1. Data Collection & Analysis ETI 6134 Dr. Karla Moore

2. Today’s lecture  Types of Data  Population and Sample  Data Collection  Data Analysis

3. Types of Data Variable:  Continuous data  Data values can be any real number  Measured data

4. Types of Data Attribute:  Discrete data  Data values can only be integers  Counted data or attribute data

5. Population & Sample Population Set of all items that possess a characteristic of interest Sample Subset of a population

6. Parameter & Statistic Parameter - is a characteristic of a population, describes a population Statistic - a characteristic of a sample, used to make inferences on the population parameters that are typically unknown, called an estimator

7. 7 Data Collection  The decision as to how much data to collect and analyze is based on the reports to be issued, the processes to be controlled, the records to be retained, and the nature of the quality improvement program

8. 8 Data Collection  Computers are well set for the collection of data  Faster data transmission, fewer errors, and lower collection costs  Sources of data  Identifiers are necessary for data analysis, report preparation, and record traceability

9. Instruments to Gather Data Surveys:  Immediate customer surveys  Customer follow-up surveys  Community surveys Personal customer contact Customer contact reports given to the contact employee

10. Instruments to Gather Data E-mail and websites Test marketing Quality guarantees Inspectors – mystery shoppers, auditors Use of 800 phone numbers or suggestion boxes

11. Instruments to Gather Data Focus groups – 3 to 12 individuals assembled to explore specific topics and questions Face-to-face interviews – Individual interviews of 30 to 60 minutes Satisfaction/complaint cards Dissatisfaction sources – complaints, claims, refunds, recalls, returns, repeat services, litigation, replacements, downgrades, warranty work

12. 12 Data Analysis  Store in the computer for retrieval at a future time, analyzed, reduced, and disseminated in the form of a report  The analysis, reduction, and reporting are programmed to occur automatically in the system

13. 13 Data Analysis  Analysis of data tools: Pareto Histogram Cause-and-Effect Diagram Check Sheet Scatter Diagrams Control Charts Stratification Software programs (Excel)

14. Data Analysis Tool: Pareto When to Use a Pareto Chart 1.When analyzing data about the frequency of problems or causes in a process 2.When there are many problems or causes and you want to focus on the most significant 3.When analyzing broad causes by looking at their specific components 4.When communicating with others about your data

15. Data Analysis Tool: Histogram When to Use a Histogram: 1.When the data are numerical. 2.When you want to see the shape of the data’s distribution, especially when determining whether the output of a process is distributed approximately normally. 3.When analyzing whether a process can meet the customer’s requirements. 4.When analyzing what the output from a supplier’s process looks like. 5.When seeing whether a process change has occurred from one time period to another. 6.When determining whether the outputs of two or more processes are different. 7.When you wish to communicate the distribution of data quickly and easily to others.

16. Data Analysis Tool: Cause-and-Effect Diagram People Hardware Methods Information Technology Broken printers Antiquated technology Poor Human Factors Lack of functionality Antiquated technology Bureaucratic culture No performance measures Lack of computer skills Lack of training Lack of empowered employees Process Inefficien-cies No performance-based rewards or incentives No standard procedures When to use a Cause-and-Effect Analysis: 1.When identifying possible causes for a problem 2.Especially when a team’s thinking tends to fall into ruts

17. Data Analysis Tool: Check Sheet When to Use a Check Sheet: 1.When data can be observed and collected repeatedly by the same person or at the same location. 2.When collecting data on the frequency or patterns of events, problems, defects, defect location, defect causes, etc. 3.When collecting data from a production process.

18. Data Analysis Tool: Scatter Diagram When to Use an Scatter Diagram: 1.When you have paired numerical data. 2.When your dependent variable may have multiple values for each value of your independent variable. 3.When trying to determine whether the two variables are related, such as: - When trying to identify potential root causes of problems. - After brainstorming causes and effects using a fishbone diagram, to determine objectively whether a particular cause and effect are related. -When determining whether two effects that appear to be related both occur with the same cause. - When testing for autocorrelation before constructing a control chart.

19. Data Analysis Tool: Scatter Diagram

20. 20 Data Analysis Tools: Control Charts When to Use a Control Chart: 1. When controlling ongoing processes by finding and correcting problems as they occur. 2. When predicting the expected range of outcomes from a process. 3. When determining whether a process is stable (in statistical control). 4. When analyzing patterns of process variation from special causes (non-routine events) or common causes (built into the process). 5. When determining whether your quality improvement project should aim to prevent specific problems or to make fundamental changes to the process

21. 21 Data Analysis Tools: Control Charts

22. 22 Data Analysis Tools: Stratification When to Use Stratification:  Before collecting data  When data come from several sources or conditions, such as shifts, days of the week, suppliers or population groups  When data analysis may require separating different sources or conditions

23. 23 Data Analysis Statistical analysis:  Statistical package  The quality engineer can specify a particular sequence of statistical calculation to use for a given set of conditions  Time is saved and the calculations are error- free

24. 24 Data Analysis Statistical analysis Benefits:  No more time-consuming doing manual calculations  One-time problem  Process control

25. 25 Material to Read About Data Collection & Analysis  http://www.asq.org/learn-about-quality/quality-tools.html http://www.asq.org/learn-about-quality/quality-tools.html Cause Analysis Tools Tips and tools for the first step to improvement: identifying the cause of a problem or situation. Evaluation and Decision-Making Tools Making informed decisions and choosing the best options with a simple, objective rating system, and determining the success of a project. Process Analysis Tools How to identify and eliminate unnecessary process steps to increase efficiency, reduce timelines and cut costs. Seven Basic Quality Tools These seven tools get to the heart of implementing quality principles. Data Collection and Analysis Tools How can you collect the data you need, and what should you do with them once they’re collected? Idea Creation Tools Ways to stimulate group creativity and organize the ideas that come from it. Project Planning and Implementing Tools How to track a project’s status and look for improvement opportunities. Seven New Management and Planning Tools Ways to promote innovation, communicate information and successfully plan major projectsCause Analysis Tools Evaluation and Decision-Making Tools Process Analysis Tools Seven Basic Quality Tools Data Collection and Analysis Tools Idea Creation Tools Project Planning and Implementing Tools Seven New Management and Planning Tools