Design of Experiment
For this entry, I have been tasked to do data analysis for Case study 1.
Below is the Case Study that I have chosen.
The case study can be retrieved here
I first tabulated the data from Case Study 1 into the template provided.
I did the full factorial data analysis first using the data tabulated, the results below are what I have collected. I have plotted 3 different graphs which showed me how each factor's significance in terms of affecting the mass of unpopped yield.
It can be seen from the graph that power has the steepest gradient followed by microwaving time and lastly the diameter of the bowl. The graph with the steepest gradient would have the highest effect on the mass of unpopped yield and the one with the least steep gradient would have the lowest effect. Thus, the factor with the highest to lowest effect would be Power, Microwaving time, and lastly Diameter of the bowl.
I then proceed to determine whether there is an interaction effect between the 3 different factors. As shown below is the results.
For the interaction of AB, the gradient of both lines are different. Therefore there’s a significant interaction between A and B.
For the interaction of AC, the gradient of both lines are different by a little margin. Therefore there’s an interaction between A and B, but the interaction is small.
Thus, for the full factorial design analysis, power has the most significance followed by microwave time and lastly, the diameter of the bowl. In order to decrease the yield of unpopped popcorns, we can set the power and microwave time to high and since the graph of the diameter of the bowl is almost horizontal, this means that the diameter has almost no effect on the yield and can be set to either high or low.
Fractional Factorial Design Analysis
Next, I moved on to doing fractional factorial design analysis based on the result I tabulated on excel, the runs I chose are #2, 3, 5 & 8. Highlighted in red are the runs I will be doing fractional factorial design on.
