Difference between revisions of "Worksheets/Week2"
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<html><div data-datacamp-exercise data-lang="r" data-height="auto"> | <html><div data-datacamp-exercise data-lang="r" data-height="auto"> | ||
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# T = time used for baking: (-1) corresponds to 80 minutes and (+1) corresponds to 100 minutes | # T = time used for baking: (-1) corresponds to 80 minutes and (+1) corresponds to 100 minutes | ||
T | T = c(-1, +1, -1, +1) | ||
# F = quantity of fat used: (-1) corresponds to 20 g and (+1) corresponds to 30 grams | # F = quantity of fat used: (-1) corresponds to 20 g and (+1) corresponds to 30 grams | ||
F | F = c(-1, -1, +1, +1) | ||
# Response y is the crispiness | # Response y is the crispiness | ||
y | y = c(37, 57, 49, 53) | ||
# Fit a linear model | # Fit a linear model | ||
model_crispy | model_crispy = lm(y ~ T + F + T*F) | ||
summary(model_crispy) | summary(model_crispy) | ||
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# Make a prediction with this model: | # Make a prediction with this model: | ||
xT = | xT = +2 # corresponds to 110 minutes | ||
xF = -1 # corresponds to 20 grams of fat | xF = -1 # corresponds to 20 grams of fat | ||
y.hat | y.hat = predict(model_crispy, data.frame(T = xT, F = xF)) | ||
paste0('Predicted value is: ', y.hat, ' crispiness.') | paste0('Predicted value is: ', y.hat, ' crispiness.') | ||
</code> | </code> | ||
</div></html> | </div></html> | ||
Latest revision as of 13:33, 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 = +2 # 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.')