1. Fit a linear model with your chosen transformation using OLS estimation method. Write out the mathematical expression for the functional form of your model using both the transformed variables and the untransformed original variables. Interpret the effect of coefficients on the response variable in the untransformed version of the regression model

BM2009=Big Mac price in 2009 in terms of minutes of a typical labour work; BM2003= bIG mac price in 2003 in terms of miuntes of a typical labour work

BM.lm2<-lm(log(BM2009)~log(BM2003)) summary(BM.lm2) ##  ## Call: ## lm(formula = log(BM2009) ~ log(BM2003)) ##  ## Residuals: ## Min 1Q Median 3Q Max  ## -1.11379 -0.10663 -0.01378 0.08207 0.89628  ##  ## Coefficients: ## Estimate Std. Error t value Pr(>|t|)  ## (Intercept) 0.64031 0.22922 2.793 0.00728 **  ## log(BM2003) 0.80293 0.06709 11.967 < 2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##  ## Residual standard error: 0.3324 on 52 degrees of freedom ## Multiple R-squared: 0.7336, Adjusted R-squared: 0.7285  ## F-statistic: 143.2 on 1 and 52 DF, p-value: < 2.2e-16