Difference between revisions of "Worksheets/Week3"
Kevin Dunn (talk | contribs) (→Part 3) |
Kevin Dunn (talk | contribs) (→Part 4) |
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# Linear model using S, P and S*P to predict the response | # Linear model using S, P and S*P to predict the response | ||
model.sales.mistake <- lm(y ~ S*P) | |||
summary( | summary(model.sales.mistake) | ||
# Uncomment this line if you run the code in RStudio | # Uncomment this line if you run the code in RStudio | ||
| Line 124: | Line 124: | ||
source('https://yint.org/contourPlot.R') | source('https://yint.org/contourPlot.R') | ||
contourPlot( | contourPlot(model.sales.mistake) | ||
</code> | </code> | ||
</div></html> | </div></html> | ||
Revision as of 09:02, 11 March 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:
Part 3
Your family runs a small business selling products online. The first factor of interest is whether to provide free shipping over €30 or over €50. The second factor is whether or not the purchaser must first create a profile before completing the transaction. The purchaser can still complete their transaction without creating a profile. Below are the data collected, showing the 8 experiments.
Part 4
Continuing with the case above:
Part 5
Your group is developing a new product, but have been struggling to get the product’s stability, measured in days, to the level required. You are aiming for a stability value of 50 days or more.