Applied Statistics for Process Improvement
Statistical analysis is a key to understanding processes
and driving process improvement. This course allows participants
to understand and use statistical tools utilized by process improvement
experts to drive enhancements in quality and productivity. Microsoft
ExcelTM is used extensively to aid participants in improving the
speed and efficiency of their analyses. Industrial process case
studies and examples are used extensively throughout the course.
Who should attend:
Individuals involved with analysis of process data underlying quality
and productivity improvement.
Prerequisite:
A knowledge of basic algebra and Microsoft Excel is highly recommended.
Computer analysis using Microsoft Excel will be emphasized.
CEU Credits: 3.2
Duration: 32 Hours (4-day course)
Course Content:
- Understanding a Single Process
- Estimating the Center and Spread
- Confidence Intervals for the Mean, Variance and Proportion
- Testing a Hypothesized Mean, Variance and Proportion
- Errors of Type I and II
- Sample Size Considerations
- Assessing Differences Between Two Gauging Points
- Assessing Differences Between Two Parallel
Processes
- Graphical Techniques
- Differences in Means
- Differences in Variation
- Differences in Proportion
- Assessing Differences Among More Than Two
Parallel Processes
- Differences Between Means
- Differences in Variation
- Differences in Proportion
- Relating Two Variables (Using an Input Variable to Predict an Output
Variable)
- Correlation
- Fitting a Line
- Residual Analysis
- Predicting the Output at a Given Level of the Input
- Confidence and Prediction Intervals
- Relating More Than Two Variables (Using More Than One Input Variable
to Predict an Output Variable)
- Building the Regression Model
- Residual Analysis
- Confidence and Prediction Intervals for Regression Models
Each participant will receive a comprehensive manual and a Certificate
of Completion at the close of the seminar.
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