Learn about join counts in Python using Airbnb data in Berlin residential districts (2018) /

This dataset teaches readers how to estimate and interpret Join Count statistics and use this information to identify whether there is clustering or dispersion in a categorical feature. This dataset contains data related to residential districts in central Berlin, Germany, and information about the...

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Bibliographic Details
Main Author:	Wolf, Levi John (Author)
Format:	eBook
Language:	English
Published:	London : SAGE Publications, Ltd., 2019.
Subjects:	Airbnb (Firm) > Statistics. Airbnb (Firm) Spatial analysis (Statistics) Geography > Statistical methods. Autocorrelation (Statistics) Python (Computer program language) > Handbooks, manuals, etc. Hospitality industry > Germany > Berlin > Statistics. Rental housing > Germany > Berlin > Statistics. Analyse spatiale (Statistique) Géographie > Méthodes statistiques. Autocorrélation (Statistique) Python (Langage de programmation) > Guides, manuels, etc. Accueil (Tourisme) > Allemagne > Berlin > Statistiques. Logement locatif > Allemagne > Berlin > Statistiques. spatial analysis. Geography > Statistical methods Hospitality industry Python (Computer program language) Rental housing Germany > Berlin Handbooks and manuals Statistics
Online Access:	Connect to the full text of this electronic book

Description
Summary:	This dataset teaches readers how to estimate and interpret Join Count statistics and use this information to identify whether there is clustering or dispersion in a categorical feature. This dataset contains data related to residential districts in central Berlin, Germany, and information about the boundaries of districts, as well as information about prices for Airbnbs in each district. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Python.
Physical Description:	1 online resource : illustrations
ISBN:	9781526499462 1526499460