/* ======================================================================= ch10.do Stata do-file for Ch.10 of the textbook Stata ni yoru Keiryoseijigaku by Masahiko Asano and Yuki Yanai Created: Feb/05/2013 by MA and YY =========================================================================*/ set more off ** change the working directory ** (the path must be tailored to your computer environment) cd ~/stata/ ** open a log file log using log_ch10.log, replace /* ----------------------------------------------------- Analysis of the beer data -------------------------------------------------------*/ ** open the dataset use beer2010.dta, clear ** scatter plot: beer vs temp ** Figure 10.1 scatter beer temp ** correlation between beer and temp correlate beer temp ** scatter plot with a regression line: beer vs temp ** Figure 10.2 twoway (scatter beer temp) (lfit beer temp) ** a simple regression ** response variable = beer ** explanatory variable = temp regress beer temp /* ----------------------------------------------------- Analysis of the electoral data -------------------------------------------------------*/ ** open the dataset use hr96-09.dta, clear ** focus on 2009 data keep if year==2009 * drop if year!=2009 ** focus on LDP candidates only keep if party==800 * drop if party!=800 ** a multiple regression ** response variable = voteshare ** explanatory variable 1 = exp ** explanatory variable 2 = previous reg voteshare exp previous ** generate a new variable "expm" generate expm = exp/1000000 ** a multiple regression ** response variable = voteshare ** explanatory variable 1 = expm ** explanatory variable 2 = previous reg voteshare expm previous /* ----------------------------------------------------- Obtain the results of a multiple regression by a series of simple regressions -------------------------------------------------------*/ ** regress voteshare on expm reg voteshare expm ** regress voteshare on previous reg voteshare previous ** save the residuals in e1 gen e1 = voteshare - (35.400008 + 1.417635*previous) * predict e1, residual ** regress expm on previous reg expm previous ** save the residual in e2 gen e2 = expm - (9.99618 + 0.378646*previous) * predict e2, res ** regress e1 on e2 reg e1 e2 ** close the log file log close