this is a Busn312 course with problems

Anonymous
timer Asked: Jul 29th, 2014

Question Description

Hello, I am looking for help with my  BUSN 312 course. Please look over this sheet and tell me if you can help me with this, Thanks

Ch7HmwkDatawk3.xlsx

Mean Charts Mean Charts: Sample Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Total 1 15.85 16.12 16.00 16.20 15.74 15.94 15.75 15.82 16.04 15.64 16.11 15.72 15.85 15.73 16.20 16.12 16.01 15.78 15.84 15.92 16.11 15.98 16.05 16.01 16.08 Observations 2 3 16.02 15.83 16.00 15.85 15.91 15.94 15.85 15.74 15.86 16.21 16.01 16.14 16.21 16.01 15.94 16.02 15.98 15.83 15.86 15.94 16.00 16.01 15.85 16.12 15.76 15.74 15.84 15.96 16.01 16.10 16.08 15.83 15.93 15.81 16.04 16.11 15.92 16.05 16.09 16.12 16.02 16.00 15.82 15.89 15.73 15.73 16.01 15.89 15.78 15.92 4 15.93 16.01 15.83 15.93 16.10 16.03 15.86 15.94 15.98 15.89 15.82 16.15 15.98 16.10 15.89 15.94 15.68 16.12 16.12 15.93 15.88 15.89 15.93 15.86 15.98 𝑅̅= 𝑥Ӗ = = x z-value A2 UCL LCL Average 𝑋ത 15.91 16.00 15.92 15.93 15.98 16.03 15.96 15.93 15.96 15.83 15.99 15.96 15.83 15.91 16.05 15.99 15.86 16.01 15.98 16.02 16.00 15.90 15.86 15.94 15.94 398.67 Range R Mean charts are used to monitor They are good for detecting anom isolating the causes of those irreg resolve issues. 0.19 0.27 0.17 0.46 0.47 0.20 0.46 0.20 0.21 0.30 0.29 0.43 0.24 0.37 0.31 0.29 0.33 0.34 0.28 0.20 0.23 0.16 0.32 0.15 0.30 7.17 0.29 15.95 Center line of the control data is the average of all the av 0.14 This information was given in the problem data 0.07 3 This information was given in the problem data 0.73 From Table 6-1 on page 194 in text 16.15626 15.73754 Mean Charts: Mean charts are used to monitor changes in a process. They are good for detecting anomalies and then isolating the causes of those irregularities in order to resolve issues. rol data is the average of all the averages iven in the problem data iven in the problem data e 194 in text Range Charts Range Charts: Sample Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Total 1 15.85 16.12 16.00 16.20 15.74 15.94 15.75 15.82 16.04 15.64 16.11 15.72 15.85 15.73 16.20 16.12 16.01 15.78 15.84 15.92 16.11 15.98 16.05 16.01 16.08 Observations 2 3 16.02 15.83 16.00 15.85 15.91 15.94 15.85 15.74 15.86 16.21 16.01 16.14 16.21 16.01 15.94 16.02 15.98 15.83 15.86 15.94 16.00 16.01 15.85 16.12 15.76 15.74 15.84 15.96 16.01 16.10 16.08 15.83 15.93 15.81 16.04 16.11 15.92 16.05 16.09 16.12 16.02 16.00 15.82 15.89 15.73 15.73 16.01 15.89 15.78 15.92 4 15.93 16.01 15.83 15.93 16.10 16.03 15.86 15.94 15.98 15.89 15.82 16.15 15.98 16.10 15.89 15.94 15.68 16.12 16.12 15.93 15.88 15.89 15.93 15.86 15.98 R D3 D4 UCL LCL Average 𝑋̅ 15.91 16.00 15.92 15.93 15.98 16.03 15.96 15.93 15.96 15.83 15.99 15.96 15.83 15.91 16.05 15.99 15.86 16.01 15.98 16.02 16.00 15.90 15.86 15.94 15.94 398.67 0.00 2.28 0.6612 0.0000 Range R Range change measure the 0.19 0.27 0.17 0.46 0.47 0.20 0.46 0.20 0.21 0.30 0.29 0.43 0.24 0.37 0.31 0.29 0.33 0.34 0.28 0.20 0.23 0.16 0.32 0.15 0.30 7.17 0.29 From Table 6-1 on page 194 in text Range Charts: Range change measure the variability in the data. e 194 in text P-Charts Sample Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Total Use P-charts to measure the proportion of sample that is defective--use this type when you know both the t Number of Defective Tires 3 2 1 2 1 3 3 2 1 2 3 2 2 1 1 2 4 3 1 1 40 z= p = σP = UCL= LCL= Number of Observations 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 400 Fraction Defective 0.15 0.10 0.05 0.10 0.05 0.15 0.15 0.10 0.05 0.10 0.15 0.10 0.10 0.05 0.05 0.10 0.20 0.15 0.05 0.05 To create the chart, select the first column of fraction defective data. Choose Insert/Chart/L Fraction 0.25 0.20 0.15 0.10 0.05 0.00 0 3.00 0.100 0.067 0.301 0.00 Round any negative number up to -0- 5 10 en you know both the total sample size and the number of defects. lect the first column of data, then hold the ctrl key and select the . Choose Insert/Chart/Line and choose a type. Fraction Defective Fraction Defective 15 20 25 C-Charts Sample Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Total Use C-charts to measure the number of defects per unit Number of Complaints 3 2 3 1 3 3 2 1 3 1 3 4 2 1 1 1 3 2 2 3 44 z= c = UCL= LCL= 3.00 2.200 6.650 0.000 Round any negative number up to -0- Computing CP Bottling Machine σ USL-LSL 6σ A 0.050 0.40 0.30 1.33 ✓ CP =1 B 0.100 0.40 0.60 0.67 CP ≤1 C 0.200 0.40 1.20 0.33 CP ≥1 CP Process variability just meets standards Process variability is outside the range of specifications Process variability is tighter than the range of specifications and exceeds minimal capability Problem 1 Sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 x = x z-value UCL LCL Observations 5.80 5.90 6.00 6.10 6.20 6.00 5.90 5.90 6.10 5.90 6.00 5.80 6.00 5.90 5.90 6.10 5.97 5.97 0.1138 0.0569 3 6.14 5.80 Standard 0.0285 0.0047 0.0010 0.0172 0.0535 0.0010 0.0047 0.0047 0.0172 0.0047 0.0010 0.0285 0.0010 0.0047 0.0047 0.0172 0.1944 Problem 4 Sample Data 1 2 3 4 5 6 7 8 9 10 1 2 3 4 16.40 15.97 15.91 16.20 15.87 15.43 16.43 15.50 16.13 15.68 16.11 16.10 16.00 16.21 16.21 15.49 16.21 15.92 16.21 16.43 15.90 16.20 16.04 15.93 16.34 15.55 15.99 16.12 16.05 16.20 15.78 15.81 15.92 15.95 16.43 15.92 16.00 16.02 16.01 15.97 A UCL LCLx = x Mean Range CL UCL 0.73 From table 6-1 16.3 15.7 1.20 1.00 0.80 0.60 x x = 0.40 0.20 0.00 1 2 3 Mean 4 5 CL 6 7 UCL 8 LCL 9 LCL 9 LCL 10 Problem 6 X bar chart Sample X 1 2 3 4 5 CL A2 UCL LCL r-chart CL D3 D4 UCL LCL R 12.10 11.80 12.30 11.50 11.60 12.00 0.7 0.4 0.6 0.4 0.9 = x + A2 ( R ) = x − A 2 (R ) Problem 10 1 2 3 4 5 6 7 8 9 10 11 12 Cbar Zvalue Sigma LCL UCL Number of Errors UCL CL 4 0.00 5 0.00 6 0.00 6 0.00 3 0.00 2 0.00 6 0.00 7 0.00 3 0.00 4 0.00 3 0.00 4 0.00 8 LCL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 0 0 0 0 0 0 0 0 0 0 0 7 6 5 4 3 2 1 0 1 2 3 4 5 Number of Errors 6 7 UCL 8 UCL 9 CL 10 11 LCL 12 Extra Credit Since you do not know how many total units were processed to result in these errors (batches), you only know the amount of errors per day each week, you will choose to use a c-chart for this set of data. Standard Material Defect Type Uneven edges Cracks Scratches Air bubbles Thickness variation M Week 1 T W TH F M Week 2 T W TH F M Week 3 T W TH F M Week 4 T W TH F These limits will set the standard--then you are looking to see if the other material can meet this standard Super Plastic Defect Type Uneven edges Cracks Scratches Air bubbles Thickness variation M Week 1 T W TH F M Week 2 T W TH F M Week 3 T W TH F M Week 4 T W TH F =SUM(B5:U5) =V5/4 Total Avg/WK c-bar z value UCL LCL Total =AVERAGE(W5:W9) 3 = c+ z* c = c − z* c Avg/WK Compare these values Since you do not know how many total units were processed to result in these errors (batches), you only know the amount of errors per day each week, you will choose to use a c-chart for this set of data. Standard Material Defect Type Air bubbles Cracks Scratches Thickness variation Uneven edges M Week 1 T W TH F M Week 2 T W TH F M Week 3 T W TH F M Week 4 T W TH F M Week 1 T W TH F M Week 2 T W TH F M Week 3 T W TH F M Week 4 T W TH F Super Plastic Defect Type Cracks Air bubbles Uneven edges Thickness variation Scratches 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Standard Material % Defective Super Plastic % Defective Total 0 0 0 0 0 0 Total 0 0 0 0 0 0 Standard Super Material % Plastic % Defective Defective #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Super Plastic % Defective #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0!
Chapter 7-Problem 2 N D T C S a. b. kanbans pks/min min pks/ctn Remember that D and T need to be in the same units, in this case time Chapter 7-Problem 4 N D T C S kanbans pcs/min min pcs/ctn pcs Remember that D and T need to be in the same units Chapter 7-Problem 6 D=( D N T C S NC )−S T units/min kanbans min unt/cont units Remember that D and T need to be in the same units Chapter 7-Problem 8 N D T C S kanbans unt/min min unt/cont Remember that D and T need to be in the same units

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