Learn about spatial fixed effects models in Python using Airbnb data in Berlin residential districts (2018) /

This dataset teaches readers how to estimate and interpret a spatial fixed-effects model in Python. This dataset contains data related to Airbnb prices in Berlin, Germany, and the contextual effects of nightly Airbnb prices will be presented. The dataset file is accompanied by a Teaching Guide, a St...

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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) Python (Computer program language) > Handbooks, manuals, etc. Hospitality industry > Germany > Berlin > Statistics. Rental housing > Germany > Berlin > Statistics. Analyse spatiale (Statistique) Python (Langage de programmation) > Guides, manuels, etc. Accueil (Tourisme) > Allemagne > Berlin > Statistiques. Logement locatif > Allemagne > Berlin > Statistiques. spatial analysis. 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 a spatial fixed-effects model in Python. This dataset contains data related to Airbnb prices in Berlin, Germany, and the contextual effects of nightly Airbnb prices will be presented. 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:	9781526499561 1526499568