Worksheets/Week3

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)