TLO-ELO_Data_QAE_Training_Courses_RFQ.pdf

ATTACHMENT B

Quality Assurance Engineer Skillset Training

Course Description

TLO

Number TLO Description

ELO

Number

ELO Description Blooms Level Sample Topics Deliverables Notes

BLACK BELT QUALITY

ENGINEERING STATISTICS TLO 1

Graphically and mathematically summarize either small samples or large amounts of data in order to reach sound conclusions. ELO 1.1

Differentiate continuous and discrete data

Learning content which:

- addresses these objectives (TLO / ELO) in a way that is interactive, constuctionally sound, and engaging;

- assesses the learners' retention, comprehension and/or application

This deliverable is at the highest level, but should be a template for lower--level deliverables

ELO 1.2

Understand measurement scales:

nominal, ordinal, interval, and ratio

ELO 1.3

Apply data collection methods:

check sheets, coding data, and automatic gauging

ELO 1.4

Apply effective sampling techniques: randomized, stratified, systematic, and representative

ELO 1.5

Evaluate measurement assurance and apply gauge R&R analysis

Student who can graphically and mathematically summarize data to analyze and evaluate it in order to reach sound conclusions

ELO 1.6

Analyze data using basic graphical tools: stem-and-leaf plots, box-and-whisker plots, run charts, scatter diagrams, frequency distributions, histograms, etc.

TLO 2

Summarize large amounts of data using descriptive statistics so that it can be discussed / presented / analyzed mathematically or statistically, for business process analysis ELO 2.1

Calculate measures of Central Tendency, including mean, median, mode

ELO 2.2

Analyze data using measures of dispersion, including range, standard deviation and variance

ELO 2.3

Analyze data by use of proportion, ratios, rates, percentages

ELO 2.4

Analyze data from samples to estimate population statistics

TLO 3

Evaluate sample data using probability distributions to predict process performance ELO 3.1

Differentiate descriptive and inferential statistics; sample statistics and population parameters

Students who are able to use predictive analysis through probability distributions to mitigate risk

ELO 3.2

Apply normal, binomial, Poisson, chi-square, student's t, and F distributions to effectively analyze data

ELO 3.3

Interpret data through hypergeometric, bivariate, exponential, lognormal, and Weibull distributions

ELO 3.4

Evaluate distribution assumptions on data

Normal probability plots Skewness and Kurtosis Chi-square goodness-of-fit tests

ELO 3.5

Utilize Central limit theorem and sampling distribution of the mean

TLO 4

Analyze data from small sample groups to accurately estimate population characteristics ELO 4.1

Analyze Statistical significance issues

Statistical vs. practical significance Interpreting p-values Type I and Type II (alpha and beta) errors

Students who are able to estimate population characteristics through statistical analysis of small sample sizes

ELO 4.2 Estimate points and intervals

Confidence intervals for means and proportions Prediction intervals Tolerance intervals

ELO 4.3

Apply hypothesis tests for population means, proportions, and variances

ELO 4.4

Estimate sample sizes for confidence intervals and hypothesis tests

ELO 4.5 Apply Paired-comparison tests

ELO 4.6 Apply Contingency tables 3

ELO 4.7 Apply nonparametric tests

Mood’s median Levene’s test Kruskal-Wallis Mann-Whitney

ELO 4.8

Analyze variation through the application of Analysis of Variance

(ANOVA)

TLO 5

Evaluate sample data to determine if process interventions are truly effective or to compare various system options before making final decisions. ELO 5.1

Apply multi-vari charts:

Distinguishing between positional, cyclical, and temporal variation

TLO 6

Evaluate a process' ability to meet spcific quality requirements or organizational objectives using process capability indices ELO 6.1 Calculate Cp, Cpk, Cr, Pp, Ppk

ELO 6.2 Calculate PPM / DPMO 4

ELO 6.3

Calculate sigma levels; percent defective; percent good

TLO 7

Analyze stability of processes and effectiveness of improvement efforts through statistical process control ELO 7.1

Analyze continuous data using variables control charts

Individual and Moving Range (I-MR) Chart Average and Range (X-Bar & R) Chart

Analyze discrete data using attributes control charts np Chart p Chart c Chart u Chart

CONTINUAL

IMPROVEMENT

ASSESSMENT TLO 1

Plan analysis of continual improvement activities ELO 1.1

Explain continual improvement to others.

ELO 1.2

Identify continual improvement actions.

ELO 1.3

Plan continual improvement assessments.

TLO 2

Analyze continual improvement data ELO 2.1

Collect continual improvement audit evidence.

ELO 2.2

Contrast necessary, value added, and superficial types of improvement.

ELO 2.3

Distinguish between containment, corrective, preventive, and innovative improvement actions.

ELO 2.4

Appraise processes using process performance indicators.

ELO 2.5

Recognize process inefficiencies, risks, and opportunities.

ELO 2.6

Compare data analysis results to scorecard objectives, reporting leading indicators.

ELO 2.7

Calculate the effect of findings on wealth (F/P matrix).

TLO 3

Manage a continual improvement process ELO 3.1

Understand management COR interests.

ELO 3.2

Assess gaps in the organization management system.

ELO 3.3

Conduct self-assessment perception surveys and analyze results.

ELO 3.4

Implement a continual improvement assessment (CIA) program.

INTRODUCTION TO SIX

SIGMA TLO 1

Interpret the benefits and implications of a Six Sigma program, and relate Six Sigma concepts to the overall business mission and objectives

ELO 1.1

Recognize Lean Six Sigma and why higher standards are required for higher performance

Introduction to Lean Six Sigma Higher Standards for Higher Performances

Learning content which addresses these objectives (TLO / ELO) in a way that is interactive, constuctionally sound, engaging, and assesses the learners' retention and comprehension

The current ASQ INTRODUCTION TO SIX SIGMA course is 4-hours Per Andy Miskovich of the CPI office (who teaches agency LSS courses):

- This is a robust "White Belt" level course.

- CPI/skillsoft provided training for the agency starts at "Yellow Belt" which is

ELO 1.2

Identify the lean Six Sigma Framework

Lean Six Sigma Framework

TLO 2

Recognize your organization as a collection of processes, with inputs that determine the outputs ELO 2.1

Explain input determines output

Input Determines Output

ELO 2.2

Explain how the concept of input determines output relates to organizational inputs and outputs

TLO 3

Describe how to use the concept of a Sigma Level to evaluate the capability of a process or organization ELO 3.1

Express the concept of Sigma Level

The Sigma Level

ELO 3.2

Calculate the Six Sigma Level using the appropriate toolset

Calculating the Sigma Level – Toolset

TLO 4

Recognize the five-step D-M-A-I- C model used to improve processes and the organizational factors that are necessary groundwork for a successful Six Sigma program

ELO 4.1

Explain DMAIC – The Lean Six Sigma Improvement Process

DMAIC – The Lean Six Sigma Improvement Process

ELO 4.2

Recognize critical success factors that are necessary groundwork for a successful six sigma program

Critical Success Factors

TLO 5

Describe how to integrate a Six Sigma effort with other process improvement initiatives, including Lean Enterprise (Lean Manufacturing)

ELO 5.1

Appraise how to integrate six sigma efforts with process improvement initiatives

Organizing for Success

ELO 5.2

Review Six Sigma program success stories

Success Stories Working Relationships

DESIGN OF EXPERIMENTS TLO 1

Explain how to use designed experiments to achieve breakthrough improvements in process efficiency and quality

ELO 1.1

Explain Design of Experiments

(DOE).

and describe its purpose, importance, and benefits

Define Design of Experiments (DOE) Role of DOE in global marketplace Reducing process variation Benefits of DOE

Learning content which addresses these objectives (TLO / ELO) in a way that is interactive, constuctionally sound, engaging, and assesses the learners' retention and comprehension

ELO 1.2

Explain key terms associated with

DOE

DOE vocabulary and key terms including:

Response Variable Factor/Level/Trials Interaction Effect Full Factorial Fractional Factorial

TLO 2

Discover Design of Experiments (DOE) methods that guide you in the optimal selection of inputs for experiments, and in the analysis of results for processes that have measurable inputs and outputs.

ELO 2.1

Explain how to conduct a well-designed statistical experiment

Introduction to DOE execution and experimentation strategy

ELO 2.2

Summarize the five phases used for applying DOE:

•Planning •Design •Run •Analysis •Action

DOE Phases:

Plan Design Run Analysis Act

ELO 2.3

Identify the steps for each phase as applied by means of DOE to a sample experiment

Consider Design Constraints & Possible Interactions Design Phase & Matrix application:

Replication & Randomization Run Phase Plus and Minus Run the Experiment The Results

ELO 2.4

Illustrate two types of simple comparative experiments - the completely randomized design and the randomized block design

3 Simple Comparative Experiments:

Completely Randomized Design Randomized Block Design

ELO 2.5

Describe a full factorial experiment

Full Factorial Experiments defined including:

Defining the levels Design Matrix Levels of factor Treatment Combinations

ELO 2.6

Show how to calculate the main and interaction effects of a full factorial experiment

Main and Interaction Effects of full factorial experiment:

Main Effect defined Main Effects plot Interaction Effect defined Calculations

TLO 3

Realize that process changes made as a result of statistically designed experiments typically result in more efficient processes and that’s what DOE is all about

ELO 3.1

Demonstrate how to analyze the results of a full factorial design

Full Factorial Design results analysis using:

ANOVA table Pareto chart of the Effects

ELO 3.2

Explain the role of replication

Replication analyzed including:

Analysis with Replicates Pareto of Effects with Replication Results with Replicates Main Effects Plot for Replicated Experiment Residuals

ELO 3.3

Describe the threats to statistical validity of a designed experiment

DOE threats to statistical validity including:

Functional Relationship Explanation Factorial Designs at 2 Levels Two Cubed Full Factorial Design Matrix Results Reducing the Model Residual Plots

MISTAKE PROOFING TLO 1

Explain why 99.9% is not good enough, why mistakes must be eliminated for good, and the basics of mistake proofing

ELO 1.1

Summarize what Mistake-Proofing is

What is Mistake-Proofing Rewriting Murphy’s Laws

Learning content which addresses these objectives (TLO / ELO) in a way that is interactive, constuctionally sound, engaging, and assesses the learners' retention and comprehension

ELO 1.2

Identify the source of mistakes

Source of mistakes

ELO 1.3

Review the mindset necessary for mistake-proofing 2

Mindset necessary for mistake-proofing

ELO 1.4

Recognize Mistake-Proofing in Everyday Life by analyzing common examples of Mistake- Proofing and using these examples to trigger ideas at work

Mistake-Proofing in everyday life

ELO 1.5

Analyze common expamles of Mistake-Proofing

Mistake-Proofing examples

ELO 1.6

Use common examples of Mistake- Proofing to trigger ideas at work 3

ELO 1.7

Explain why errors are made and the importance of obtaining the root causes of the errors

Root causes of errors

ELO 1.8

Compare inspection vs. mistake proofing

Inspection vs. Mistake-Proofing

ELO 1.9

Summarize how Mistake-Proofing works including the language of Mistake-Proofing

Mistake-Proofing fundamentals

ELO 1.10

Explain the role of teams in Mistake-Proofing efforts

Team effect

TLO 2

Identify different types of mistake-proofing solutions ELO 2.1

Explain the 8 forms of Mistake- Proofing

8 forms of mistake-proofing solutions

ELO 2.2

Summarize the guidelines for selecting a mistake-proofing approach

2 Guidelines for selecting a mistake-proofing approach

TLO 3

Recognize how to apply different mistake-proofing techniques

ELO 3.1

Explain how to apply error-proofing processes using Forced Control, Shutdown, Warning or Sensory Alert techniques.

Error proofing processes using:

Forced Control Devices Shutdown Devices Warning Devices Sensory Alert Devices

ELO 3.2

Summarize how to use ten continuous improvement tools to complement mistake-proofing efforts

Ten continuous improvement tools to complement mistake-proofing efforts.

TLO 4

Identify the best way to mistake-proof a given situation

ELO 4.1

Summarize the assessment techniques for determining the practicality, feasibility, and cost-effectiveness of mistake-proofing solutions

Practical, feasible, and cost effective solutions

ELO 4.2

Use the practicality, feasibility, and cost-effectiveness of mistake-proofing solutions to determine how robust the solutions are

Robustness of solutions

TLO 5

Summarize how to Integrate mistake-proofing as part of an overall Quality Improvement Process ELO 5.1

Recognize the tips for keeping your mistake-proofing solution from being ignored or disabled

2 Keep solutions from being overridden or ignored

ELO 5.2

Explain how to incorporate mistake proofing into the quality improvement process

2 Incorporate Mistake-Proofing into quality process

PROCESS CAPABILITY

ANALYSIS TLO 1

Describe how well a process is able to meet customer requirements by measure of process capability

ELO 1.1

Explain Process Capability, Control Chart, Specification Limits, Characteristics of Data, Population and Sample terminology, Symbols for Center and Spread, and Samples Done Properly

Introduction to Process Capability and Terminology

Learning content which addresses these objectives (TLO / ELO) in a way that is interactive, constuctionally sound, engaging, and assesses the learners' retention and comprehension

ELO 1.2

Explain Natural Tolerance as it relates to Process Capability

Natural Tolerance

ELO 1.3

Explain continuous data vs.

discrete data

Two types of data: continuous and discrete

ELO 1.4

Identify how sample measurements are used to estimate population values

Sample Data vs. Population Data

ELO 1.5

Compute which Control Chart type is most appropriate for monitoring a particular process parameter

Control Chart usage

ELO 1.6

Compute Cp, Cpk, Pp, and Ppk capability index values for processes using continuous data

3 Compute and interpret Process Capability Index values

ELO 1.7

Interpret Cp, Cpk, Pp and Ppk capability index values and relate them to a defect level

ELO 1.8

Use Cp, Cpk, Pp and Ppk measurements to interpret the values and relate them to a defect level

3 Defect levels related to Process Capability index values

ELO 1.9

Evaluate relevant process information for a process using discrete data

Process Capability and discrete data

TLO 2

Identify when one process is more capable than another

ELO 2.1

Explain Histogram in Process Capability analysis and Relative Capability

Process Capability evaluation

ELO 2.2

Explain how to perform a capability or performance study to assess a process relative to the specifications

Process Capability Studies and results analysis

ELO 2.3

Distinguish capable from non-capable processes

TLO 3

Explain a very powerful operation metric called Rolled Throughput Yield ELO 3.1

Explain Sigma Levels

Rolled Throughput Yield and Sigma Levels

ELO 3.2

Define Rolled Throughput Yield

ELO 3.3

Calculate Rolled Throughput Yield

QUALITY FUNCTION

DEPLOYMENT TLO 1

Explain quality function deployment (QFD) and its history and benefits

ELO 1.1

Paraphrase Quality Function Deployment

Explanation of QFD

Learning content which addresses these objectives (TLO / ELO) in a way that is interactive, constuctionally sound, engaging, and assesses the learners' retention and comprehension

ELO 1.2

Identify how QFD creates a system for product development 2

QFD system for product development ELO 1.3 Summarize the history of QFD 2

ELO 1.4

Express the focus within each of the four phases of a QFD process 2 Focus of each of the four phases of a QFD process

ELO 1.5

Summarize the benefits of a QFD

QFD Benefits

ELO 1.6

Express at least 4 potential uses of a QFD

QFD uses

TLO 2

Describe pre-planning a QFD Project ELO 2.1

Describe the process for completing a QFD project

Pre-Plannin a QFD Project

ELO 2.2

Recognize the key questions needed to plan and complete a QFD project

Key questions for planning a QFD Project

ELO 2.3

Express the definition for the Voice of the Market

Define the Voice of the Market

ELO 2.4

Describe the components of a great QFD team

QFD Project team

ELO 2.5

Recognize the documentation needed for each phase of a QFD project

QFD Project planning - required documentation

TLO 3

Recognize the Vocie of the Customer and its tie-in to QFD project planning ELO 3.1

Describe the difference in the 3 levels of quality in the Kano Model

2 Review of "Voice of the Customer" core subject matter

ELO 3.2

Identify where customer information could be collected within your organization

ELO 3.3

Explain how to determine the number of customer to interview 2

ELO 3.4

Describe the different types of customers

ELO 3.5

Express how to create a customer selection matrix

ELO 3.6

Explain what the term “Gemba” means

TLO 4

Identify the difference between customer needs and features

ELO 4.1

Explain the two basic requirements for collecting the Voice of the Customer

Customer Needs vs. Features

ELO 4.2

Differentiate between a need and a solution or feature

ELO 4.3

Recognize what to do when a customer gives you a solution rather than a true customer need

ELO 4.4

Explain the term “level of abstraction”

Level of Abstraction and the "orange to orange concept"

ELO 4.5

Express how to turn a verbatim statement into information that can be measured

Need for statements that can be measured

TLO 5

Describe how to calculate a Phase 1 QFD (House of Quality) Matrix ELO 5.1

Recognize the difference between the what’s and the how’s 2 Understand a Phase 1 QFD matrix and its symbols

ELO 5.2

Identify the stages in calculating a QFD matrix

Calculate a Phase 1 QFD matrix

ELO 5.3

Describe the methodology behind the relationship matrix 2

Define Relationship matrix

ELO 5.4

Explain the meaning for the symbols used in a QFD matrix

ELO 5.5

Calculate a simple House of Quality (QFD) matrix

TLO 6

Describe the rooms in the "House of Quality" ELO 6.1

Explain the different rooms in the House of Quality

14 Rooms in the House of Quality

ELO 6.2

Describe the steps in creating a House of Quality

Steps to create a House of Quality

ELO 6.3 Construct a House of Quality 6

ELO 6.4

Recognize what to look for when evaluating a House of Quality 2

ELO 6.5

Evaluate an existing House of Quality and make recommendations

Evaluate a House of Quality

TLO 7

Describe Phase 2, the Product Design Phase, of a QFD process

ELO 7.1

Define the steps needed to create a product design matrix 2

Product Design Phase of a QFD

ELO 7.2

Construct a product design matrix

TLO 8

Describe Phase 3, the Process Planning Phase, of a QFD process

ELO 8.1

Explain the steps needed to create a process design matrix 2

Process Design Phase of a QFD

ELO 8.2

Review example of a process design matrix

TLO 9

Describe Phase 4, the Production Planning Phase, of a QFD process

ELO 9.1

Explain the rationale in creating a control matrix

Production Planning Phase of a QFD

ELO 9.2

Construct a process planning matrix

ELO 9.3

Evaluate production planning concepts

TLO 10

Explain TRIZ, the systematic approach for analyzing challenging problems where inventiveness is required ELO 10.1

Explain the theory behind TRIZ

TRIZ Principles - Concepts and application ELO 10.2 Discuss the history of TRIZ 2

ELO 10.3

Express the 39 most common problems/features of the technical contradiction matrix of

TRIZ

ELO 10.4

Express the 40 inventive principles of problem solving of TRIZ 2

ELO 10.5

Use TRIZ principles on the House of Quality technical contradiction matrix

TLO 11

Explain Product Concepting and its use in the QFD process

ELO 11.1

Explain the 4 parts to product concepting 2

Product Concepting in the QFD process

ELO 11.2

Describe where in the QFD process to do concepting

ELO 11.3

Describe the steps involved in concept definition, concept generation and concept evaluation

ELO 11.4

Express the definition for evaluation criteria in relation to the concept selection process

QUALITY TOOLS TLO 1

Review the basics of quality

ELO 1.1

Discuss quality

Introduction to Quality Basics

Learning content which addresses these objectives (TLO / ELO) in a way that is interactive, constuctionally sound, engaging, and assesses the learners' retention and comprehension

ELO 1.2

Explain why continuous improvement cannot happen without quality tools

ELO 1.3

Identify the stages of the Shewhart Model

ELO 1.4

Discuss each stage in the PDSA (plan-do-study-act) model 2

ELO 1.5

Express the basic components of organizational change

ELO 1.6

Identify the components of an effective team

TLO 2

List the quality tools and the importance of them

ELO 2.1

Discuss the importance of data in creating continuous improvement 2

Quality Tools Overview and Importance

ELO 2.2

Identify tools that generate data

ELO 2.3

Identify tools that both generate and analyze data

ELO 2.4

Identify tools that analyze data

TLO 3

Describe the Quality Tool - Brainstorming ELO 3.1

Discuss the reasons for brainstorming

Quality Tool - Brainstorming

ELO 3.2

Explain when to use brainstorming

ELO 3.3

Identify three distinct types of brainstorming

ELO 3.4

Illustrate the steps involved in the three different types of brainstorming

ELO 3.5

Explain the guidelines for conducting a brainstorming session

TLO 4

Describe the Quality Tool - Flow Charting ELO 4.1

Explain flow charting

Quality Tool - Flow Charting

ELO 4.2

Discuss when to use a flow chart

ELO 4.3

Summarize the different uses for flow charting

ELO 4.4

Identify the different symbols used in simple flow charting

ELO 4.5

Describe the guidelines for flow charting

TLO 5

Describe the Quality Tool - Affinity Diagrams ELO 5.1

Explain affinity diagrams

Quality Tool - Affinity Diagrams

ELO 5.2

Identify when to use an affinity diagram

ELO 5.3

Illustrate the steps for affinitizing information

ELO 5.4

Describe the critical guidelines for using an affinity diagram 2

TLO 6

Describe the Quality Tool - Cause and Effect Diagram ELO 6.1

Explain a cause and effect diagram

Quality Tool - Cause and Effect Diagram

ELO 6.2

Identify when to use a cause and effect diagram

ELO 6.3

Illustrate the steps in creating a cause and effect diagram

ELO 6.4

Describe the guidelines for using a cause and effect diagram 2

TLO 7

Describe the Quality Tool - Force Field Analysis ELO 7.1

Explain all the components in a force field analysis

Quality Tool - Force Field Analysis

ELO 7.2

Interpret when to utilize force field analysis

ELO 7.3

Identify the tools used in conjunction with a force field analysis

ELO 7.4

Illustrate the steps in conducting a force field analysis 2

TLO 8

Describe the Quality Tool - Tree Diagrams ELO 8.1

Explain the meaning for a tree diagram

Quality Tool - Tree Diagrams

ELO 8.2

Summarize when to use a tree diagram

ELO 8.3

Illustrate the steps needed to create a tree diagram

ELO 8.4

Recognize critical guidelines needed for creating a tree diagram

TLO 9

Describe the Quality Tool - Prioritization Matrices ELO 9.1

Summarize the definition of a prioritization matrix

Quality Tool - Prioritization Matrices

ELO 9.2

Identify when to use a prioritization matrix

ELO 9.3

Describe the meaning of a data set

ELO 9.4

Recognize how to construct a prioritization matrix

ELO 9.5

Illustrate the guidelines for using a prioritization matrix

TLO 10

Describe the Quality Tool - Dot Plots ELO 10.1

Explain the definition of a dot plot

Quality Tool - Dot Plots

ELO 10.2

Express when to use a dot plot

ELO 10.3

Illustrate the steps in creating a dot plot

ELO 10.4

Summarize the guidelines in creating a dot plot

TLO 11

Describe the Quality Tool - Pareto Charts ELO 11.1

Explain the definition of a Pareto diagram

Quality Tool - Pareto Charts

ELO 11.2

Express when to use a Pareto diagram

ELO 11.3

Illustrate the steps in creating a Pareto diagram

ELO 11.4

Summarize the guidelines for creating a Pareto diagram

TLO 12

Describe the Quality Tool - Scatter Diagrams ELO 12.1

Explain the definition for a scatter diagram

Quality Tool - Scatter Diagrams

ELO 12.2

Express when to use a scatter diagram

ELO 12.3

Differentiate between negative and positive correlations 4

ELO 12.4

Illustrate the steps for creating a scatter diagram

ELO 12.5

Summarize the guidelines for using a scatter diagram

TLO 13

Describe the Quality Tool - Nominal Group Techniques

ELO 13.1

Explain the meaning of the term nominal group technique 2

Quality Tool - Nominal Group Techniques

ELO 13.2

Express when to use nominal group techniques

ELO 13.3

Discuss the purpose of using nominal group techniques

ELO 13.4

Illustrate the order of steps in a nominal group exercise

ELO 13.5

Summarize the guidelines for conducting a successful team session using nominal group techniques

TLO 14

Apply each tool learned to a scenario and identify, analyze and solve a specific problem

ELO 14.1

Discover which tool applies in each scenario - 11 scenarios; 1 for each quality tool

Application of Quality Tools in actual scenarios

ELO 14.2

Analyze each scenario and determine which to to apply to approach a given problem - 11 scenarios; 1 for each quality tool

ELO 14.3

Summarize the soluion to a specific problem in each scenario which will incorporate the use of one of the 11 quality tools - 11 scenarios; 1 for each quality tool

REGRESSION ANALYSIS TLO 1

Review how to use a scatter plot to determine if two variables appear to be correlated and to what degree

ELO 1.1

Explain univariate vs. bivariate data

Unviariate vs. Bivariate data

Learning content which addresses these objectives (TLO / ELO) in a way that is interactive, constuctionally sound, engaging, and assesses the learners' retention and comprehension

ELO 1.2

Describe Correlation and Regression

Define Correlation and Regression

ELO 1.3 Describe Correlation Analysis 2 Correlation Analysis

ELO 1.4

Explain Correlation Coefficient

Correlation Coefficient

ELO 1.5

Explain the Coefficient of Determination

Coefficient of Determination

ELO 1.6

Describe the concept of "Strength of Correlation"

Strength of Correlation

ELO 1.7

Express the properties of R (the Coefficient of Correlation) 2

Coefficient of Correlation - R ELO 1.8 Create a scatter plot 6 Scatter Plot

ELO 1.9

Use a scatter plot to determine if two variables ar correlated and to what degree

Scatter Plot use

TLO 2

Explain how to calculate the correlation coefficient and the coefficient of determination

ELO 2.1

Calculate the Correlation Coefficient

Calculate Coefficient of Correlation

ELO 2.2

Calculate the Coefficient of Determination

Calcualte Coefficient of Determination

ELO 2.3 Review correlation examples 2 Correlation examples

TLO 3

Show how regression analysis can be used to predict the value of one variable from another variable by fitting a least squares regression line to the data and judging the validity of the model

ELO 3.1

Explain the Regression model

Regression Analysis reviewed

ELO 3.2

Describe the Least Squares method

Least Squares method

ELO 3.3

Summarize how the least squares method provides estimate values for "a" and "b" that will minimize the sum of the squared errors

Estimating Least Squares parameters

ELO 3.4

Interpret the Linear Regression model and the parameter estimates

Linear Regression model analysis

ELO 3.5 Review model assumptions 2

ELO 3.6 Review model verification 2

ELO 3.7

Compute a Simple Linear Regression

Compute Simple Linear Regression

ELO 3.8

Analyze the computed Simple Linear Regression including:

•Assumptions •Measures of s, R, R-squared, R-squared(adj) •Regression Diagnostics

TLO 4

Explain the purpose of multiple regression and how it differs from simple regression

ELO 4.1

Review the steps of a Simple Linear Regression

Simple Linear Regression Review

ELO 4.2

Explain the Multiple Regression model

Introduction to Multiple Regression model

ELO 4.3

Review the purpose and applications of the Multiple Regression model

ELO 4.4

Compare the Simple and Multiple Regression models

Compare Regression models

TLO 5

Describe the elements of the multiple linear regression model

ELO 5.1

Review the Multiple Regression Model 2

Disection of the Multiple Regression Model

ELO 5.2

Describe the dependent response variable

ELO 5.3

Describe the regression coefficients and the Y intercept

ELO 5.4

Describe the response and the predictors

TLO 6

identify assumptions about the data required for regression analysis to work ELO 6.1

Summarize ordinal data

Data requirements and assumptions for Multiple Linear Regression to provide valid results

ELO 6.2 Summarize nominal data 2 ELO 6.3 Explain quantitative variables 2 ELO 6.4 Explain dummy variables 2

ELO 6.5

Describe the assumption that a true linear relationship between the response and predictor variables must exist for regression to work

ELO 6.6

Explain the assumption of independence between the response and predictor variables

ELO 6.7

Explain the assumption of homoscedasticity

ELO 6.8

Explain the assumption of the errors in residuals must be independent and normally distributed, with a mean of zero

TLO 7

Use the “best subsets” method to determine the possible regression models ELO 7.1

Summarize the "best subsets" method used to identify all possible models

2 "Best subsets" method of model selection for Multiple Linear Regression

ELO 7.2

Explain multicollinearity and collinearity 2 Colinearity in reference to Multiple Linear

Regression

ELO 7.3

Explain how multicollinearity is observed using a correlation matrix

ELO 7.4

Explain how another way to visualize multicollinearity is to examine a matrix plot of two-variable correlations

ELO 7.5

Summarize how anoter measure of multicollinearity is the variance inflation factor

ELO

Express how multicollinearity is problematic because it can increase the variance of the regression coefficients, making them unstable and difficult to interpret

ELO 7.4

Use the "best subsets" method to identify subsets of the predictors based on maximum R squared criteria and several other factors

Use of "best subsets" method

ELO 7.5

Analyze an example of "best subsets" of Multiple Linear Regression in Minitab from setup of the problem to final results

Full example of "best subsets" via Minitab

TLO 8

Apply several techniques for selecting the best model

ELO 8.1

Explain that when there is not a clear choice between models parsimony of the model can be beneficial

2 Concept of Parsimony and its use in model selection

ELO 8.2

Summarize Standard Error of the Regression (S) and how it can be used as a measure of model fit and selection

2 Concept of Standard Error of the Regression (S) and its use in model selection

ELO 8.3

Explain how Mallows Cp, a statistic that is used as an aid when choosing between competing multiple regression models, compares the precision of the full model to the models with the best subsets of predictors

Concept of Mallows Cp and its use in model selection

ELO 8.4

Summarize why the selected model, the "best model" should be rerun and put throgh the model selection process again

Model selection methodology

ELO 8.5

Explain the case of "Unusual Observations"where the standardized residual has an absolute value greater than two

Be Aware of Unusual Observations

TLO 9

Discuss what can go wrong with multiple regression that may lead to incorrect conclusions

ELO 9.1

Explain when an important control variable is missed, the result of a Multiple Linear Regression can lead to a bias

Non-inclusion of important control variable

ELO 9.2

Explain that If the measurement bias is large enough, one can incorrectly conclude that a variable has no effect on the dependent variable when it really does, or vice versa

Bias - Analysis and Impact

ELO 9.3

Explain that excessive measurement error of the input creates an uncertainty in the estimated coefficient prediction and the model

Measurement Error - Analysis and Impact

ELO 9.4

Explain how ample size has a profound effect on the test of statistical significance

ELO 9.5

Explain that if the sample is too small, it may be difficult to detect an important factor

2 Sample Size for Valid Multiple Linear Regression

ELO 9.6

Explain that if the sample size is too large, the result may be statistically significant but not practically meaningful

TLO 10

Describe how to use information generated by a computer output from a multiple linear regression to write the equation of the line and perform predictions based upon the model

ELO 10.1

Interpret the Multiple Linear Regression model and the parameter estimates

Multiple Linear Regression model analysis ELO 10.2 Review model assumptions 2

Review model verification 2

ELO 10.3

Compute a Multiple Linear Regression

Compute Multiple Linear Regression

ELO 10.4

Analyze the computed Multiple Linear Regression results including:

•Assumptions •Measures of s, R, R-squared, R-squared(adj) •Regression Diagnostics

VOICE OF CUSTOMER TLO 1

Explain the Voice of the Customer (VOC) concept and the basics of how to gather customer data and use different methodologies to analyze customer input to gain an appreciation for the importance of understanding customer needs

ELO 1.1

Define the meaning for the VOC

VOC Defined

Learning content which addresses these objectives (TLO / ELO) in a way that is interactive, constuctionally sound, engaging, and assesses the learners' retention and comprehension

ELO 1.2

Describe the difference between basic, performance, and excitement quality as defined by the Kano Model

The Kano Model and Customer Satisfaction

ELO 1.3

Identify how and where customer information can be collected within an organization and outside the organization

Ways to Solicit Customer Input

ELO 1.4

Produce a customer selection matrix based on an organization's market

Types and Number of Customers to Contact

ELO 1.5

Prepare phone scripts and contact letters 3 Customer Contact Letters

Generic Interview Questions

ELO 1.6

Recognize the difference between a customer need and a product feature

Needs vs. Features

ELO 1.7

Define appropriate questioning techniques using the VOC process 2 Questioning Techniques

ELO 1.8

Identify benefits of one-on-one interviews vs. focus groups 2

Interviews vs. Focus Groups One-on-one Interviews

ELO 1.9

Describe how to conduct one-on-one interviews and focus groups 1

Roles in a Focus Group Conducting Focus Groups

ELO 1.10

Explain how to build subconscious rapport

Building Subconscious Rapport

ELO 1.11

Outline the steps involved in a total VOC process

Details of the VOC process

ELO 1.12

Discover key environmental aspects at the customers' location 3

Going to the Customer's Place - The "Gemba"

PRODUCT AND PROCESS

DESIGN TLO 1

Evalutate Product, Process and Service Design ELO 1.1

Classify product, process and service quality characteristics

Classification of quality characteristics and defects

Learning content which:

- prepares students to sit for the Product and Process Design module questions in the ASQ Certified Quality Engineer exam

- addresses these objectives (TLO / ELO) in a way that is interactive, constuctionally sound, and engaging;

- assesses the learners' retention, comprehension and/or application

These TLOs, ELOs and Sample Topics mirror those in module three of the QAE162 course because it is built upon that module. It is a 3-hour virtual instructor-led course, during which solution methods are demonstrated and students are able to ask question, that has been extremely helpful in closing a knowledge gap for DCMA employees and has contributed to our higher-than-industry pass rate in the CQE program.

ELO 1.2 Analyze design inputs and review

Translate design inputs into robust design using techniques, such as FMEA, QFD, Design for X (DFX) and Design for Six Sigma (DFSS) Common elements of the design review process

ELO 1.3

Interpret drawings and specifications

Interpret requirements in relation to product and process characteristics and technical drawings

ELO 1.4

Interpret results of evaluations and tests to verify and validate product, process and service design

Installation Qualification (IQ), Operational Qualification (OQ) and Process Qualification

(PQ)

ELO 1.5

Evaluate reliability and maintainability of processes and products

Improve reliability with Predictive and preventive maintenance tools and techniques Analyze indices, such as Mean Time to Failure (MTTF), Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), availability and failure rate Distinguishing between the elements of reliability models, such as exponential, Weibull and bathtub curve Interpret results of reliability, safety and hazard assessment tools, such as FMEA, FMECA and Fault Tree Analysis (FTA)

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