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R_introduction
Introduction to R and basic R syntax
London Property Transactions (at Jan 2025 values)
Contains HM Land Registry data © Crown copyright and database right 2021. This data is licensed under the Open Government Licence v3.0.
Document
Analisis faktor-faktor psikologis yang memengaruhi burnout pada guru menggunakan pendekatan Structural Equation Modeling (SEM), Confirmatory Factor Analysis (CFA), dan Multidimensional Scaling (MDS). Dataset bersumber dari penelitian Habibi dkk. (2020).
Inflation_19TH
Modelling
EGarch_Garch254
inflation rates
volatility
forecast the rates for the next 60 months
chap4
chap4
Pensions_19th
Fitting models, forecasting. volatility
Getting Data from the World Bank Website using Python and Pandas-Datareader Module
This article demonstrates how to access and analyze global development indicators from the World Bank using Python’s `pandas-datareader` module. We walk through package installation, querying available countries and indicators, and extracting time-series data for selected metrics. Using real-world examples such as GDP per capita, life expectancy, and access to electricity, we illustrate how to generate insightful visualizations that highlight stark regional disparities—particularly between Sub-Saharan Africa and Europe. The approach outlined provides a reproducible workflow for data analysts, researchers, and policy professionals seeking to work with high-quality international development data in Python. The article concludes with suggestions for deeper analysis and integration with geospatial and statistical tools.