Difference between revisions of "Worksheets/Week3"
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# A = additive at 20mL and 30mL for low and high levels | # A = additive at 20mL and 30mL for low and high levels | ||
A | A = c(-1, +1, -1, +1) | ||
# B = without (-) or with (+) baffles | # B = without (-) or with (+) baffles | ||
B | B = c(-1, -1, +1, +1) | ||
# Response y is the amount of side product formed, y [grams] | # Response y is the amount of side product formed, y [grams] | ||
y | y = c(89, 268, 179, 448) | ||
# Fit a linear model | # Fit a linear model | ||
model_siderxn = lm(y ~ A + B + A*B) | |||
summary( | summary(model_siderxn) | ||
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# See how the two factors affect the response: | # See how the two factors affect the response: | ||
contourPlot( | contourPlot(model_siderxn) | ||
interaction.plot(A, B, y) | interaction.plot(A, B, y) | ||
interaction.plot(B, A, y) | interaction.plot(B, A, y) | ||
| Line 34: | Line 34: | ||
xA = -1 | xA = -1 | ||
xB = -1 | xB = -1 | ||
y.hat | y.hat = predict(model.siderxn, data.frame(A = xA, B = xB)) | ||
paste0('Predicted value is: ', y.hat, ' grams of side product.') | paste0('Predicted value is: ', y.hat, ' grams of side product.') | ||
</code> | </code> | ||
Revision as of 13:34, 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.
Part 2
Continuing from above, with 2 extra experimental points: