- 6.3.2 Additive Randomized Block Design -- 6.4 TUKEY TEST FOR NONADDITIVITY -- 6.5 ASSUMPTIONS FOR THE COVARIANCE MATRIX -- 6.6 INTRACLASS CORRELATION -- 6.7 REPEATED MEASURES MODELS: A SPECIAL CASE OF RANDOMIZED BLOCK DESIGNS -- 6.8 INDEPENDENT VERSUS PAIRED-SAMPLES t-TEST -- 6.9 THE SUBJECT FACTOR: FIXED OR RANDOM EFFECT? -- 6.10 MODEL FOR ONE-WAY REPEATED MEASURES DESIGN -- 6.10.1 Expected Mean Squares for Repeated Measures Models -- 6.11 ANALYSIS USING R: ONE-WAY REPEATED MEASURES: LEARNING AS A FUNCTION OF TRIAL -- 6.12 ANALYSIS USING SPSS: ONE-WAY REPEATED MEASURES: LEARNING AS A FUNCTION OF TRIAL.
6.12.1 Which Results Should Be Interpreted?.
Record Nr. UNINA-9910555019303321
Denis Daniel J. <1974->  
Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied univariate, bivariate, and multivariate statistics : understanding statistics for social and natural scientists, with applications in SPSS and R / / Daniel J. Denis
Applied univariate, bivariate, and multivariate statistics : understanding statistics for social and natural scientists, with applications in SPSS and R / / Daniel J. Denis
Autore Denis Daniel J. <1974->
Edizione [Second edition.]
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021]
Descrizione fisica 1 online resource (576 pages) : illustrations
Disciplina 519.53
Soggetto topico Analysis of variance
Multivariate analysis
ISBN 1-119-58301-2
1-119-58300-4
1-119-58302-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preliminary considerations -- Introductory statistics -- Analysis of variance : fixed effects models -- Factorial analysis of variance : modeling interactions -- Introduction to random effects and mixed models -- Randomized blocks and repeated measures -- Linear regression -- Multiple linear regression -- Interactions in multiple linear regression : dichotomous, polytomous, and continuous moderators -- Logistic regression and the generalized linear model -- Multivariate analysis of variance -- Discriminant analysis -- Principal components analysis -- Factor analysis -- Path analysis and structural equation modeling.
Record Nr. UNINA-9910830351603321
Denis Daniel J. <1974->  
Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Autore Denis Daniel J. <1974->
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2016]
Descrizione fisica 1 online resource (763 pages) : illustrations
Disciplina 519.5/3
Soggetto topico Analysis of variance
Multivariate analysis
Soggetto genere / forma Electronic books.
ISBN 1-118-63223-0
1-118-63231-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910460626803321
Denis Daniel J. <1974->  
Hoboken, New Jersey : , : Wiley, , [2016]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Autore Denis Daniel J. <1974->
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2016]
Descrizione fisica 1 online resource (763 pages) : illustrations
Disciplina 519.5/3
Soggetto topico Analysis of variance
Multivariate analysis
ISBN 1118632230
9781118632239
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910796096703321
Denis Daniel J. <1974->  
Hoboken, New Jersey : , : Wiley, , [2016]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Applied univariate, bivariate, and multivariate statistics / / Daniel J. Denis
Autore Denis Daniel J. <1974->
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , [2016]
Descrizione fisica 1 online resource (763 pages) : illustrations
Disciplina 519.5/3
Soggetto topico Analysis of variance
Multivariate analysis
ISBN 1118632230
9781118632239
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910808575603321
Denis Daniel J. <1974->  
Hoboken, New Jersey : , : Wiley, , [2016]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied univariate, bivariate, and multivariate statistics using Python / / Daniel J. Denis
Applied univariate, bivariate, and multivariate statistics using Python / / Daniel J. Denis
Autore Denis Daniel J. <1974->
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, , [2021]
Descrizione fisica 1 online resource (300 pages)
Disciplina 519.5302855133
Soggetto topico Statistics
Multivariate analysis
Python (Computer program language)
Soggetto genere / forma Electronic books.
ISBN 1-5231-4320-7
1-119-57818-3
1-119-57820-5
1-119-57817-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNINA-9910554816803321
Denis Daniel J. <1974->  
Hoboken, New Jersey : , : John Wiley & Sons, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied univariate, bivariate, and multivariate statistics using Python : a beginner's guide to advanced data analysis / / Daniel J. Denis
Applied univariate, bivariate, and multivariate statistics using Python : a beginner's guide to advanced data analysis / / Daniel J. Denis
Autore Denis Daniel J. <1974->
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, , [2021]
Descrizione fisica 1 online resource (300 pages)
Disciplina 519.5302855133
Soggetto topico Statistics
Multivariate analysis
Python (Computer program language)
ISBN 1-5231-4320-7
1-119-57818-3
1-119-57820-5
1-119-57817-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNINA-9910830622503321
Denis Daniel J. <1974->  
Hoboken, New Jersey : , : John Wiley & Sons, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Univariate, bivariate, and multivariate statistics using R : quantitative tools for data analysis and data science / / Daniel J. Denis
Univariate, bivariate, and multivariate statistics using R : quantitative tools for data analysis and data science / / Daniel J. Denis
Autore Denis Daniel J. <1974->
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2020
Descrizione fisica 1 online resource (287 pages)
Disciplina 519.53
Soggetto topico Analysis of variance
Multivariate analysis
Mathematical statistics - Data processing
R (Computer program language)
Anàlisi de variància
Anàlisi multivariable
Estadística matemàtica
Processament de dades
Soggetto genere / forma Llibres electrònics
ISBN 1-119-54991-4
1-119-54996-5
1-119-54995-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction to applied statistics -- Introduction to R and computational statistics -- Exploring data with R : essential graphics and visualization -- Means, correlations, counts : drawing inferences using easy-to-implement statistical tests -- Power analysis and sample size estimation using R -- Analysis of variance : fixed effects, random effects, mixed models and repeated measures -- Simple and multiple linear regression -- Logistic regression and the generalized linear model -- Multivariate analysis of variance (MANOVA) and discriminant analysis -- Principal components analysis -- Exploratory factor analysis -- Cluster analysis -- Nonparametric tests.
Record Nr. UNINA-9910555025503321
Denis Daniel J. <1974->  
Hoboken, New Jersey : , : Wiley, , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui