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Buildings of Tomorrow: Goals and Challenges for Design and Operation of High-Performance Buildings
| Buildings of Tomorrow: Goals and Challenges for Design and Operation of High-Performance Buildings |
| Autore | Košir Mitja |
| Pubbl/distr/stampa | Basel, : MDPI Books, 2022 |
| Descrizione fisica | 1 electronic resource (230 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
climate change
bioclimatic design passive design energy efficiency overheating building resilience robustness shape factor building thermal envelope energy demand CO2 emissions white roofs cool roofs reflective material cost-benefit energy savings urban heat island thermal comfort indoor environmental quality educational buildings energy consumptions local discomfort building energy retrofitting phase change materials aerogel render heat stress risk emission lifecycle cost peak cooling load residential building building envelope multi-objective genetic algorithm TRNSYS climate zone multi-criteria decision making CRITIC TOPSIS capture devices variables field surveys thermal perceptions adaptive actions hostel dormitories composite climate of India reflective materials mitigation outdoor comfort visual comfort heat stress optimization skyscrapers |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Buildings of Tomorrow |
| Record Nr. | UNINA-9910595075003321 |
Košir Mitja
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| Basel, : MDPI Books, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Making it count : the improvement of social research and theory / / Stanley Lieberson
| Making it count : the improvement of social research and theory / / Stanley Lieberson |
| Autore | Lieberson Stanley <1933-2018.> |
| Pubbl/distr/stampa | Berkeley, : University of California Press, c1985 |
| Descrizione fisica | 1 online resource (xiv, 257 pages) |
| Disciplina | 301/.072 |
| Soggetto topico |
Sociology - Research - Methodology
Social sciences - Research - Methodology |
| Soggetto non controllato |
boyles law
causality causation conducting research data collection empiricism evaluating data logic nonexperimental data nonfiction political science quasi experiment research assumptions research methods research questions research sampling problems science scientific enterprise scientific method scientific theory selectivity social research social science sociological methodology sociology variables |
| ISBN |
1-282-35531-7
9786612355318 0-520-90842-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. Current practices -- pt. 2. Toward a solution. |
| Record Nr. | UNINA-9910778081203321 |
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Lieberson Stanley <1933-2018.>
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| Berkeley, : University of California Press, c1985 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Small sample size solutions : a guide for applied researchers and practitioners / / edited by Rens van de Schoot and Milica Miočević
| Small sample size solutions : a guide for applied researchers and practitioners / / edited by Rens van de Schoot and Milica Miočević |
| Autore | van de Schoot Rens |
| Pubbl/distr/stampa | Taylor & Francis, 2020 |
| Descrizione fisica | 1 online resource (xiv, 269 pages) : digital, PDF file(s) |
| Disciplina | 001.42 |
| Collana | European Association of Methodology series |
| Soggetto topico |
Research - Methodology
Data sets |
| Soggetto non controllato |
statistical methods
researchers statistical model research small sample estimation population variables observations social sciences behavioral sciences medical sciences epidemiology psychology marketing economics analysis |
| ISBN |
1-000-76101-0
1-000-76108-8 0-367-22222-1 0-429-27387-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction (Van de Schootand Miočević) List of Symbols Part I: Bayesian solutions 1. Introduction to Bayesian statistics(Miočević, Levy,and van de Schoot) 2.The role of exchangeability in sequential updating of findings from small studies and the challenges of identifying exchangeable data sets (Miočević, Levy,and Savord) 3. A tutorial on using the WAMBS checklist to avoid the misuse of Bayesian statistics(van de Schoot, Veen, Smeets, Winter,and Depaoli) 4. The importance of collaboration in Bayesian analyses with small samples (Veenand Egberts) 5. A tutorial on Bayesian penalized regression with shrinkage priors for small sample sizes (van Erp) PartII: n=1 6. One by one: the designand analysis of replicated randomized single-case experiments(Onghena) 7. Single-case experimental designs in clinical intervention research (Maricand van der Werff) 8. How to improve the estimation of a specific examinee's (n=1)math ability when test data are limited(Lekand Arts) 9. Combining evidence over multiple individual analyses(Klaassen) 10. Going multivariate in clinical trial studies: a Bayesian framework for multiple binary outcomes(Kavelaars) PartIII: Complex hypotheses and models 11. An introduction to restriktor: evaluating informative hypotheses for linear models (VanbrabantandRosseel) 12. Testing replication with small samples: applications to ANOVA(Zondervan-Zwijnenburgand Rijshouwer) 13. Small sample meta-analyses: exploring heterogeneity using MetaForest (van Lissa) 14. Item parcels as indicators: why, when, and how to use them in small sample research(Rioux, Stickley, Odejimi,and Little) 15. Small samples in multilevel modeling(Hoxand McNeish) 16. Small sample solutions for structural equation modeling(Rosseel) 17. SEM with small samples: two-step modeling and factor score regression versus Bayesian estimation with informative priors (Smidand Rosseel) 18. Important yet unheeded: some small sample issues that are often overlooked(Hox) Index |
| Record Nr. | UNINA-9910377813603321 |
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van de Schoot Rens
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| Taylor & Francis, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Spatiotemporal data analysis / / Gidon Eshel
| Spatiotemporal data analysis / / Gidon Eshel |
| Autore | Eshel Gidon <1958-> |
| Edizione | [Course Book] |
| Pubbl/distr/stampa | Princeton : , : Princeton University Press, , [2012] |
| Descrizione fisica | 1 online resource (336 p.) |
| Disciplina | 519.5/36 |
| Soggetto topico | Spatial analysis (Statistics) |
| Soggetto genere / forma | Electronic books. |
| Soggetto non controllato |
EOF analysis
EOF GramГchmidt orthogonalization SVD analysis SVD astrophysics autocorrelation functions autocovariance autoregressive model climate science column space covariability matrix data analysis data matrices degrees of freedom deterministic science ecology eigen-decomposition eigen-techniques eigenanalysis eigenvalues empirical orthogonal functions empirical science empiricism exercises forward problem geophysics inverse problem linear algebra linear regression matrices matrix structure matrix medicine multidimensional data sets multidimensional data nondeterministic phenomena null space phenomena probability distribution row space singular value decomposition spatiotemporal data spectral representation square matrices statistics stochastic processes subjective decisions theoretical science time series timescale tornado variables vectors |
| ISBN | 1-4008-4063-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Contents -- Preface -- Acknowledgments -- Part 1. Foundations -- One. Introduction and Motivation -- Two. Notation and Basic Operations -- Three. Matrix Properties, Fundamental Spaces, Orthogonality -- Four. Introduction to Eigenanalysis -- Five. The Algebraic Operation of SVD -- Part 2. Methods of Data Analysis -- Six. The Gray World of Practical Data Analysis: An Introduction to Part 2 -- Seven. Statistics in Deterministic Sciences: An Introduction -- Eight. Autocorrelation -- Nine. Regression and Least Squares -- Ten. The Fundamental Theorem of Linear Algebra -- Eleven. Empirical Orthogonal Functions -- Twelve. The SVD Analysis of Two Fields -- Thirteen. Suggested Homework -- Index |
| Record Nr. | UNINA-9910461571103321 |
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Eshel Gidon <1958->
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| Princeton : , : Princeton University Press, , [2012] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Spatiotemporal data analysis / / Gidon Eshel
| Spatiotemporal data analysis / / Gidon Eshel |
| Autore | Eshel Gidon <1958-> |
| Edizione | [Course Book] |
| Pubbl/distr/stampa | Princeton : , : Princeton University Press, , [2012] |
| Descrizione fisica | 1 online resource (336 p.) |
| Disciplina | 519.5/36 |
| Soggetto topico | Spatial analysis (Statistics) |
| Soggetto non controllato |
EOF analysis
EOF GramГchmidt orthogonalization SVD analysis SVD astrophysics autocorrelation functions autocovariance autoregressive model climate science column space covariability matrix data analysis data matrices degrees of freedom deterministic science ecology eigen-decomposition eigen-techniques eigenanalysis eigenvalues empirical orthogonal functions empirical science empiricism exercises forward problem geophysics inverse problem linear algebra linear regression matrices matrix structure matrix medicine multidimensional data sets multidimensional data nondeterministic phenomena null space phenomena probability distribution row space singular value decomposition spatiotemporal data spectral representation square matrices statistics stochastic processes subjective decisions theoretical science time series timescale tornado variables vectors |
| ISBN | 1-4008-4063-5 |
| Classificazione | SCI019000MAT002050 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Contents -- Preface -- Acknowledgments -- Part 1. Foundations -- One. Introduction and Motivation -- Two. Notation and Basic Operations -- Three. Matrix Properties, Fundamental Spaces, Orthogonality -- Four. Introduction to Eigenanalysis -- Five. The Algebraic Operation of SVD -- Part 2. Methods of Data Analysis -- Six. The Gray World of Practical Data Analysis: An Introduction to Part 2 -- Seven. Statistics in Deterministic Sciences: An Introduction -- Eight. Autocorrelation -- Nine. Regression and Least Squares -- Ten. The Fundamental Theorem of Linear Algebra -- Eleven. Empirical Orthogonal Functions -- Twelve. The SVD Analysis of Two Fields -- Thirteen. Suggested Homework -- Index |
| Record Nr. | UNINA-9910789871903321 |
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Eshel Gidon <1958->
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| Princeton : , : Princeton University Press, , [2012] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Spatiotemporal data analysis / / Gidon Eshel
| Spatiotemporal data analysis / / Gidon Eshel |
| Autore | Eshel Gidon <1958-> |
| Edizione | [Course Book] |
| Pubbl/distr/stampa | Princeton : , : Princeton University Press, , [2012] |
| Descrizione fisica | 1 online resource (336 p.) |
| Disciplina | 519.5/36 |
| Soggetto topico | Spatial analysis (Statistics) |
| Soggetto non controllato |
EOF analysis
EOF GramГchmidt orthogonalization SVD analysis SVD astrophysics autocorrelation functions autocovariance autoregressive model climate science column space covariability matrix data analysis data matrices degrees of freedom deterministic science ecology eigen-decomposition eigen-techniques eigenanalysis eigenvalues empirical orthogonal functions empirical science empiricism exercises forward problem geophysics inverse problem linear algebra linear regression matrices matrix structure matrix medicine multidimensional data sets multidimensional data nondeterministic phenomena null space phenomena probability distribution row space singular value decomposition spatiotemporal data spectral representation square matrices statistics stochastic processes subjective decisions theoretical science time series timescale tornado variables vectors |
| ISBN | 1-4008-4063-5 |
| Classificazione | SCI019000MAT002050 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Contents -- Preface -- Acknowledgments -- Part 1. Foundations -- One. Introduction and Motivation -- Two. Notation and Basic Operations -- Three. Matrix Properties, Fundamental Spaces, Orthogonality -- Four. Introduction to Eigenanalysis -- Five. The Algebraic Operation of SVD -- Part 2. Methods of Data Analysis -- Six. The Gray World of Practical Data Analysis: An Introduction to Part 2 -- Seven. Statistics in Deterministic Sciences: An Introduction -- Eight. Autocorrelation -- Nine. Regression and Least Squares -- Ten. The Fundamental Theorem of Linear Algebra -- Eleven. Empirical Orthogonal Functions -- Twelve. The SVD Analysis of Two Fields -- Thirteen. Suggested Homework -- Index |
| Record Nr. | UNINA-9910823944403321 |
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Eshel Gidon <1958->
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| Princeton : , : Princeton University Press, , [2012] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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