Difference between revisions of "Worksheets/Week2"

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A baked food product:
* T = time used to bake the product
* F = quantity of fat added
<html><div data-datacamp-exercise data-lang="r" data-height="auto">
    <code data-type="sample-code">
# T = time used for baking: (-1) corresponds to 80 minutes and (+1) corresponds to 100 minutes
A <- c(-1, +1, -1, +1)
# F = quantity of fat used: (-1) corresponds to 20 g and (+1) corresponds to 30 grams
B <- c(-1, -1, +1, +1)
# Response y is the crispiness
y <- c(37, 57, 49, 53)
# Fit a linear model
model_crispy <- lm(y ~ A + B + A*B)
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)
    </code>
</div></html>

Revision as of 08:13, 26 September 2019

Worksheets Next step: Week3 →

A baked food product:

  • T = time used to bake the product
  • F = quantity of fat added

# T = time used for baking: (-1) corresponds to 80 minutes and (+1) corresponds to 100 minutes A <- c(-1, +1, -1, +1) # F = quantity of fat used: (-1) corresponds to 20 g and (+1) corresponds to 30 grams B <- c(-1, -1, +1, +1) # Response y is the crispiness y <- c(37, 57, 49, 53) # Fit a linear model model_crispy <- lm(y ~ A + B + A*B) 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)