R Notebook

library(resampledata)

## 
## Attaching package: 'resampledata'

## The following object is masked from 'package:datasets':
## 
##     Titanic

data(Beerwings)
attach(Beerwings)
head(Beerwings)

names(Beerwings)

## [1] "ID"       "Hotwings" "Beer"     "Gender"

plot(Hotwings,Beer,ylab="Beer (Fluid Ounces)",main="Beer and Hotwing Consumption")

beerwingmodel <- lm(Hotwings ~ Beer)
beerwingmodel

## 
## Call:
## lm(formula = Hotwings ~ Beer)
## 
## Coefficients:
## (Intercept)         Beer  
##      3.6329       0.3168

which(Beer == 30)

## [1] 14 15 18 19 20 27 28

actualwings <- Beerwings[14,2]
thirtyounce <- Beerwings[14,"Beer"]
predictedwings <- coef(beerwingmodel)%*%c(1,thirtyounce)
residual <- actualwings - predictedwings
residual

##           [,1]
## [1,] -1.137209

summary(beerwingmodel)

## 
## Call:
## lm(formula = Hotwings ~ Beer)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.2364 -1.3603 -0.1868  1.2658  5.9619 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.63293    1.35859   2.674   0.0124 *  
## Beer         0.31681    0.04739   6.686 2.95e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.022 on 28 degrees of freedom
## Multiple R-squared:  0.6148, Adjusted R-squared:  0.6011 
## F-statistic:  44.7 on 1 and 28 DF,  p-value: 2.953e-07

Today I was reminded of the syntax for predicting variables using a matrix. This is something I had forgotten how to do in regards to the % symbol and etc. above. I would be interested in learning other ways in which you can predict data points for the linear regression.