jcruet_bi variate regression model, R

jcruet_bi variate regression model

#X data
x <- c(20,
16,
19.79999924,
18.39999962,
17.10000038,
15.5,
14.69999981,
17.10000038,
15.39999962,
16.20000076,
15,
17.20000076,
16,
17,
14.39999962)

#Y data
y <- c(88.59999847,
71.59999847,
93.30000305,
84.30000305,
80.59999847,
75.19999695,
69.69999695,
82,
69.40000153,
83.30000305,
79.59999847,
82.59999847,
80.59999847,
83.5,
76.30000305)
#mean
mean(x)
#mean
mean(y)
#variance
var(x)
#variance
var(y)
#standard deviation
sd(x)
#standard deviation
sd(y)
#sum of sd x and y
sum (sd(x+y))
#sum of squared deviation
(sd(x)+sd(y))
#corelation of x and y
cor(x,y)
#covariance of x and y
cov(x,y)
#interpret correlation
#My correlation is closer to positive 1, near perfect correlation.
reg <- lm(y ~ x)

summary(reg)
# sqrt(sum(reg$residuals**2)/3)
# reg$fitted.values
# plot(density(reg$residuals))
# 1 - sum(reg$residuals**2) / sum((y-mean(y))^2)
#R^2 statistic, 70% of the variation is explained by the model.
#Data point will fall 60% from the regression model.
#The F statistic is associated with the p value of .0001067
#plot
plot (x,y)


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