MGMT 1350 Syllabus

Subject Code

MGMT

Course Number

1350

Course Title

Quality Assurance Tools

Prerequisites

Program admission

Corequisites

Terms Offered

Offered Spring

Credit Hours

(3-0-3)

Course Description

This course introduces the data collection, analysis, and statistical tools used in Six Sigma projects. Students will have opportunities to apply these tools and to interpret the results. The course will emphasize hypotheses testing in its relation to the overall improvement of processes. It provides a methodical approach to problem resolution and prevention.

Course Outcomes

Data Collection Tools

  • Demonstrate the use of Current State vs Mapping.
  • Utilize various charts, tables, and graphs for the presentation of data.
  • Interpret a graphical display of quantitative and qualitative data.
  • Calculate mean, median, and mode to determine central tendency.
  • Calculate rang and standard deviation using concepts and formulas.
  • Identify how a change in dispersion will affect the shape of a Histogram.
  • Calculate Z score using values for mean and standard deviation of a data set.
  • Utilize a Pareto chart calculate the COPQ.
  • Utilize Gauge R and R validate a measurement system.

Analysis Tools and Techniques

  • Construct a cause and effect diagram to determine root causes.
  • Develop a scatter diagram using two variables to determine correlation.
  • Determine the link between FEMA, process maps, and cause and effect diagrams.
  • Calculate process capability.
  • Develop Future state Value Stream Map after evaluating current state VSM.

Statistical Tools

  • Demonstrate the use of statistics in conducting a Six Sigma project.
  • Apply the basic rules of probability to find the probability of various events and determine if events are independent or mutually exclusive.
  • Compute the mean and variance of the discrete probability distribution.
  • Identify the characteristics of a normal curve and estimate the capability of a process.
  • Distinguish between the use of descriptive or inferential statistics.
  • Construct and interpret a confidence interval for a single population mean for both small and large sample sizes.
  • Utilize correct hypothesis testing to determine valid conclusions.
  • Compute a confidence interval for single population mean and interpret the meaning of the confidence interval.
  • Perform a hypothesis test for either of two population variances.
  • identify when the method of ANOVA should be applied.
  • Determine appropriate degrees of freedom and how to calculate the F values.
  • Interpret results and draw valid statistical conclusions.