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FEM11149 - Introduction to Data
创建日期：2024-04-18 15:55:52
所属分类：金融Finance
标签： FEM11149
Introduction to Data

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FEM11149 - Introduction to Data 

In this final assignment you are not going to be given step-by-step instructions. 

You are expected to know which techniques are needed to clean and subset data (if needed), 

run models and their respective diagnostics. What you should hand-in 

• A PDF file, generated using R Markdown containing – A business report (max 4 pages) with your analysis – 

The report should follow the guidelines specified on Canvas - except for the code appendix (see below) 

• A *.rmd file, used to generate the PDF file above No attachments to the PDF file are allowed. Deadline: 24/10, 23:59.

 Final Assignment: investigating income inequality in Brazil Congratulations! Because of your hard work for Gelukshuisje,

 you are now a worldwide known data scientist. The United Nations Office in Brazil is looking for a consultant for a data science job, 

and your former intern from Gelukshuisje, Ahsia, recommended you. You are trusted with sample data from the last Brazilian 

National Census (2010). The dataset Brazil_data_census contains 4500 rows. Rows correspond to Brazilian cities, 

that are divided among the 26 states (estados/UF) plus the Federal District, where Brasília is located. In the 30 columns, 

besides the identification of the city, you find several numerical indicators, that range from total population to proportion of houses with electricity. The outcome of interest is the income appropriated by the 10% richest divided by the income of the 40% poorest within a city, identified as R1040 in the dataset (hereby referred as 10/40 ratio). This is a comparison of the per capita income of the richest decile with the 2/5 poorest and gives a notion of inequality. Column descriptions are in the file Brazilian_census_databook.xlsx. 1 The plan You need to investigate what explains the 10/40 ratio. For that, you will build and compare two regression models: • The first model will be a penalized regression using LASSO; • The second model is a regression using PCA scores as explanatory variables, that is, principal compo- nents scores are used instead of the original variables to explain your outcome of interest using a linear regression model. Minimum Requirements You need to set aside a sample size of 10 municipalities for which you will perform an out-of-sample prediction exercise to compare the results from both models. For both models, use the best practices you saw in the lectures. Specifically for PCA, in addition to three simple criteria, please use permutation test to select the meaningful number of principal components. Moreover, apply bootstrap procedure to Kaiser’s rule, i.e. test if the variance explained by each component is significantly larger than 1. Use the results of your analysis to name and interpret the selected components. Note that those are partial requirements and are not sufficient for a full grade. Everything should be explained and interpreted. Single results without interpretation will not be considered. Be aware that different criteria for selecting the principal components might differ in their conclusions, and the final decision is up to you and need to be justified. 

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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