Difference between revisions of "Worksheets/Week2"

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     <code data-type="sample-code">
     <code data-type="sample-code">


# T = time used for baking: (-1) corresponds to 80 minutes and (+1) corresponds to 100 minutes
T <- c(-1, +1, -1, +1)


# F = quantity of fat used: (-1) corresponds to 20 g and (+1) corresponds to 30 grams
F <- c(-1, -1, +1, +1)
# Response y is the crispiness
y <- c(37, 57, 49, 53)
# Fit a linear model
model_crispy <- lm(y ~ T + F + T*F)
summary(model_crispy)
# Uncomment this line if you run the code in RStudio
#library(pid)
# Comment this line if you run this code in RStudio
source('https://yint.org/contourPlot.R')
# See how the two factors affect the response:
contourPlot(model_crispy )
interaction.plot(T, F, y)
interaction.plot(F, T, y)
# Make a prediction with this model:
xT = -1  # corresponds to 110 minutes
xF = -1  # corresponds to 20 grams of fat
y.hat <- predict(model_crispy, data.frame(T = xT, F = xF))
paste0('Predicted value is: ', y.hat, ' crispiness.')
     </code>
     </code>
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Revision as of 08:44, 26 September 2019

# T = time used for baking: (-1) corresponds to 80 minutes and (+1) corresponds to 100 minutes T <- c(-1, +1, -1, +1) # F = quantity of fat used: (-1) corresponds to 20 g and (+1) corresponds to 30 grams F <- c(-1, -1, +1, +1) # Response y is the crispiness y <- c(37, 57, 49, 53) # Fit a linear model model_crispy <- lm(y ~ T + F + T*F) summary(model_crispy) # Uncomment this line if you run the code in RStudio #library(pid) # Comment this line if you run this code in RStudio source('https://yint.org/contourPlot.R') # See how the two factors affect the response: contourPlot(model_crispy ) interaction.plot(T, F, y) interaction.plot(F, T, y) # Make a prediction with this model: xT = -1 # corresponds to 110 minutes xF = -1 # corresponds to 20 grams of fat y.hat <- predict(model_crispy, data.frame(T = xT, F = xF)) paste0('Predicted value is: ', y.hat, ' crispiness.')