Fluent Essays

Submitted in PDF format only.  Do not submit Excel spreadsheets or datasets.  Please copy and paste ALL prompts in your write up with your response presented beneath.  Respond in complete sentences. Respond to ALL requested actions.  Please format to class expectations  all charts and tables that you generate Directions: Examine the dataset for this assignment and then respond to the prompts below.  The prompts are open-ended so please take the time to provide a complete detailed and statistically justified response using concepts introduced in this class.. 1). On visual inspection of the dataset file only,  which of the following 3 statistical tools, explored in class, may be possible candidates to use to perform an analysis of this dataset ? Reference specific variables , and variable characteristics, as you explain your choice(s). Simple Regression One-way ANOVA Time Series Analysis 2) Select ONLY one of your analysis choices from #1.  State your selection. 3) Perform all needed analysis steps on the dataset using your choice technique stated in #2.  When performing your analysis,  you are not permitted to use exact variable assignment combinations that have been assigned in previous homework assignments using this dataset. Report all charts, tables, and interpretations used in your analysis.. 4) Report the conclusion from your analysis intro Control charts To create a control chart are used to monitor and improve processes collect subgroups (samples) of process output over time data is collected over time calculate sample statistics for each subgroup past performances are use to predict future outcomes plot the statistics over time add control limits (ususally within +- 3 standard deviations of the statistic of measure Phase 1 control charts Goals are to find patterns over time and points that fall outside limits analyzed at the start of a process to determine where improvements are needed Phase 2 control charts analyed after improvements have been imposed Causes of variation special and common special causes are correctable without changing the process common cause variation is inherit in the process (random) common cause variation (no points outside limits) - stable process - in control - predictable A rule of thumb is 8 or more consecutive points above or below the center line may indicate a trend special cause variation (point outside limit) - out of control process unpredictable p chart used for categorical variables ni = Number of observations to find p-bar (mean) divide sum of pi by the number of observations number of Days 10 subgroup size 100 Generate the plot p-bar (p-bar(1-p-bar))/ni UCL LCL pi LCL UCL Day defects proportion (pi) defects per day 0.147 0.00125391 0.2532317749 0.0407682251 0.12 0.041 0.253 1 12 0.12 0.14 0.041 0.253 2 14 0.14 0.1 0.041 0.253 3 10 0.1 0.18 0.041 0.253 4 18 0.18 0.21 0.041 0.253 5 21 0.21 0.14 0.041 0.253 6 14 0.14 0.15 0.041 0.253 7 15 0.15 0.12 0.041 0.253 8 12 0.12 0.15 0.041 0.253 9 15 0.15 0.16 0.041 0.253 10 16 0.16 sum of pi 1.47 p-bar (mean) 0.147 pi 0.12 0.14000000000000001 0.1 0.18 0.21 0.14000000000000001 0.15 0.12 0.15 0.16 LCL 4.0768225092489396E-2 4.0768225092489396E-2 4.0768225092489396E-2 4.0768225092489396E-2 4.0768225092489396E-2 4.0768225092489396E-2 4.0768225092489396E-2 4.0768225092489396E-2 4.0768225092489396E-2 4.0768225092489396E-2 UCL 0.25323177490751059 0.25323177490751059 0.25323177490751059 0.25323177490751059 0.25323177490751059 0.25323177490751059 0.25323177490751059 0.25323177490751059 0.25323177490751059 0.25323177490751059 Number of Days Probability c chart monitors errors in an area of opportunity (space, time, etc) number of areas(time) sampled (n) 10 ci is the number of errors in area i Graph the chart Day errors (ci) Ci UCL LCL 1 7 2 3 3 6 4 3 5 4 6 5 7 3 8 5 SQRT is the square root feature in Excel 9 2 10 0 sum ci c-bar (Mean) r chart Use when observation size is 10 or less Is the process range in control? number of days collection occurs (k) 10 observation size (use in control constants table) 5 Graph the process From Control table Day 5 delivery times collected each day (Xi) Ri R-bar D3 D4 UCL LCL 1 6.7 11.7 9.7 7.5 7.8 2 7.6 11.4 9 8.4 9.2 3 9.5 8.9 9.9 8.7 10.7 4 11 9.9 11.3 11.6 8.5 5 8.3 8.4 9.7 9.8 7.1 D3 and D4 are control group constants 6 9.4 9.3 8.2 7.1 6.1 they represent relations between variation and mean for a given sample size 7 10 10.7 9 8.2 11 values are found in tables 8 9.5 10.5 7 8.6 10.1 9 7.8 9 12 9.1 11.7 10 9.9 10.1 8.9 9.6 7.1 sum Ri R-bar Table of Control Chart Constants X-bar Chart for sigma R Chart Constants S Chart Constants Constants estimate Sample Size = m A2 A3 d2 D3 D4 B3 B4 2 1.880 2.659 1.128 0 3.267 0 3.267 3 1.023 1.954 1.693 0 2.574 0 2.568 4 0.729 1.628 2.059 0 2.282 0 2.266 5 0.577 1.427 2.326 0 2.114 0 2.089 6 0.483 1.287 2.534 0 2.004 0.030 1.970 7 0.419 1.182 2.704 0.076 1.924 0.118 1.882 8 0.373 1.099 2.847 0.136 1.864 0.185 1.815 9 0.337 1.032 2.970 0.184 1.816 0.239 1.761 10 0.308 0.975 3.078 0.223 1.777 0.284 1.716 11 0.285 0.927 3.173 0.256 1.744 0.321 1.679 12 0.266 0.886 3.258 0.283 1.717 0.354 1.646 13 0.249 0.850 3.336 0.307 1.693 0.382 1.618 14 0.235 0.817 3.407 0.328 1.672 0.406 1.594 15 0.223 0.789 3.472 0.347 1.653 0.428 1.572 16 0.212 0.763 3.532 0.363 1.637 0.448 1.552 17 0.203 0.739 3.588 0.378 1.622 0.466 1.534 18 0.194 0.718 3.640 0.391 1.608 0.482 1.518 19 0.187 0.698 3.689 0.403 1.597 0.497 1.503 20 0.180 0.680 3.735 0.415 1.585 0.510 1.490 21 0.173 0.663 3.778 0.425 1.575 0.523 1.477 22 0.167 0.647 3.819 0.434 1.566 0.534 1.466 23 0.162 0.633 3.858 0.443 1.557 0.545 1.455 24 0.157 0.619 3.895 0.451 1.548 0.555 1.445 25 0.153 0.606 3.931 0.459 1.541 0.565 1.435 Control chart constants for X-bar, R, S, Individuals (called "X" or "I" charts), and MR (Moving Range) Charts. NOTES: To construct the "X" and "MR" charts (these are companions) we compute the Moving Ranges as: R2 = range of 1st and 2nd observations, R3 = range of 2nd and 3rd observations, R4 = range of 3rd and 4th observations, etc. with the "average" moving range or "MR-bar" being the average of these ranges with the "sample size" for each of these ranges being n = 2 since each is based on consecutive observations ... this should provide an estimated standard deviation (needed for the "I" chart) of σ = (MR-bar)/d2 where the value of d2 is based on, as just stated, m = 2. Similarly, the UCL and LCL for the MR chart will be: UCL = D4(MR-bar) and LCL = D3(MR-bar) but, since D3 = 0 when n = 0 (or, more accurately, is "not applicable") there will be no LCL for the MR chart, just a UCL. Copyright 2011 John Wiley & Sons, Inc. 1 Statistical Quality Control Copyright 2011 John Wiley & Sons, Inc. 2 • Quality is when a product delivers what is stipulated for in its specifications • Crosby: “quality is conformance to requirements” • Feigenbaum: “quality is a customer determination” • Garvin: five dimensions of quality Quality Copyright 2011 John Wiley & Sons, Inc. 3 • Transcendent quality: “innate excellence” • Product quality: quality is measurable • User quality: quality is determined by the consumer • Manufacturing quality: quality is measured by the manufacturer's ability to target the product specifications with little variability • Value Quality: Has to do with the price and cost Garvin’s Five Dimensions of Quality Copyright 2011 John Wiley & Sons, Inc. 4 Quality Control • Quality control - the collection of strategies, techniques, and actions taken by an organization to assure themselves of a quality product. • After-process quality control - involves inspecting the attributes of a finished product to determine whether the product is acceptable • reporting of the number of defects per time period • screening defective products from consumers • In-process quality control - techniques measure product attributes at various intervals throughout the manufacturing process in an effort to pinpoint problem areas. Copyright 2011 John Wiley & Sons, Inc. 5 Total Quality Management • W. Edwards Deming - the “father of the quality movement” said that the achievement of quality begins with top managers’ commitment and extends all the way to suppliers and consumers. • He believed that quality control is a long-term total company effort that he entitled “total quality management (TQM)”. • Deming presented a cause-and-effect explanation of the impact of TQM on a company, known as the Deming chain reaction. • The chain reaction begins with improving quality, which decreases costs and improves productivity: • Productivity = Copyright 2011 John Wiley & Sons, Inc. 6 Deming's 14 Points to Improved TQM 1. Create constancy of purpose for improvement of product and service. 2. Adopt the new philosophy. 3. Cease dependence on mass inspection. 4. End the practice of awarding business on price tag alone. 5. Improve constantly and forever the system of production and service. 6. Institute training. 7. Institute leadership. Copyright 2011 John Wiley & Sons, Inc. 7 Deming's 14 Points to Improved TQM 8. Drive out fear. 9. Break down barriers between staff areas. 10. Eliminate slogans. 11. Eliminate numerical quotas. 12. Remove barriers to pride of workmanship. 13. Institute a vigorous program of education and retraining. 14. Take action to accomplish the transformation. Copyright 2011 John Wiley & Sons, Inc. 8 Six Sigma • Six sigma - total quality approach that measures the capacity of a process to perform defect free work. • Requires that there be no more than 3.4 incorrectly filled prescriptions of 3.4 unsatisfactory landings per million, with a goal of approaching zero. • Forces companies that adopt it to work much harder and more quickly to discover and reduce sources of variation in processes. • May be required to attain world-class status and be a top competitor in the international market. Copyright 2011 John Wiley & Sons, Inc. 9 Six Sigma • Contains a formalized problem-solving approach called the DMAIC process (Define, Measure, Analyze, Improve, and Control). • Strong focus on the customer, both internal and external, that is often referred to as Critical to Quality (CTQ). • Most members of an organization are trained in the methodology. • Companies using Six Sigma discovered that so many problems existed that required a complete redesign. • History shows that most companies can only achieve about a 5.0 sigma status. Copyright 2011 John Wiley & Sons, Inc. 10 Lean Manufacturing • A quality-management philosophy that focuses on the reduction of wastes and the elimination of unnecessary steps in an operation or process. • The Toyota Production System is generally credited with developing the notion of lean manufacturing. • Focuses on 7 wastes: 1. Overproduction 2. Waiting time 3. Transportation 4. Processing 5. Inventory 6. Motion 7. Scrap Copyright 2011 John Wiley & Sons, Inc. 11 Important Quality Concepts • Benchmarking - examine and emulate the best practices and techniques used in the industry. • a positive, proactive process to make changes that will effect superior performance. • Just-In-Time Inventory Systems - necessary parts for production arrive “just in time”. • reduced holding costs, personnel, and space needed for inventory. • no extra raw materials or inventory of parts for production are stored. • Reengineering - complete redesign of the core business process in a company. Copyright 2011 John Wiley & Sons, Inc. 12 Other Quality Control Concepts • Failure Mode and Effects Analysis: • A systematic way for identifying the effects of a potential product or process failure and includes methodology for eliminating or reducing the chance of a failure occurring. • Used for analyzing potential reliability problems early in the development cycle. • Poka-Yoke: • “mistake proofing” • Uses devices, methods, or inspections in order to avoid machine error or human error. • Two main types: • Prevention-based • Detection-based Copyright 2011 John Wiley & Sons, Inc. 13 Other Quality Control Concepts • Team Building: • Occurs when a group of employees are organized to undertake management tasks and perform other functions such as organizing, developing, and overseeing projects. • More workers take over managerial responsibilities. • A quality circle is a small group of workers and their supervisor who meet regularly to consider quality issues. Copyright 2011 John Wiley & Sons, Inc. 14 Process Analysis A process is a series of actions, changes or functions that bring about a result – examined through flow charts and diagrams. The seven basic tools are as follows: 1. Flowchart or process map 2. Pareto chart 3. Cause-and-effect diagram (Ishikawa or fishbone chart) 4. Control chart 5. Check sheet or checklist 6. Histogram 7. Scatter chart or scatter diagram Copyright 2011 John Wiley & Sons, Inc. 15 Flowcharts A flowchart is a schematic representation of all the activities and interactions that occur in a process. Copyright 2011 John Wiley & Sons, Inc. 16 Flow Charts - schematic representation of all the activities and interactions that occur in a process. Copyright 2011 John Wiley & Sons, Inc. 17 Pareto Analysis • Pareto Analysis - quantitative tallying of the number and types of defects that occur with a product. • Pareto Chart - ranked vertical bar chart with most frequently occurring on the left. Copyright 2011 John Wiley & Sons, Inc. 18 Fishbone Fishbone Diagram - display of potential cause-and-effect relationships. Copyright 2011 John Wiley & Sons, Inc. 19 Check Sheets Check Sheets or Checklists – Display the frequency of outcomes for some quality-related event or activity under study. Copyright 2011 John Wiley & Sons, Inc. 20 Other Process Analysis • Histograms – Depicts a frequency distribution of quantitative data. • Scatter Chart or Scatter Diagram – for examining the relationship between two variables. Copyright 2011 John Wiley & Sons, Inc. 21 • Control chart – graphical method for evaluating whether a process is or is not in a “state of statistical control . • Types of control charts: • Control charts for measurement: x-bar and R charts • Control charts for attribute compliance: p and c charts • Elements of a control chart: • Centerline • Upper control limit (UCL) • Lower control limit (LCL) Control Charts Copyright 2011 John Wiley & Sons, Inc. 22 • Chart of sample means computed for a series of small random samples over a period of time. • The centerline is the average of the sample means, • The upper control limit (UCL) is 3 standard deviations of means above the centerline. • The lower control limit (LCL) is 3 standard deviations below the center line. Control Chart Copyright 2011 John Wiley & Sons, Inc. 23 Steps to Creating an Control Chart Monitor process location (center): 1. Decide on the quality to be measured. 2. Determine a sample size. 3. Gather 20 to 30 samples. 4. Compute the sample average for each sample. 5. Compute the sample range for each sample. 6. Determine the average sample mean for all samples. 7. Determine the average sample range (or sample standard deviation) for all samples. 8. Using the size of the samples, determine the value of A2 or A3. 9. Compute the UCL and the LCL Copyright 2011 John Wiley & Sons, Inc. 24 R Control Chart Monitor process variation: 1. Decide on the quality to be measured. 2. Determine a sample size. 3. Gather 20 to 30 samples. 4. Compute the sample range for each sample. 5. Determine the average sample mean for all samples. 6. Using the size of the samples, determine the values of D 3 and D 4 . 7. Compute the UCL and the LCL Copyright 2011 John Wiley & Sons, Inc. 25 R Chart Formulas Copyright 2011 John Wiley & Sons, Inc. 26 Control Chart: Formulas Copyright 2011 John Wiley & Sons, Inc. 27 A manufacturing facility produces bearings. The diameter specified for the bearings is 5 millimeters. Every 10 minutes, six bearings are sampled and their diameters are measured and recorded. Twenty of these samples of six bearings are gathered. Use the resulting data and construct an chart. Data for Demonstration Problem 18.1: Samples 1 - 10 Copyright 2011 John Wiley & Sons, Inc. 28 1 2 3 4 5 6 7 8 9 10 5.13 4.96 5.21 5.02 5.12 4.98 4.99 4.96 4.96 5.03 4.92 4.98 4.87 5.09 5.08 5.02 5.00 5.01 5.00 4.99 5.01 4.95 5.02 4.99 5.09 4.97 5.00 5.02 4.91 4.96 4.88 4.96 5.08 5.02 5.13 4.99 5.02 5.05 4.87 5.14 5.05 5.01 5.12 5.03 5.06 4.98 5.01 5.04 4.96 5.11 4.97 4.89 5.04 5.01 5.13 4.99 5.01 5.02 5.01 5.04 4.9933 4.9583 5.0567 5.0267 5.1017 4.9883 5.0050 5.0167 4.9517 5.0450 0.25 0.12 0.34 0.10 0.07 0.05 0.03 0.09 0.14 0.18 X R Data for Demonstration Problem 18.1: Samples 1 - 10 Copyright 2011 John Wiley & Sons, Inc. 29 Data for Demonstration Problem 18.1: Samples 11 - 20 11 12 13 14 15 16 17 18 19 20 4.91 4.97 5.09 4.96 4.99 5.01 5.05 4.96 4.90 5.04 4.93 4.91 4.96 4.99 4.97 5.04 4.97 4.93 4.85 5.03 5.04 5.02 5.05 4.82 5.01 5.09 5.04 4.97 5.02 4.97 5.00 4.93 5.12 5.03 4.98 5.07 5.03 5.01 5.01 4.99 4.90 4.95 5.06 5.00 4.96 5.12 5.09 4.98 4.88 5.05 4.82 4.96 5.01 4.96 5.02 5.13 5.01 4.92 4.86 5.06 4.9333 4.9567 5.0483 4.9600 4.9883 5.0767 5.0317 4.9617 4.9200 5.0233 0.22 0.11 0.16 0.21 0.06 0.12 0.12 0.09 0.17 0.09 X R Copyright 2011 John Wiley & Sons, Inc. 30 Demonstration Problem 18.1: Control Chart Computations Copyright 2011 John Wiley & Sons, Inc. 31 Sigma level: 3 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Bearing Diameter UCL = 5.0679 Average = 5.0022 LCL = 4.9364 Control Chart: Bearing Diameter Mean 5.10963 5.05590 5.00217 4.94844 4.89471 X Demonstration Problem 18.1: Control Chart Copyright 2011 John Wiley & Sons, Inc. 32 Output for R Control Chart Copyright 2011 John Wiley & Sons, Inc. 33 Construct an R chart for the 20 samples of data in Demonstration Problem 18.1 on bearings. Demonstration Problem 18.2: R Control Chart Copyright 2011 John Wiley & Sons, Inc. 34 Control Chart: Bearing Diameter Sigma level: 3 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Range .4 .3 .2 .1 0.0 Bearing Diameter UCL = .2725 Average = .1360 LCL = .0000 Demonstration Problem 18.2: R Control Chart Copyright 2011 John Wiley & Sons, Inc. 35 Monitor proportion in noncompliance: 1. Decide on the quality to be measured. 2. Determine a sample size. 3. Gather 20 to 30 samples. 4. Compute the sample proportion for each sample. 5. Determine the average sample proportion for all samples. 6. Compute the UCL and the LCL P Charts Copyright 2011 John Wiley & Sons, Inc. 36 P Chart Formulas Copyright 2011 John Wiley & Sons, Inc. 37 A company produces bond paper and, at regular intervals, samples of 50 sheets of paper are inspected. Suppose 20 random samples of 50 sheets of paper each are taken during a certain period of time, with the following numbers of sheets in noncompliance per sample. Construct a p chart from these data. Demonstration Problem 18.3: Twenty Samples of Bond Paper Copyright 2011 John Wiley & Sons, Inc. 38 Sample n Number Out of Compliance Sample n Number Out of Compliance 1 50 4 11 50 2 2 50 3 12 50 6 3 50 1 13 50 0 4 50 0 14 50 2 5 50 5 15 50 1 6 50 2 16 50 6 7 50 3 17 50 2 8 50 1 18 50 3 9 50 4 19 50 1 10 50 2 20 50 5 Demonstration Problem 18.3: Twenty Samples of Bond Paper Copyright 2011 John Wiley & Sons, Inc. 39 Sample n n non Sample n n non 1 50 4 0.08 11 50 2 0.04 2 50 3 0.06 12 50 6 0.12 3 50 1 0.02 13 50 0 0.00 4 50 0 0.00 14 50 2 0.04 5 50 5 0.10 15 50 1 0.02 6 50 2 0.04 16 50 6 0.12 7 50 3 0.06 17 50 2 0.04 8 50 1 0.02 18 50 3 0.06 9 50 4 0.08 19 50 1 0.02 10 50 2 0.04 20 50 5 0.10 pp Demonstration Problem 18.3: Preliminary Calculations Copyright 2011 John Wiley & Sons, Inc. 40 Demonstration Problem 18.3: Centerline, UCL, and LCL Computations Copyright 2011 John Wiley & Sons, Inc. 41 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0 5 10 15 20 Sample Number P = .053 UCL = .148 LCL = 0 p Demonstration Problem 18.3: P Control Chart Copyright 2011 John Wiley & Sons, Inc. 42 Demonstration Problem 18.3: MINITAB P Control Chart Copyright 2011 John Wiley & Sons, Inc. 43 Monitor number of nonconformances per item: 1. Decide on nonconformances to be evaluated. 2. Determine the number of items to be studied (at least 25). 3. Gather items. 4. Determine the value of c for each item by summing the number of nonconformances in the item. 5. Determine the average number of nonconformances per item. 6. Determine the UCL and the LCL. c Charts Copyright 2011 John Wiley & Sons, Inc. 44 c Chart Formulas Copyright 2011 John Wiley & Sons, Inc. 45 A manufacturer produces gauges to measure oil pressure. As part of the company’s statistical process control, 25 gauges are randomly selected and tested for non-conformances. The results are shown here. Use these data to construct a c chart that displays the non-conformances per item. Demonstration Problem 18.4: Number of Nonconformities in Oil Gauges Copyright 2011 John Wiley & Sons, Inc. 46 Item Number Number of Nonconformities Item Number Number of Nonconformities 1 2 14 2 2 0 15 1 3 3 16 4 4 1 17 0 5 2 18 2 6 5 19 3 7 3 20 2 8 2 21 1 9 0 22 3 10 0 23 2 11 4 24 0 12 3 25 3 13 2 Demonstration Problem 18.4: Number of Nonconformities in Oil Gauges Copyright 2011 John Wiley & Sons, Inc. 47 Demonstration Problem 18.4: c Chart Calculations Copyright 2011 John Wiley & Sons, Inc. 48 0 1 2 3 4 5 6 7 0 5 10 15 20 25 Item Number c UCL = 6.2 LCL = 0 c = 2.0 Demonstration Problem 18.4: c Chart Copyright 2011 John Wiley & Sons, Inc. 49 Demonstration Problem 18.4: MINITAB c Chart Copyright 2011 John Wiley & Sons, Inc. 50 Interpreting Control Charts • Points are above UCL and/or below LCL • Eight or more consecutive points fall above or below the centerline. Ten out of 11 points fall above or below the centerline. Twelve out of 14 points fall above or below the centerline. • A trend of 6 or more consecutive points (increasing or decreasing) is present • Two out of 3 consecutive values are in the outer one-third. • Four out 5 consecutive values are in the outer two-thirds. • The centerline shifts from chart to chart. Sheet1 SIC Code No. Emp. No. Prod. Wkrs. Value Added by Mfg. Cost of Materials Value of Indus. Shipmnts New Cap. Exp. End Yr. Inven. Indus. Grp. 201 433 370 23518 78713 4 1833 3630 1 202 131 83 15724 42774 4 1056 3157 1 203 204 169 24506 27222 4 1405 8732 1 204 100 70 21667 37040 4 1912 3407 1 205 220 137 20712 12030 4 1006 1155 1 206 89 69 12640 13674 3 873 3613 1 207 26 18 4258 19130 3 487 1946 1 208 143 72 35210 33521 4 2011 7199 1 209 171 126 20548 19612 4 1135 3135 1 211 21 15 23442 5557 3 605 5506 2 212 3 2 287 163 1 2 42 2 213 2 2 1508 314 1 15 155 2 214 6 4 624 2622 1 27 554 2 221 52 47 2471 4219 2 292 929 3 222 74 63 4307 5357 2 454 1427 3 223 13 12 673 1061 1 20 325 3 224 17 13 817 707 1 84 267 3 225 169 147 8986 10421 3 534 2083 3 226 51 41 3145 4140 2 220 697 3 227 55 44 4076 7125 2 176 1446 3 228 84 76 3806 8994 2 423 1014 3 229 61 47 4276 5504 2 464 1291 3 231 27 22 1239 716 1 22 356 4 232 200 178 9423 8926 3 200 2314 4 233 294 250 11045 11121 3 189 2727 4 234 38 32 1916 2283 1 29 682 4 235 17 14 599 364 1 21 197 4 236 34 28 2063 1813 1 20 450 4 237 1 1 34 71 1 2 17 4 238 31 25 1445 1321 1 16 526 4 239 224 179 10603 12376 3 465 2747 4 241 83 68 5775 9661 3 539 578 5 242 172 147 10404 19285 4 1071 3979 5 243 257 209 13274 18632 4 711 3329 5 244 51 43 1909 2170 1 88 355 5 245 82 68 4606 7290 2 182 580 5 249 94 78 5518 8135 2 715 1604 5 251 273 233 12464 12980 3 481 3535 6 252 70 53 5447 4011 2 358 829 6 253 37 29 2290 5101 2 128 447 6 254 81 61 4182 3755 2 177 956 6 259 54 39 2818 2694 2 109 718 6 261 15 11 2201 3279 2 698 725 7 262 116 90 18848 20596 4 3143 4257 7 263 55 42 9655 10604 3 2360 1502 7 265 212 163 15668 24634 4 1352 3976 7 267 232 182 25918 28963 4 1750 5427 7 271 403 136 30692 8483 4 1277 894 8 272 121 16 17982 6940 3 311 1216 8 273 136 57 17857 8863 3 618 3736 8 274 69 25 9699 2823 2 144 874 8 275 604 437 38407 29572 4 2959 4300 8 276 41 28 3878 3811 2 198 688 8 277 21 12 3989 1047 2 66 577 8 278 65 50 4388 2055 2 130 504 8 279 55 39 4055 1098 2 210 236 8 281 80 45 16567 11298 3 2002 2644 9 282 115 79 25025 34596 4 3731 6192 9 283 213 106 59813 27187 4 4301 11533 9 284 126 75 31801 19932 4 1304 4535 9 285 51 28 8497 9849 3 404 2178 9 287 37 24 12277 11130 3 1025 2354 9 289 76 45 11547 13085 3 1006 2749 9 291 67 43 26006 132880 4 5197 10718 10 295 25 18 3464 6182 2 251 658 10 299 14 8 2187 4446 2 124 670 10 301 65 54 7079 7091 3 579 1067 11 302 8 7 442 496 1 9 175 11 305 61 46 4528 3805 2 341 1057 11 306 122 95 7275 7195 3 435 1411 11 308 763 598 55621 57264 4 5658 11874 11 311 15 12 1313 1865 1 52 404 12 313 3 2 162 163 1 1 35 12 314 37 31 1907 1682 1 35 716 12 315 2 2 53 85 1 12 62 12 316 6 4 747 395 1 18 199 12 317 8 7 328 255 1 6 75 12 319 7 6 233 177 1 4 40 12 321 12 9 1717 943 1 248 282 13 322 60 51 6532 3527 2 853 1505 13 323 64 50 4850 4254 2 493 883 13 324 17 13 3509 2282 2 495 828 13 325 31 25 2176 1387 1 201 700 13 326 45 36 2696 1183 1 154 600 13 327 205 152 15739 17010 4 1200 1966 13 328 17 13 999 565 1 50 263 13 329 72 53 7838 5432 2 464 1652 13 331 221 174 29180 45696 4 3433 12198 14 332 128 106 9061 6913 3 651 1543 14 333 35 26 4200 11184 3 635 1834 14 334 15 11 1410 5735 2 90 694 14 335 162 123 16670 31892 4 1761 6377 14 336 94 79 5856 4696 2 459 938 14 339 32 23 3164 2790 2 271 800 14 341 33 27 3999 9364 2 526 1453 15 342 140 107 11750 8720 3 620 3124 15 343 45 32 4412 3527 2 178 1121 15 344 432 315 27974 31527 4 1139 7204 15 345 104 81 6936 4909 2 421 1768 15 346 259 211 19880 21531 4 1908 3997 15 347 129 99 7793 6232 3 724 1181 15 348 40 24 3528 1689 2 85 1077 15 349 300 219 21718 19273 4 1273 6460 15 351 79 55 10513 12954 3 678 3679 16 352 94 70 9545 11858 3 414 3339 16 353 205 133 18178 23474 4 889 7344 16 354 295 211 22673 14343 4 1485 6730 16 355 192 110 19221 16515 4 1334 6823 16 356 265 172 23110 18543 4 1260 7898 16 357 259 96 41135 60857 4 2917 10277 16 358 201 147 17521 21819 4 907 4857 16 359 392 293 25322 13897 4 1568 4964 16 361 74 51 6700 5523 2 308 1495 17 362 171 120 14278 12657 3 784 3887 17 363 108 87 9466 12578 3 721 2299 17 364 157 117 13428 11065 3 671 3076 17 365 49 37 3459 7621 2 485 1070 17 366 258 120 38705 29591 4 2268 9467 17 369 151 106 13920 13398 3 1286 3514 17 373 141 108 7903 7760 3 351 2165 18 374 31 23 2590 4363 2 97 1233 18 375 18 14 1435 1674 1 131 412 18 376 81 29 9986 8120 3 490 4770 18 379 47 35 3564 5476 2 142 1102 18 381 186 68 21071 8760 4 1223 6183 19 382 272 141 29028 18028 4 1466 7681 19 384 268 157 31051 16787 4 1648 7761 19 385 27 17 2390 1020 1 197 426 19 386 61 36 14032 8114 3 724 2290 19 387 6 4 415 382 1 17 177 19 391 43 30 2761 3646 2 119 1451 20 393 13 10 685 506 1 15 328 20 394 103 76 8327 6604 3 396 2608 20 395 35 26 2643 1789 1 197 799 20 396 24 19 1406 997 1 51 415 20 399 179 123 11199 8530 3 595 2861 20 Sheet2 Sheet3