library(poliscidata)
library(ggplot2)
library(dplyr)
library(tidyr)
library(corrplot)

options(scipen = 10)

dat <- states
dat <- dat[, c("demstate13",
            "abort_rate08",
            "gay_support",
            "prochoice",
            "obama2012")]
head(dat)
str(dat)

dat %>%
gather() %>%
ggplot(aes(value)) +
facet_wrap(~ key, scales = "free") +
geom_histogram()

print("Mean of each variable:")
apply(dat, 2, mean)

print("SD of each variable:")
apply(dat, 2, sd)

dat %>%
gather() %>%
ggplot(aes(x= key, y = value)) +
geom_boxplot()

# Dependent Variable: demstate13
# Independent Variables: abort_rate08, gay_support, prochoice, obama2012

# H0: abort_rate08 has no significant effect on demstate13
# H1: abort_rate08 hassignificant effect on demstate13

# H0: gay_support has no significant effect on demstate13
# H1: gay_support hassignificant effect on demstate13

# H0: prochoice has no significant effect on demstate13
# H1: prochoice hassignificant effect on demstate13

# H0: obama2012 Grad has no significant effect on demstate13
# H1: obama2012 Grad hassignificant effect on demstate13

pairs(dat)
# Show scatter plots between each variables.

corrplot(cor(dat))
# Plotting correlation plot towards +1, shows positive
# towards -1, shows negative coorelation.

cor.test(dat$demstate13, dat$abort_rate08

1. Read a paper about the application or theory of the logistic reg...