#
#
#### An excellent example of what appears to be a good linear regression model, 
#### but; upon inspection of the scatterplot matrix...FUNKINESS!!
#

dataset <- read.table("http://www.unt.edu/rss/class/Jon/R_SC/Module6/IntroPsych_Spring2010.txt", 
  header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)
attach(dataset)
subset <- data.frame(number_grade, absences, neuroticism, extroversion, openness)
detach(dataset)

model.1 <- lm(number_grade ~ absences + neuroticism + extroversion + openness, subset)
library(QuantPsyc)
summary(model.1)
round(lm.beta(model.1), 4)

# Funky, but strong, relationships among the variables. 

library(car)
scatterplotMatrix(subset)
cor(subset)

# Notice the clustering of the outcome variable (number_grade) and the non-lineaer 
# relationship between it and the predictor 'absences' (caused by absences being so 
# skewed). Neuroticism and extroversion also appear to be non-normal; but, they 
# still appear to be 'linearly' related to the outcome. 



# END, Feb. 2011