Difference between revisions of "Worksheets/Week3"

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=== Part 1 ===
=== Part 1 ===
A factorial experiment was run to investigate the settings that minimize the production of an unwanted side product. The two factors being investigated are called A and B for simplicity.


<html><div data-datacamp-exercise data-lang="r" data-height="auto">
<html><div data-datacamp-exercise data-lang="r" data-height="auto">
     <code data-type="sample-code">
     <code data-type="sample-code">


# Create the design matrix in a quick way in R
# A = additive at 20mL and 30mL for low and high levels
A <- c(-1, +1, -1, +1)
A = c(-1, +1, -1, +1)
B <- c(-1, -1, +1, +1)
 
y <- c(89, 268, 179, 448)
# B = without (-) or with (+) baffles
mod.siderxn <- lm(y ~ A + B + A*B)
B = c(-1, -1, +1, +1)
summary(mod.siderxn)
 
# Response y is the amount of side product formed, y [grams]
y = c(89, 268, 179, 448)
 
# Fit a linear model
model_siderxn = lm(y ~ A + B + A*B)
summary(model_siderxn)
 
 
# Uncomment this line if you run the code in RStudio
#library(pid)
#library(pid)


# Comment this line if you run this code in RStudio
source('https://yint.org/contourPlot.R')
source('https://yint.org/contourPlot.R')
#source('https://yint.org/paretoPlot.R')
 
contourPlot(mod.siderxn)
# See how the two factors affect the response:
contourPlot(model_siderxn)
interaction.plot(A, B, y)
interaction.plot(A, B, y)
interaction.plot(B, A, y)
interaction.plot(B, A, y)
# Make a prediction with this model:
xA = -1
xB = -1
y.hat = predict(model_siderxn, data.frame(A = xA, B = xB))
paste0('Predicted value is: ', y.hat, ' grams of side product.')
    </code>
</div></html>
=== Part 2 ===
Continuing from above, with 2 extra experimental points:
<html><div data-datacamp-exercise data-lang="r" data-height="500px">
    <code data-type="sample-code">
# A = additive at 20mL and 30mL for low and high levels
A = c(-1, +1, -1, +1, 0, 0)
# B = without (-) or with (+) baffles
B = c(-1, -1, +1, +1, -1, +1)
# Response y is the amount of side product formed, y [grams]
y = c(89, 268, 179, 448, 186, 300)
model_siderxn_cp <- lm(y ~ A + B + A*B)
summary(model_siderxn_cp)
# 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')
contourPlot(model_siderxn_cp)
     </code>
     </code>
</div></html>
</div></html>

Latest revision as of 13:35, 26 September 2019

Part 1

A factorial experiment was run to investigate the settings that minimize the production of an unwanted side product. The two factors being investigated are called A and B for simplicity.

# A = additive at 20mL and 30mL for low and high levels A = c(-1, +1, -1, +1) # B = without (-) or with (+) baffles B = c(-1, -1, +1, +1) # Response y is the amount of side product formed, y [grams] y = c(89, 268, 179, 448) # Fit a linear model model_siderxn = lm(y ~ A + B + A*B) summary(model_siderxn) # 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_siderxn) interaction.plot(A, B, y) interaction.plot(B, A, y) # Make a prediction with this model: xA = -1 xB = -1 y.hat = predict(model_siderxn, data.frame(A = xA, B = xB)) paste0('Predicted value is: ', y.hat, ' grams of side product.')

Part 2

Continuing from above, with 2 extra experimental points:

# A = additive at 20mL and 30mL for low and high levels A = c(-1, +1, -1, +1, 0, 0) # B = without (-) or with (+) baffles B = c(-1, -1, +1, +1, -1, +1) # Response y is the amount of side product formed, y [grams] y = c(89, 268, 179, 448, 186, 300) model_siderxn_cp <- lm(y ~ A + B + A*B) summary(model_siderxn_cp) # 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') contourPlot(model_siderxn_cp)