#Download the Software #visit: http://www.rstudio.com http://www.rstudio.com/products/rstudio/downlo /products/rstudio/download/ ad/ #Install Packages #Create Directory setwd( "D:/Bappa/PGPBA/Data/") #Import file DE2<-read.csv(file.choose(), DE2<-read.csv(file.cho ose(), header=T) #attach(DE2) attach(DE2) #Install Packages #Install 'MASS' library(epiR) library(het.test) library(car) library(lmtest) library(sandwich) library(caret) library(e1071) library(nortest) library(MASS) #Reading and Verifying the Data dim(DE2) #Rename Create variables poverty<-ifelse(MPCE
#pie count<-table(Religion) count pie(count, main="Religion") #histogram hist(HH_Size,freq=F) lines(density(HH_Size)) hist(MPCE) hist(logMPC) plot(HH_Size,logMPC, xlab="Size",ylab="MPC") cor(HH_Size,logMPC)
#####calculate Probability distributions ##Binomial #Point Probability dbinom(4,size=20,prob=0.13) #Cumulative probability pbinom(4,size=20,prob=0.13) ##Poisson #Point Probability dpois(8,lambda=4) #Cumulative Probability ppois(8,lambda=4) #Normal pnorm(9,mean=7.775,sd=0.64,lower.tail=F) pnorm(9,mean=7.775,sd=0.64,lower.tail=T) ###Normality Tests #Tests for Normality #Create Normal Distn y<-rnorm(1000,0,1) #Visual: Histograms hist(y) qqnorm(y) qqline(y, col="red") #Shapiro Test shapiro.test(y) #Anderson Darling Test ad.test(y) #Kolmogoroc Smirnoff test ks.test(y,pnorm,mean=0,sd=1) #Shapiro Test shapiro.test(MPCE) #Anderson Darling Test ad.test(MPCE) #Kolmogoroc Smirnoff test ks.test(MPCE,pnorm,mean=2961.274,sd=2378.087) #Shapiro Test
shapiro.test(logMPC) #Anderson Darling Test ad.test(logMPC) #Kolmogoroc Smirnoff test ks.test(logMPC,pnorm,mean=7.775,sd=0.64) ###t-Distribution: #Find the 2.5th and 97.5th percentiles of the Student t distribution with 10 deg rees of freedom. qt(c(.025, .975), df=10) ###Chi Square #Find the 95th percentile of the Chi-Squared distribution with 10 degrees of fre edom. qchisq(.95, df=10) ####F Dtsribution #Find the 95th percentile of the F distribution with (5, 2) degrees of freedom qf(.95, df1=5, df2=2) ###General rule: dpois, dbinom etc for point/density,,,,ppois, pbinom for cumula tive ####Hypothesis Test # For n>30 and population variance known: Apply z test as follows #say (mu=7.5) and pop sd=0.66, Null: xbar(meanlogMPC)> mu; n=100 #test stat (mean(logMPC)-7.5)/(0.66/sqrt(10)) pnorm(1.319)
#One Sample Mean (using t test) #One sample two tail t.test(logMPC,mu=8) #One sample upper tail t.test(logMPC,mu=7, alternative="greater") #Two samples mean #T.test 2 sample 2 sided t.test(HH_Size~poverty,mu=0,alt="two.sided",conf=0.95,var.eq=F,paired=F) #T.test 2 sample 1 sided t.test(HH_Size~poverty,mu=0,alt="less",conf=0.95,var.eq=F,paired=F) x1<- rnorm(50, mean = 0, sd = 2) x2<-rnorm(100,mean=1,sd=2) n<-40
#Two samples Variance var.test(x1, x2, ratio = 1, alternative = c("two.sided"))
####ANOVA #1 way ANOVA boxplot(logMPC~Social_Group) aov.mpc<-aov(logMPC~Social_Group) summary(aov.mpc) tk<-TukeyHSD(aov.mpc) tk plot(tk) #####Regression #define some more useful variables #define global Y<-cbind(poverty) Assets<-cbind(bedstead,almirah ,chair, r adio, t v, fan, fridge, cycle, car) Demography<- cbind(Hindu, Islam, SC, ST, OBC, HH_Size) Access<- cbind(LPG,electricity)
AC,
stove,
#Simple Linear Regression plot(logMPC~HH_Size) #run the OLS olsreg<-lm(logMPC~HH_Size, data=DE2) #Output summary(olsreg) #Best Fit Line abline(olsreg) #Multiple Linear Regression olsreg1<-lm(logMPC~Assets+ Demography+ Access) summary(olsreg1) plot(olsreg1) #Saving the Work #Further Help: Visit http://www.r-tutor.com #A good Starting Book: Mark Gardener, "Beginning R: The Statistical Programming Language", Wiley India Pvt. Ltd, INR 649 # Some useful Websites #For background as well as commands: Visit yout tube videos: #Basics of R (videos by Ed Boone, Phil Chan) # Econometrics Academy
