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IENG 486 - Lecture 09

IENG 486 - Lecture 09. Examples of Hypothesis Tests: Anthropometric Data and Intro to the Seven Tools of Ishikawa. Assignment: . Preparation: Print Hypothesis Test Tables from Materials page Have this available in class …or exam! Reading: Chapter 5:

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IENG 486 - Lecture 09

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  1. IENG 486 - Lecture 09 Examples of Hypothesis Tests: Anthropometric Data and Intro to the Seven Tools of Ishikawa IENG 486: Statistical Quality & Process Control

  2. Assignment: • Preparation: • Print Hypothesis Test Tables from Materials page • Have this available in class …or exam! • Reading: • Chapter 5: • 5.1 through 5.2, and 5.4 - only these portions are on Exam I • HW 3: • CH 5: # 5, 26, 27 • Review for Exam I IENG 486: Statistical Quality & Process Control

  3. R-L Side, Equal Variance Dominant Hand Means Comparison: L = x1 = 129.4, S12 = 2788, n1 = 34 people R = x2 = 104.0, S22 = 1225, n2 = 20 people Sp = 47.1, v = 52 Two-Sided Test at  = .05 HA: There is a difference Test: Is | t0 | > t.025, 52? |1.91| > 2.021 - NO! Keep the Null Hypothesis: There is NOT a difference btwn L & R ! Grip Strength Data Results IENG 486: Statistical Quality & Process Control

  4. R-L Side, No Assumptions Dom. Hand Means Comparison: L = x1 = 129.4, S12 = 2788, n1 = 34 people R = x2 = 104.0, S22 = 1225, n2 = 20 people v = 51 Two-Sided Test at  = .05 HA: There is a difference Test: Is | t0 | > t.025, 51? |2.12| > 2.021 - YES! Reject the Null Hypothesis: There IS a difference btwn L & R! Why is this wimpy test significant when the other wasn’t? ANS: Check the equal variance assumption! Grip Strength Data Results IENG 486: Statistical Quality & Process Control

  5. Unknown 0 Variances Comparison: S12 = 2788 n1 = 34, v1 = 33 S22 = 1225 n2 = 20, v2 = 19 Two-Sided Test at  = .10 HA: There is a difference Test: Is F0 > F.05, 33, 19? 2.276 > 2.07 - YES! (if not, also checkF1– /2, 33, 19) Reject the Null Hypothesis: There IS a difference in variance! At  = .05, this test is just barely not significant (Should also have checked for Normality with Normal Prob. Plot) Grip Strength Data Results IENG 486: Statistical Quality & Process Control

  6. Statistical Quality Improvement • Goal: Control and Reduction of Variation • Causes of Variation: • Chance Causes / Common Causes • In Statistical Control • Natural variation / background noise • Assignable Causes / Special Causes • Out of Statistical Control • Things we can do something about - IF we act quickly! • Both can cause defects – because specifications are often set regardless of process capabilities! IENG 486: Statistical Quality & Process Control

  7. Ishikawa’s “Magnificent Seven” Tools • The Seven Tools are: • Histogram / Stem & Leaf Diagram • Cause & Effect (Fishbone) Diagram • Defect Concentration Diagram • Check Sheet • Scatter (Plot) Diagram • Pareto Chart • Control Chart - covered after exam! • The tools were not invented by Ishikawa, but were very successfully put into methodical use by him • The first six are used before starting to use the seventh • They are also reused when needed to find an assignable cause IENG 486: Statistical Quality & Process Control

  8. Ishikawa’s Tools: Histogram • A histogram is a bar chart that takes the shape of the distribution of the data. The process for creating a histogram depends on the purpose for making the histogram. • One purpose of a histogram is to see the shape of a distribution. To do this, we would like to have as much data as possible, and use a fine resolution. • A second purpose of a histogram is to observe the frequency with which a class of problems occurs. The resolution is controlled by the number of problem classes.– see Pareto Chart slide! IENG 486: Statistical Quality & Process Control

  9. Ishikawa’s Tools: Fishbone Diagram • Cause & Effect diagram constructed by brainstorming • Identified problem at the “head” • Connects potential causes along the spine • Sub-causes are listed along the major “bones” • Man • Material • Method • Machine • Environment IENG 486: Statistical Quality & Process Control

  10. Man Method Skill Level Low RPM Attention Level Travel Limits Dusty Environment Poor Conductor Temperature Humidity Poor Mixing Orifice Clogs Poor Vendor Worn Parts Machine Material Bad Paint Cause & Effect Diagram, Cont. • The purpose of the cause and effect diagram is to obtain as many potential influencers of a process, so that the problem solving can take a more directed approach. IENG 486: Statistical Quality & Process Control

  11. Ishikawa’s Tools: Defect Diagram • A defect concentration diagram graphically records the frequency of a defect with respect to product location. • Obtain a digital photo or multi-view part print showing all product faces. • Operator tallies the number and location of defects as they occur on the diagram. IENG 486: Statistical Quality & Process Control

  12. Title Header Info: Date, Time, Location, Operator, etc. Times of Occurrence (periodic) Types of Errors Raw Data recorded here Type of Error Statistics Time of Occurrence Statistics Overall Statistics Instructions, settings, comments, etc. Ishikawa’s Tools: Check Sheet • Check sheets are used to collect data (values or pieces of information) in a consistent manner. • List each of the known / possible problems • Record each occurrence including time-orientation. IENG 486: Statistical Quality & Process Control

  13. Y Y Y X X X Ishikawa’s Tools: Scatter Plot • A scatter plot shows the relationship between any two variables of interest: • Plot one variable along the X-axis and the other along the Y-axis • The presence of a relationship can be inferred or ruled out, but it cannot determine if a cause and effect relationship exists IENG 486: Statistical Quality & Process Control

  14. Ishikawa’s Tools: Pareto Chart • 80% of any problem is the result of 20% of the potential causes • Histogram categories are sorted by the magnitude of the bar • A line graph is overlaid, and depicts the cumulative proportion of defects • Quickly identifies where to focus efforts IENG 486: Statistical Quality & Process Control

  15. Statistical Quality Control and Improvement Improving Process Capability and Performance Continually Improve the System Characterize Stable Process Capability Head Off Shifts in Location, Spread Time Identify Special Causes - Bad (Remove) Identify Special Causes - Good (Incorporate) Reduce Variability Center the Process LSL 0 USL Use of Ishikawa’s Tools • Removing special causes of variation • Preparation for: • hypothesis tests • control charts • process improvement IENG 486: Statistical Quality & Process Control

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