# Options

# Surpress scientific notations, digits and lines

options(scipen=999, digits=10, max.print=99999999)


# Assignment 1

attach(DELL_DATA_cleaned)

library(psych)

summary(DELL_DATA_cleaned)

describe(DELL_DATA_cleaned)

library(naniar)

vis_miss(DELL_DATA_cleaned)

# Chisquare Tests

prop.table(table(q14))

chisq.test(table(q14), p=c(0.55,0.45))

chisq.test(q13, SAMPLE, correct=FALSE)
E<-chisq.test(q13, SAMPLE, correct=FALSE)
str(E)
E$expected

chisq.test(q13, SAMPLE, correct=FALSE, simulate.p.value=TRUE, B=10000)

fisher.test(table(q13,SAMPLE), workspace=2000000)

library(vcd)

assocstats(table(q13,SAMPLE))


#  Paired t test

t.test(q5, q4, paired=TRUE, alternative = "greater")


# Nonpar: Wilcoxon

wilcox.test(q5,q4, paired=TRUE, exact=FALSE, correct=FALSE)

library(coin)

wilcoxsign_test(q5 ~ q4, zero.method="Wilcoxon", distribution="asymptotic")

# Oneway ANOVA

# Type III === SPSS
# To get SPSS equivalent SPSS results

options(contrasts=c("contr.sum", "contr.poly"))

FIT.01<- aov(q6 ~ factor(q11), data=DELL_DATA_cleaned)

summary(FIT.01)

drop1(FIT.01,~.,test="F")

print(model.tables(FIT.01,"means"),digits=10) 

library(car)

Anova(FIT.01, type=3)

TukeyHSD(FIT.01)


# DELL Experiment

FIT.02<- aov(ATT ~ OL + factor(BRAND)*factor(PRODUCT), data=DELL_EXP)

summary(FIT.02)

Anova(FIT.02, type=3)

# Plots

plot(FIT.02)

attach(DELL_EXP)

interaction.plot(factor(BRAND),factor(PRODUCT),ATT) 

FIT.03<- lm(ATT ~ OL + factor(BRAND)*factor(PRODUCT), data=DELL_EXP)
summary(FIT.03)