#load data EAMMIdata <- read.csv(file = "https://bit.ly/2NGiyrS", header = TRUE) head(EAMMIdata)

#The variables we'll use are age and Political_views #The following code discard all the rows that do not have valid values in the attributes age and Political_views EAMMIdata <- filter(EAMMIdata,age!="NA") EAMMIdata <- filter(EAMMIdata,Political_views!="NA") #check if there is any missing data in age and Political_views favstats(~Political_views,data=EAMMIdata) favstats(~age,data=EAMMIdata)

#obtain the favstats of Political_views EAMMI.stats = favstats(~ Political_views, data = EAMMIdata) #generate a histogram gf_histogram(~Political_views, data = EAMMIdata, fill = "pink", color = "black", bins = "7") %>% #draw a vertical line on the mean gf_vline(xintercept = EAMMI.stats$mean, color = "blue") #generate a point plot gf_point(Political_views~age,data=EAMMIdata) %>% #draw a horizontal line to emulate the simple model gf_hline(yintercept=EAMMI.stats$mean, color= "blue")

#fit a simple model Empty.model <- lm(Political_views ~ NULL, data= EAMMIdata) Empty.model

#Make the ANOVA table anova(Empty.model)

EAMMI.stats <- favstats(~ Political_views, data= EAMMIdata) #obtain 1000 means from samples generated from a normal distribution with means and standard deviations that are the same as our original sample sampled_b0s <- do(1000) * mean(rnorm(EAMMI.stats$n, EAMMI.stats$mean, EAMMI.stats$sd)) head(sampled_b0s) gf_histogram(~mean, data=sampled_b0s)

#run favstats on the sampling distribution sampled_b0s.stats <- favstats(sampled_b0s$mean) sampled_b0s.stats #obtain sum of squared errors of the sampling distribution anova(lm(sampled_b0s$mean~NULL))

confint(Empty.model)

#sort the values of the sampling distribution of the mean sampled_b0s <- arrange(sampled_b0s,mean) n=sampled_b0s.stats$n #Higher bound based on our sampling distribution sampled_b0s$mean[0.975*n] #Lower bound based on our sampling distribution sampled_b0s$mean[0.025*n]

#divide age into 3 equally sized groups EAMMIdata$agefactor <- ntile(EAMMIdata$age, 3) #recode 1 as young, 2 as middle, 3 as old EAMMIdata$agefactor <- factor(EAMMIdata$agefactor, levels =c(1,2,3), labels = c("young", "middle","old")) #sanity check to see if code works as intended tally(EAMMIdata$agefactor) head(select(EAMMIdata,age,agefactor))

#Create a jitter plot gf_jitter(Political_views ~ agefactor, data = EAMMIdata) #box plot gf_boxplot(Political_views ~ agefactor, data =EAMMIdata)

#fit a linear model with agefactor as the explanatory variable Political.model <- lm(Political_views ~ agefactor, data = EAMMIdata) Political.model

#obtain the anova table anova(Political.model) supernova(Political.model)

#create 1000 linear models, each from a resampled data resampled_models <- do(1000) * lm(Political_views~agefactor, data=resample(EAMMIdata,1723)) #see what it looks like head(resampled_models) #we see that b0,b1,b2 are stored in columns #visualization of the three sampling distributions gf_histogram(~resampled_models$Intercept) gf_histogram(~resampled_models$agefactormiddle) gf_histogram(~resampled_models$agefactorold) #gf_histogram(~resampled_models$F)

#run favstats on the sampling distributions favstats(~resampled_models$Intercept) #b0 favstats(~resampled_models$agefactormiddle) #b1 favstats(~resampled_models$agefactorold) #b2

#construct confident intervals for the model confint(Political.model)

supernova(Political.model)

#point plot gf_jitter(Political_views ~ age, data = EAMMIdata) %>% #create a line to emulate the linear model gf_lm(color = "orange")

#fit a linear model for the two continuous variables quant.model <- lm(Political_views ~ age, data = EAMMIdata) quant.model

anova(quant.model)

#obtain 1000 b1s by resampling our original sample SDoB1 <- do(1000)* b1(lm(Political_views ~ age, data = resample(EAMMIdata,1724))) head(SDoB1) #create a histogram gf_histogram(~b1, data = SDoB1)

favstats(SDoB1$b1)

#run confident interval on the model confint(quant.model)

#obtain anova table for model comparison supernova(quant.model)