Linear regression in R

linear_reg.R

#-------------------------------------------------------------------------------
#               Linear regression  y = a + bx  
#-------------------------------------------------------------------------------

# Fit the model
model1 <- lm(wt ~ disp)

# Parameters and information about the model
model1
coef(model1)
summary(model1)

# Plot the model
abline(model1,col='red',lwd=2)


##################################################################################
#                         OUTPUT
##################################################################################
# > summary(model1)
#
# Call:
# lm(formula = wt ~ disp)
#
# Residuals:
#      Min       1Q   Median       3Q      Max 
# -0.89044 -0.29775 -0.00684  0.33428  0.66525 
# 
# Coefficients:
#             Estimate Std. Error t value Pr(>|t|)    
# (Intercept) 1.5998146  0.1729964   9.248 2.74e-10 ***
# disp        0.0070103  0.0006629  10.576 1.22e-11 ***
# ---
# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Residual standard error: 0.4574 on 30 degrees of freedom
# Multiple R-squared:  0.7885,	Adjusted R-squared:  0.7815 
# F-statistic: 111.8 on 1 and 30 DF,  p-value: 1.222e-11