Hi, I'm struggling with this management 361 question, called the Rat Freeze Problem.

Poindexter thought the trap's failure might have something to do with quality problems at the new supplier of his LOX spray nozzles. Specifically, he suspected that the supplier was not properly holding a key diameter at the throat of that nozzle. The Datasets Poindexter has two datasets to use in exploring the problem. The first dataset was obtained two months ago when the process was known to be definitely under control. It consists of 50 individual nozzle diameters. The diameters are normally distributed. That dataset can be found on the first sheet in the Excel workbook (labeled "50 Throat Diameters"). Poindexter's specification on that diameter was 0.1000 plus or minus 0.0050. The second dataset (which can be found on the sheet labeled "20 Samples of Size 5") consists of 20 samples of 5 nozzle throat diameters. This data was taken more recently (just a week ago) and possibly after a problem had developed at the supplier. That is, this sample was taken when the process may or may not have been under control...we just don't know. Each horizontal row in the second dataset represents one sample consisting of 5 observations (5 diameters). Each row gives that sample's 5 individual diameters in columns B to F, then gives the whole sample's mean diameter in column H, and the sample's range of diameters in column I. The 20 samples are listed in the order in which they were taken. That is, sample 1 (the first row) was taken first, sample 2 (the second row) was taken second, etc. Again, this data was taken after a problem may have developed so we don't know if the process was still under control or if it was still normally distributed. Assume that there is nothing wrong with the design of the Rat Freeze. That is, if the Rat Freeze is manufactured to specification, the rat will freeze on the spot. That means, that either the rat was able to move more than an inch due to a failure in the supplier's manufacturing process or there really isn't really a problem at all (the fact that the rat didn't die instantly was just a very unlikely fluke ... perhaps the rat in question had especially good genetics, ate well, and exercised often). You need to find out what the truth is; is there a problem with the supplier's process or not?

1.) Does the first dataset show that the process had a problem two months ago (yes or no)? If yes, which type of problem did it have? Show whatever calculations/charts you need to support your answer. You may or may not need both calculations and charts. No points will be awarded for unsupported answers since they may be just wild guesses.

2.) Does the second dataset show that a problem existed a week ago? If so, which type of problem? Be careful...you may need to use more than one type of chart to answer this problem. Show whatever calculations or charts you need to support your answer.

Dataset 1

Item # Throat Diameters 1 0.1011 2 0.1011 3 0.0999 4 0.1009 5 0.1008 6 0.0997 7 0.1028 8 0.0991 9 0.0989 10 0.1001 11 0.0996 12 0.0996 13 0.1026 14 0.1016 15 0.1020 16 0.1027 17 0.1012 18 0.1005 19 0.1006 20 0.1010 21 0.1037 22 0.1011 23 0.1013 24 0.0989 25 0.1024 26 0.1026 27 0.1004 28 0.1007 29 0.1021 30 0.1013 31 0.0998 32 0.0991 33 0.1016 34 0.1012 35 0.0992 36 0.1004 37 0.1021 38 0.1023 39 0.1002 40 0.1021 41 0.0982 42 0.1007 43 0.1002 44 0.1011 45 0.1008 46 0.1014 47 0.0997 48 0.1020 49 0.1025 50 0.0997

Dataset 2

Sample | Mean / Range 1 0.0982 0.0989 0.1010 0.0993 0.1005 | 0.0996 / 0.0028 2 0.0997 0.1028 0.0991 0.0980 0.1001 | 0.0999 / 0.0048 3 0.0998 0.1001 0.1025 0.0991 0.0991 | 0.1001 / 0.0034 4 0.0998 0.0991 0.1016 0.1012 0.0992 | 0.1002 / 0.0025 5 0.0982 0.1007 0.1002 0.1000 0.1008 | 0.1000 / 0.0026 6 0.1028 0.0989 0.1008 0.0997 0.1016 | 0.1007 / 0.0039 7 0.1011 0.1011 0.0999 0.1009 0.1008 | 0.1008 / 0.0011 8 0.1011 0.1006 0.1013 0.1011 0.1007 | 0.1009 / 0.0007 9 0.1026 0.1008 0.1011 0.1004 0.0999 | 0.1010 / 0.0027 10 0.1014 0.0997 0.1020 0.1025 0.0997 | 0.1011 / 0.0029 11 0.0996 0.0996 0.1026 0.1010 0.1020 | 0.1010 / 0.0031 12 0.0996 0.1021 0.1021 0.0996 0.1023 | 0.1011 / 0.0028 13 0.1027 0.1012 0.1005 0.1006 0.1010 | 0.1012 / 0.0022 14 0.1012 0.1012 0.1008 0.1015 0.1002 | 0.1010 / 0.0013 15 0.1014 0.1016 0.1015 0.1000 0.1010 | 0.1011 / 0.0016 16 0.1011 0.1002 0.0997 0.1030 0.1020 | 0.1012 / 0.0033 17 0.1026 0.1004 0.1007 0.1021 0.1013 | 0.1014 / 0.0022 18 0.1004 0.1021 0.1017 0.1002 0.1021 | 0.1013 / 0.0019 19 0.1037 0.1011 0.1013 0.0989 0.1024 | 0.1015 / 0.0048 20 0.1004 0.1009 0.1020 0.1021 0.1026 | 0.1016 / 0.0022 Average 0.1008 / 0.0026