Bettwäsche CASATEX Indi Gr B L 240 cm x 220 cm (1 St) B L 40 cm x 80 cm & 40 cm x 80 cm (2 St) Biber lila (winterberry) Bettwäsche 240x220 cm

Lieferumfang: Sofa mit Armlehne inkl. 3 Rückenkissen und 4 Deko-Kissen 174597636493 Springer 23194 https: www.billiger.de common modules api cmodul?mc=6yaa604IiBJo&p=-p6STOxmd3FRDMrEUYsO-lVt6WZAp33cWGGzTjRsvqB5p11Pk4U7aWI-yYEstmQrYkZKpwcYI7dIHx9e5GpWVCkWXk2Uqxi9b-BpUXdO7Gzobzp-A5MHfJ3vvvNr1_-bpVygqhKvtvxrQUdlpsG-hPQi1Be4GVRHA&mid=174597636493&id=174597636493&ts=20231117 0.00 0 9783031248566 117.69 With the primary goal of expanding access to spatial data science tools this book offers dozens of minimal or low-code functions and tutorials designed to ease the implementation of fully reproduci ble Spatial Socio-Econometric Modeling (SSEM) analyses. Designed as a University of Pennsylvania Ph.D. level course for sociologists political scientists urban planners criminologists and data scientists this textbook equips social scientists with all concepts explanations and functions required to strengthen their data storytelling. It specifically provides social scientists with a comprehensive set of open-access minimal code tools to:-Identify and access place-based longitudinal and cross-sectional data sources and formats-Conduct advanced data management including crosswalks joining and matching-Fully connect social network analyses with geospatial statistics-Formulate research questions designed to account for place-based factors in model specification and assesstheir relevance compared to individual- or unit-level indicators-Estimate distance measures across units that follow road network paths -Create sophisticated and interactive HTML data visualizations cross-sectionally or longitudinally to strengthen research storytelling capabilities-Follow best practices for presenting spatial analyses findings and implications-Master theories on neighborhood effects equality of opportunity and geography of (dis)advantage that undergird SSEM applications and methods-Assess multicollinearity issues via machine learning that may affect coefficients' estimates and guide the identification of relevant predictors-Strategize how to address feedback loops by using SSEM as an identification framework that can be merged with standard quasi-experimental techniques like propensity score models instrumental variables and difference in differences-Expand the SSEM analyses to connections that emerge via social interactions such as co-authorship and advice networks or any form of relational dataThe applied nature of the book along with the cost-free multi-operative R software makes the usability and applicability of this textbook worldwide."