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1. |
Record Nr. |
UNISANNIOBVEE093645 |
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Autore |
Torsellini, Orazio <1545-1599> |
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Titolo |
Particulae Latinae orationis ab Horatio Tursellino collectae, nunc vero ex aliis scriptoribus, de quibus in praefatione, purgatae, & auctae, & ad usum Seminarii Patavini accommodatae |
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Pubbl/distr/stampa |
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Patavii : ex typographia Seminarii, 1765 |
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Descrizione fisica |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Front. stampato in rosso e nero |
Fregio xilogr. sul front |
Segn.: Ï1 a¹² B-T¹² V¹Ⱐ|
La c. a1r contiene l'occhietto |
La c. a2r contiene front. dell'edizione stampata a Padova da Giovanni Ricci nel 1744 |
A c. a3r inizia la prefazione di Jacopo Facciolati |
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2. |
Record Nr. |
UNINA9910703035403321 |
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Titolo |
Healthy planet, healthy people [[electronic resource] ] : global warming and public health : hearing before the Select Committee on Energy Independence and Global Warming, House of Representatives, One Hundred Tenth Congress, second session, April 9, 2008 |
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Pubbl/distr/stampa |
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Washington : , : U.S. G.P.O., , 2010 |
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Descrizione fisica |
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1 online resource (iii, 209 pages) : illustrations |
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Soggetti |
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Global warming - Health aspects - United States |
Climatic changes - Health aspects - United States |
Greenhouse effect, Atmospheric - Health aspects - United States |
Environmentally induced diseases - United States |
Public health - Environmental aspects |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Title from title screen (viewed on Jan. 12, 2011). |
"Serial no. 110-32." |
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Nota di bibliografia |
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Includes bibliographical references. |
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3. |
Record Nr. |
UNINA9910483326403321 |
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Autore |
Schwarz Jason S. |
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Titolo |
Python for Marketing Research and Analytics / / by Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XI, 272 p. 90 illus., 79 illus. in color.) |
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Disciplina |
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Soggetti |
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Mathematical statistics - Data processing |
Statistics |
Social sciences - Statistical methods |
Statistics and Computing |
Statistics in Business, Management, Economics, Finance, Insurance |
Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Part I: Basics of Python -- Chapter 1: Welcome to Python -- Chapter 2: The Python Language -- Part II Fundamentals of Data Analysis -- Chapter 3: Describing Data -- Chapter 4: Relationships Between Continuous Variables -- Chapter 5: Comparing Groups: Tables and Visualizations -- Chapter 6: Comparing Groups: Statistical Tests -- Chapter 7: Identifying Drivers of Outcomes: Linear Models -- Chapter 8: Additional Linear Modeling Topics -- Part III Advanced data analysis -- Chapter 9: Reducing Data Complexity -- Chapter 10: Segmentation: Unsupervised Clustering Methods for Exploring Subpopulations -- Chapter 11: Classification: Assigning observations to known categories -- Chapter 12: Conclusion -- Index. |
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Sommario/riassunto |
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This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate |
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code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics. . |
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