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.
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 | ||
| ||
Applying and interpreting statistics : a comprehensive guide / Glen McPherson
| Applying and interpreting statistics : a comprehensive guide / Glen McPherson |
| Autore | McPherson, Glen |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | New York : Springer, c2001 |
| Descrizione fisica | xxviii, 640 p. : ill. ; 24 cm |
| Disciplina | 507.2 |
| Altri autori (Persone) | McPherson, Glen.author |
| Collana | Springer texts in statistics |
| Soggetto topico |
Research - Statistical methods
Science - Statistical methods Statistics Statistica - Manuali |
| ISBN | 0387951105 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991001802659707536 |
|
McPherson, Glen
|
||
| New York : Springer, c2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
Applying contemporary statistical techniques [e-book] / Rand R. Wilcox
| Applying contemporary statistical techniques [e-book] / Rand R. Wilcox |
| Autore | Wilcox, Rand R. |
| Pubbl/distr/stampa | Amsterdam ; Boston : Academic Press, c2003 |
| Descrizione fisica | 1 v. (various pagings) : ill. ; 26 cm |
| Disciplina | 519.5 |
| Soggetto topico | Statistics |
| ISBN |
9780127515410
0127515410 |
| Formato | Risorse elettroniche  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991003272069707536 |
|
Wilcox, Rand R.
|
||
| Amsterdam ; Boston : Academic Press, c2003 | ||
| Risorse elettroniche | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
Applying quantitative bias analysis to observational epidemiologic research [e-book] / by Timothy L. Lash, Matthew P. Fox, Aliza K. Fink
| Applying quantitative bias analysis to observational epidemiologic research [e-book] / by Timothy L. Lash, Matthew P. Fox, Aliza K. Fink |
| Autore | Lash, Timothy L. |
| Pubbl/distr/stampa | New York : Springer, 2009 |
| Descrizione fisica | v.: digital |
| Altri autori (Persone) |
Fox, Matthew P.
Fink, Aliza K. |
| Collana | Statistics for biology and health, 1431-8776 |
| Soggetto topico |
Statistics
Public health |
| ISBN | 9780387879598 |
| Formato | Software  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991001288489707536 |
|
Lash, Timothy L.
|
||
| New York : Springer, 2009 | ||
| Software | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
Appunti di metodi matematici e statistici / Paolo Baldi
| Appunti di metodi matematici e statistici / Paolo Baldi |
| Autore | Baldi, Paolo |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Bologna : CLUEB, 1996 |
| Descrizione fisica | vi, 208 p. ; 24 cm |
| Disciplina | 519 |
| Collana | Alma materiali. Didattica |
| Soggetto topico |
Mathematics
Statistics |
| ISBN | 8880914634 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Record Nr. | UNISALENTO-991003422969707536 |
|
Baldi, Paolo
|
||
| Bologna : CLUEB, 1996 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
Artificial Intelligence for Smart Manufacturing [[electronic resource] ] : Methods, Applications, and Challenges / / edited by Kim Phuc Tran
| Artificial Intelligence for Smart Manufacturing [[electronic resource] ] : Methods, Applications, and Challenges / / edited by Kim Phuc Tran |
| Autore | Tran Kim Phuc |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (271 pages) |
| Disciplina | 670.28563 |
| Collana | Springer Series in Reliability Engineering |
| Soggetto topico |
Industrial engineering
Production engineering Statistics Machine learning Industrial and Production Engineering Applied Statistics Machine Learning |
| Soggetto non controllato |
Manufactures
Technology & Engineering |
| ISBN | 3-031-30510-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Introduction to Artificial Intelligence for Smart Manufacturing -- Chapter 2: Artificial Intelligence for Smart Manufacturing in Industry 5.0: Methods, Applications, and Challenges -- Chapter 3: Quality control for Smart Manufacturing in Industry 5.0 -- Chapter 4: Dynamic Process Monitoring Using Machine Learning Control Charts -- Chapter 5: Fault Prediction of Papermaking Process Based on Gaussian Mixture Model and Mahalanobis Distance -- Chapter 6: Multi-objective optimization of flexible flow-shop intelligent scheduling based on a hybrid intelligent algorithm -- Chapter 7: Personalized pattern recommendation system of men’s shirts -- Chapter 8: Efficient and Trustworthy Federated Learning-based Explainable Anomaly Detection: Challenges, Methods, and Future Directions -- Chapter 9: Multimodal machine learning in prognostics and health management of manufacturing systems -- Chapter 10: Explainable Artificial Intelligence for Cybersecurity in Smart Manufacturing -- Chapter 11: Wearable technology for Smart Manufacturing in Industry 5.0 -- Chapter 12: Benefits of using Digital Twin for online fault diagnosis of a manufacturing system. |
| Record Nr. | UNINA-9910728942103321 |
|
Tran Kim Phuc
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Asian journal of mathematics & statistics
| Asian journal of mathematics & statistics |
| Pubbl/distr/stampa | Faisalabad, : Asian Network for Scientific Information |
| Descrizione fisica | 1 online resource |
| Disciplina | 510 |
| Soggetto topico |
Mathematics - Research
Statistics |
| Soggetto genere / forma | Periodicals. |
| ISSN | 2077-2068 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Asian journal of mathematics and statistics |
| Record Nr. | UNINA-9910145320403321 |
| Faisalabad, : Asian Network for Scientific Information | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
ASMOD 2018: Proceedings of the International Conference on Advances in Statistical Modelling of Ordinal Data
| ASMOD 2018: Proceedings of the International Conference on Advances in Statistical Modelling of Ordinal Data |
| Autore | Capecchi Stefania |
| Pubbl/distr/stampa | FedOA - Federico II University Press, 2018 |
| Descrizione fisica | 1 electronic resource (219 pages) |
| Disciplina | 320.01 |
| Soggetto topico | Statistics |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | ASMOD 2018 |
| Record Nr. | UNINA-9910345967303321 |
|
Capecchi Stefania
|
||
| FedOA - Federico II University Press, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Assessment methods in statistical education [[electronic resource] ] : an international perspective / / edited by Penelope Bidgood, Neville Hunt, Flavia Jolliffe
| Assessment methods in statistical education [[electronic resource] ] : an international perspective / / edited by Penelope Bidgood, Neville Hunt, Flavia Jolliffe |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2010 |
| Descrizione fisica | 1 online resource (306 p.) |
| Disciplina |
519.5071
519.50711 |
| Altri autori (Persone) |
BidgoodPenelope
HuntNeville JolliffeF. R <1942-> (Flavia R.) |
| Soggetto topico |
Mathematical statistics - Study and teaching - Evaluation
Statistics |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-54777-1
9786612547775 0-470-71047-0 0-470-71046-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Assessment Methods in Statistical Education; Contents; Contributors; Foreword; Preface; Acknowledgements; PART A SUCCESSFUL ASSESSMENT STRATEGIES; 1 Assessment and feedback in statistics; 2 Variety in assessment for learning statistics; 3 Assessing for success: An evidence-based approach that promotes learning in diverse, non-specialist student groups; 4 Assessing statistical thinking and data presentation skills through the use of a poster assignment with real-world data; 5 A computer-based approach to statistics teaching and assessment in psychology; PART B ASSESSING STATISTICAL LITERACY
6 Assessing statistical thinking7 Assessing important learning outcomes in introductory tertiary statistics courses; 8 Writing about findings: Integrating teaching and assessment; 9 Assessing students' statistical literacy; 10 An assessment strategy to promote judgement and understanding of statistics in medical applications; 11 Assessing statistical literacy: Take CARE; PART C ASSESSMENT USING REAL-WORLD PROBLEMS; 12 Relating assessment to the real world; 13 Staged assessment: A small-scale sample survey; 14 Evaluation of design and variability concepts among students of agriculture 15 Encouraging peer learning in assessment instruments16 Inquiry-based assessment of statistical methods in psychology; PART D INDIVIDUALISED ASSESSMENT; 17 Individualised assessment in statistics; 18 An adaptive, automated, individualised assessment system for introductory statistics; 19 Random computer-based exercises for teaching statistical skills and concepts; 20 Assignments made in heaven? Computer-marked, individualised coursework in an introductory level statistics course; 21 Individualised assignments on modelling car prices using data from the Internet; References; Index |
| Record Nr. | UNINA-9910140706303321 |
| Hoboken, N.J., : Wiley, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Assessment methods in statistical education [[electronic resource] ] : an international perspective / / edited by Penelope Bidgood, Neville Hunt, Flavia Jolliffe
| Assessment methods in statistical education [[electronic resource] ] : an international perspective / / edited by Penelope Bidgood, Neville Hunt, Flavia Jolliffe |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2010 |
| Descrizione fisica | 1 online resource (306 p.) |
| Disciplina |
519.5071
519.50711 |
| Altri autori (Persone) |
BidgoodPenelope
HuntNeville JolliffeF. R <1942-> (Flavia R.) |
| Soggetto topico |
Mathematical statistics - Study and teaching - Evaluation
Statistics |
| ISBN |
1-282-54777-1
9786612547775 0-470-71047-0 0-470-71046-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Assessment Methods in Statistical Education; Contents; Contributors; Foreword; Preface; Acknowledgements; PART A SUCCESSFUL ASSESSMENT STRATEGIES; 1 Assessment and feedback in statistics; 2 Variety in assessment for learning statistics; 3 Assessing for success: An evidence-based approach that promotes learning in diverse, non-specialist student groups; 4 Assessing statistical thinking and data presentation skills through the use of a poster assignment with real-world data; 5 A computer-based approach to statistics teaching and assessment in psychology; PART B ASSESSING STATISTICAL LITERACY
6 Assessing statistical thinking7 Assessing important learning outcomes in introductory tertiary statistics courses; 8 Writing about findings: Integrating teaching and assessment; 9 Assessing students' statistical literacy; 10 An assessment strategy to promote judgement and understanding of statistics in medical applications; 11 Assessing statistical literacy: Take CARE; PART C ASSESSMENT USING REAL-WORLD PROBLEMS; 12 Relating assessment to the real world; 13 Staged assessment: A small-scale sample survey; 14 Evaluation of design and variability concepts among students of agriculture 15 Encouraging peer learning in assessment instruments16 Inquiry-based assessment of statistical methods in psychology; PART D INDIVIDUALISED ASSESSMENT; 17 Individualised assessment in statistics; 18 An adaptive, automated, individualised assessment system for introductory statistics; 19 Random computer-based exercises for teaching statistical skills and concepts; 20 Assignments made in heaven? Computer-marked, individualised coursework in an introductory level statistics course; 21 Individualised assignments on modelling car prices using data from the Internet; References; Index |
| Record Nr. | UNINA-9910830046203321 |
| Hoboken, N.J., : Wiley, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Soggetto topico
-
Statistics
(1152)
- Mathematical statistics (141)
- Econometrics & economic statistics (117)
- Macroeconomics (103)
- Public Finance (98)