Module 1 Workshops and Assignment Copyright © Jacob L. Cybulski Assignment A1 Preview: R/R Studio & Regression The following mini-case study will be used as part of your assignment A1 and to guide activities of workshops M1T1-M1T4 (module M1). The World Bank approached you to assist in the identification of social and environmental predictors of wealth and poverty. While the impact of national economic development on people’s wealth is well known, the World Bank seeks to develop a model, which could predict the effects of social and environmental changes on the economic well-being of people living in different countries. They would also like to determine a course of action aimed at improving the situation in the countries most affected by such changes. You have been asked to identify a number of predictors of wealth and poverty. Justify your variable selection. Explore their characteristics and relationships using statistical methods and data visualisation. Build a multiple regression model in R and evaluate its performance. Interpret generated results and use them to suggest ways of reducing poverty across the globe. Work in a team, develop your solution and report individually. Submit your partial results and final findings via CloudDeakin. Access the World Bank data here: http://data.worldbank.org/indicator 1 Use R and R Studio Awake: Create a problem definition and write a brief specification of its possible solution (all this may change later). Crawl: Select 10-15 social and environmental predictors of 3-5 different types of wealth and poverty indicators. Justify your selection. Download the WB indicators and extract the required candidate variables. Visualise and interpret their characteristics. Transform these variables if needed in the following process. Step: Use correlation analysis and data visualization to investigate any relationships between the selected variables. Interpret obtained results and suggest the best selection of variables for further processing. Walk and Hop: Create, evaluate and optimize a multiple regression model. Report its properties and performance. Run: Use the final model to solve the WB problem. Write the final report and recommendations. Fly: Conduct independent research to extend your work, to improve the model and to present its results in the best way.