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  • 1
    Online Resource
    Online Resource
    Cham, Switzerland : Springer
    UID:
    b3kat_BV045537864
    Format: 1 Online-Ressource (XX, 487 Seiten) , Illustrationen, Diagramme (teilweise farbig)
    Edition: Second edition
    ISBN: 9783030143169
    Series Statement: Use R!
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-14315-2
    Language: English
    Subjects: Computer Science
    RVK:
    RVK:
    Keywords: Marketingforschung ; Statistik ; R ; Einführung
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    b3kat_BV047047384
    Format: 1 Online-Ressource (XI, 272 Seiten) , Illustrationen
    ISBN: 9783030497200
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-49719-4
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-49721-7
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-49722-4
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Python ; Datenanalyse ; Marketingforschung
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 3
    Online Resource
    Online Resource
    Cham [u.a.] : Springer
    UID:
    b3kat_BV042481786
    Format: 1 Online-Ressource (XVIII, 454 S.) , 108 illus., 54 illus. in color
    ISBN: 9783319144368
    Series Statement: Use R!
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-319-14435-1
    Language: English
    Subjects: Computer Science , Economics
    RVK:
    RVK:
    RVK:
    Keywords: Marketingforschung ; Statistik ; R ; Einführung
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  • 4
    UID:
    b3kat_BV042488459
    Format: XVIII, 454 S. , graph. Darst., Kt.
    ISBN: 9783319144351
    Series Statement: Use R!
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-3-319-14436-8
    Language: English
    Subjects: Computer Science , Economics
    RVK:
    RVK:
    RVK:
    Keywords: Marketingforschung ; Statistik ; R ; Einführung
    URL: Cover
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  • 5
    UID:
    b3kat_BV045948214
    Format: xx, 487 Seiten , Illustrationen, Diagramme
    Edition: Second edition
    ISBN: 9783030143152
    Series Statement: Use R!
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-3-030-14316-9
    Language: English
    Subjects: Computer Science
    RVK:
    RVK:
    Keywords: Marketingforschung ; Statistik ; R
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    Online Resource
    Online Resource
    Cham : Springer
    UID:
    gbv_1656031086
    Format: Online-Ressource (XVIII, 454 p. 108 illus., 54 illus. in color, online resource)
    ISBN: 9783319144368
    Series Statement: Use R!
    Content: Welcome to R -- The R Language -- Describing Data -- Relationships Between Continuous Variables -- Comparing Groups: Tables and Visualizations -- Comparing Groups: Statistical Tests -- Identifying Drivers of Outcomes: Linear Models -- Reducing Data Complexity -- Additional Linear Modeling Topics -- Confirmatory Factor Analysis and Structural Equation Modeling -- Segmentation: Clustering and Classification -- Association Rules for Market Basket Analysis -- Choice Modeling -- Conclusion -- Appendix: R Versions and Related Software -- Appendix: Scaling up -- Appendix: Packages Used -- Index
    Content: This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications
    Additional Edition: ISBN 9783319144351
    Additional Edition: Druckausg. Chapman, Chris R for marketing research and analytics Cham : Springer, 2015 ISBN 9783319144351
    Language: English
    Subjects: Economics
    RVK:
    Keywords: Statistik ; R ; Marketing ; Einführung
    URL: Cover
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  • 7
    Online Resource
    Online Resource
    Cham : Springer International Publishing | Cham : Imprint: Springer
    UID:
    gbv_1741571960
    Format: 1 Online-Ressource(XI, 272 p. 90 illus., 79 illus. in color.)
    Edition: 1st ed. 2020.
    ISBN: 9783030497200
    Series Statement: Springer eBook Collection
    Content: 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.
    Content: 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 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. .
    Additional Edition: ISBN 9783030497194
    Additional Edition: ISBN 9783030497217
    Additional Edition: ISBN 9783030497224
    Additional Edition: Erscheint auch als Druck-Ausgabe Schwarz, Jason S. Python for marketing research and analytics Cham, Switzerland : Springer, 2020 ISBN 9783030497194
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030497217
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030497224
    Language: English
    Subjects: Computer Science , Economics
    RVK:
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    Keywords: Marktanalyse ; Python
    URL: Cover
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