Women Datasets

For this discussion board I will be using the women dataset to predict the height using the weight.

df <- women

head(df)
##   height weight
## 1     58    115
## 2     59    117
## 3     60    120
## 4     61    123
## 5     62    126
## 6     63    129

Including Plots

You can also embed plots, for example:

lm <- lm(weight ~ height, data = df)

print(lm)
## 
## Call:
## lm(formula = weight ~ height, data = df)
## 
## Coefficients:
## (Intercept)       height  
##      -87.52         3.45
plot( weight ~ height, data = df)
abline(lm)

summary(lm)
## 
## Call:
## lm(formula = weight ~ height, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7333 -1.1333 -0.3833  0.7417  3.1167 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -87.51667    5.93694  -14.74 1.71e-09 ***
## height        3.45000    0.09114   37.85 1.09e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.525 on 13 degrees of freedom
## Multiple R-squared:  0.991,  Adjusted R-squared:  0.9903 
## F-statistic:  1433 on 1 and 13 DF,  p-value: 1.091e-14
plot(fitted(lm),resid(lm))

qqnorm(resid(lm))
qqline(resid(lm))

par(mfrow=c(2,2)) 
plot(lm)

There is a curved pattern in the residuals so a linear model might not be a great fit for the data