CP5070-2021-2B02-GROUP4-BEHPEIJIE

1.DOE PRACTICAL TEAM MEMBER 1. Person A (Jun Weng) 2. Person B (Roy) 3. Person C (Adam) 4. Person D (yongjie) 5. Person E (peijie) 2.FULL FACTORIAL DATA TABLE 3.FRACTIONAL FACTORIAL DATA TABLE SCOPE OF THE TEST The human factor is assumed to be negligible. Therefore different user will not have any effect on the flying distance of projectile. Flying distance for catapult A and catapult B is collected using the factors below: Arm length =  __ 28 __cm Start angle = ___ 20 __ degree Stop angle = ___ 60 __ degree Step 1: State the statistical Hypotheses: State the null hypothesis (H 0 ): The catapult that has 20cm arm length and start with angle 20 degree  and stop with 60 degree, the flying distance for both catapult A and B have no difference.   State the alternative hypothesis (H 1 ): The catapult that has 20cm arm length and start with angle 20 degree  and stop with 60 degree, the flying distance for both catapult A and B are different. Step 2: Formulate an analysis ...

FULL  FACTORIAL DESIGN Stop angle has the largest impact on the distance traveled by the projectile while arm length and start angle are relatively similar in terms of significance. FRACTIONAL  FACTORIAL DESIGN The results are similar to the full factorial design as the most significant factor is the stop angle followed by arm length and start angle. CONCLUSION I would recommend the full factorial. As by having only 16 data points, we already have cut down the number of runs to 2 each. So the data is already not as reliable. If we did fractional factorial, it might not give enough data for us to get accurate analysis and the right interactions. Furthermore, doing a full factorial for 16 data points is not considerably difficult, hence there should be no issues with doing the full factorial.