rm(list=ls())

# remove variables from previous session
rm(list=ls())

# read file
dfr <- read.csv(file="FCAT_Mult_grade3.csv",head=TRUE,sep=",") 

#ssrss03 = FCAT Reading Score
#Orfwrcf = Oral Reading Fluency – FCAT Passages

print(dfr$ssrss03)
print(dfr$Orfwrcf)
regr = lm(ssrss03 ~ Orfwrcf, data=dfr)
plot(dfr$ssrss03, dfr$Orfwrcf)

#create artificial data
# objective: the line y = .4 x + 2

nb.pts = 50
x = sample(seq(-5,5,.01), nb.pts)

#---------------------------------
par(mfrow=c(2,2))
fluct = c(.05,.25,.5,2.5)

# Keep intercept fixed (=2) and change the slope
slopes = c(-.5, 0., .5, 2.)
y1 = slopes[1]*x + 2
y2 = slopes[2]*x + 2
y3 = slopes[3]*x + 2
y4 = slopes[4]*x + 2

# Add an error term (the same for each plot)
y1 = y1+rnorm(nb.pts, sd=fluct[3])
y2 = y2+rnorm(nb.pts, sd=fluct[3])
y3 = y3+rnorm(nb.pts, sd=fluct[3])
y4 = y4+rnorm(nb.pts, sd=fluct[3])

plot(x,y1); 
plot(x,y2) 
plot(x,y3); plot(x,y4)
#----------------------------------

plot(x,y1,ylim=c(-10,10),main="slope=-0.5",cex.main=2)
plot(x,y2,ylim=c(-10,10),main="slope=0.",cex.main=2)
plot(x,y3,ylim=c(-10,10),main="slope=0.5",cex.main=2)
plot(x,y4,ylim=c(-10,10),main="slope=2",cex.main=2)

par(mfrow=c(1,1))