DOE

 Hello and welcome! Today, I will be documenting on how I applied FULL & FRACTIONAL data analysis. I am going to determine the effect of the different factors and rank them on how significant each factor is.

I am to analyse CASE STUDY 1. These are the 3 factors:

A: Diameter of the bowls to contain the corn, 10 cm and 15 cm

B: Microwaving time, 4 mins and 6 mins

C: Power of microwave setting, 75% and 100%

Data Collected:


Google Drive Link: https://docs.google.com/file/d/1UtFq9WKG-ZKQU7mfzyCree01LKiKdr1K/edit?usp=docslist_api&filetype=msexcel 

Ranking of factors from MOST to LEAST significant: C, B & A

FULL DESIGN DATA ANALYSIS

The following interactions are:

A x B

A x C

B x C

After selecting the runs for each interaction analysis and completing the necessary calculations. This is what my graphs looks like:

  1. Interaction effect of A x B
For these selections, I have selected Runs 1,5,3,8 for Low B and Runs 2,7,4,6 for High B.

The graph shows that the gradient of A at High B and A at Low B are different. Hence, there will be significant interaction between the two factors.

   2. Interaction effect of A x C

For these selections, I have selected Runs 2,8,1,4 for Low C and Runs 3,7,5,6  for High C.

The graph shows that the gradient of A at Low C and A at High C are ever so slightly different. This means that there will be a slight interaction between the two factors but it is not as significant compared to A x B.

  3. Interaction effect of B x C

For these selections, I have selected Runs 1,8,2,4 for Low C and Runs 3,5,6,7 for High C.


The graph shows that the gradient of B at Low C and B at High C are significantly different. Hence, there would be a significant interaction between B x C.

FRACTIONAL DESIGN DATA ANALYSIS

The runs I have selected for fractional data analysis are 4, 5, 7 & 8.

Ranking of factors from MOST to LEAST significant: C, B & A

The following interactions are:

A x B

A x C

B x C

  1. Interaction effect of A x B


The graph shows that the gradient of A at Low B and A at High B are significantly different. It also shows both line intersecting each other at one point. Hence, there would be a significant interaction between A x B.

   2. Interaction effect of A x C

The graph shows that the gradient of A at Low C and A at High C are significantly different. It also shows both line are soon coming to intersecting each other at one point. Hence, there would be a significant interaction between A x C.

  3. Interaction effect of B x C

The graph shows that the gradient of B at Low C and B at High C are significantly different. Hence, there would be a significant interaction between B x C.

CONCLUSION

FULL Factorial

BxC, AxB, AxC

Most to least significant 

FRACTIONAL Factorial

AxB, AxC, BxC

Most to least significant


The most influential factor is B while the factor that has the most significant change to the amount of corn popped is C.









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