Created
August 28, 2015 00:55
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Linear regression in R
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#------------------------------------------------------------------------------- | |
# 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 |
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