Quality Management Suite (value bundle)
July 19th - December 13th, 2022
Online and self-paced
Complete three courses within the assigned timeframe and you will receive the Quality Management Suite badge and a Binghamton University-issued completion certificate will be available to successful participants to print.
- Introduction to Probability & Statistics
- Advanced Probability & Statistics
- Quality Management Fundamentals
In today's competitive business environment, customer satisfaction is key to a company's success. Quality management is vital to business success in that it ensures consistency in a company's products, services and processes. Quality management includes everything you do to assure you produce and deliver your company’s products and services to consistently meet your customers' expectations at an affordable cost. Also, quality products will make an important contribution to long-term revenue and profitability. This suite provides topics that cover the fundamentals of Quality Management along with the tools and techniques required to measure your company's ability to consistently, effectively and efficiently meet customer needs.
- Binghamton University and SUNY students: $270 (you save $5 per course)
- Binghamton University faculty/staff/alumni: $420 (you save $10 per course)
- Standard Fee: $690 (you save $20 per course)
If you are not able to complete all courses in the bundle within the course timeframe you can retake the individual course for a fee of $35 (students) and $50 (non-students).
You will have access to the course materials from July 19 – Dec. 13, 2022. Each course has pre-recorded learning modules, self-assessment quizzes (ungraded) and a final graded online exam or assignment.
If you successfully complete all four courses within the assigned timeframe, you will receive the Quality Management Suite badge and a Binghamton University-issued completion certificate. You have to pass each course with a grade of 70% or higher in order to receive the credentials.
Introduction to Probability and Statistics
This course teaches the foundational concepts of data analytics. Data science is a growing field of study and practice as data is quickly becoming the world most abundant and untapped resource. Social, mobile and the proliferation of interconnectivity via the internet has led to vast amounts of data; however, we all require the ability to transform these raw data into meaningful information to help make better and faster decisions. Introduction to Probability and Statistics provides the class participant with the means to convert several forms of data into usable information. The course consists of six hours of pre-recorded lectures, self-assessment quizzes and an online multiple-choice exam.
- Session 1 - Course Introduction, Course Outline, Fundamental of Problem Solving, Introduction to Statistics, Sampling Process, Introduction to Minitab
- Session 2 - Basic Statistics: Measures of Location, Measures of Variability, Data Visualization, Coefficient of Variation, Dot Plot, Histogram, Stem And Leaf, Box Plot
- Session 3 - Basic Statistics: Random Distribution, Variables Types, T test, Z test, Statistical Tables, Confidence Intervals for Mean, Confidence Interval for Proportions, Confidence Interval for Standard Deviation
- Session 4 - Advanced Statistics: Compare Means, Compare Variances, Compare Proportions, Rejection Region, Fail to Reject, Type 1 error and Type 2 error, Power and Sample Size, P-value
Advanced Probability and Statistics
Our world is becoming more and more data intensive with every passing day; so the need to translate data into useful and meaningful information is greater than ever before. The ability to collect and analyze a variety of data types using descriptive and inferential statistics is foundational to effectively interpreting your specific sets of data, as well as understanding the appropriate analytical techniques for the various types of data collected will determine the outcome of your analysis. This course will teach you important concepts, principles and practices so that you can be much more successful at gaining important insights from your data analysis. (Introduction to Probability and Statistics, or equivalent, is a recommended prerequisite for this course). The course consists of six hours of pre-recorded lectures, self-assessment quizzes and an online multiple-choice exam.
- Session 1 - Hypothesis test: type 1 error, type 2 error, ANOVA, MANOVA, Nonparametric Tests
- Session 2 - Correlation Analysis, Regression Analysis, Multiple Regression
- Session 3 - Logistic Regression Analysis, Root Cause Analysis
- Session 4 - Factorial Experiment, Fractional Factorial Experiment, Minitab
- Session 5 - Reliability Total Productive Maintenance, MANOVA with Minitab, Regression with Minitab, Correlation with Minitab
Quality Management Fundamentals
In today's competitive world, quality is a fundamental expectation of all customers. The quality of your organization's products and services is the foundation of your business success. Brand loyalty, market performance, and customer satisfaction all rely on building a strong reputation that begins with superior quality. Building a reputation for outstanding quality; however, does not happen by accident. It takes business leaders, supervisors, engineers and all employees to design, develop and deliver products and services that satisfy our customers' needs. This course teaches several proven and powerful quality management methods for ensuring that the products and services your business provides is meeting the performance expectations that your customers demand. The course consists of eight hours of pre-recorded lectures, self-assessment quizzes and an online multiple-choice exam. Topics include:
- Quality History and Juran's Quality Principles.
- Cost of Quality and the Cost of Poor Quality.
- The 7 "Old" and 7 "New" Total Quality Management (TQM) Tools.
- Metrology and Measurement System Analysis (MSA).
- Process Capability and Process Stability.
- Achieving 6-Sigma quality performance and beyond.
- Multivariate and Short Run SPC.
- Quality Management Systems following ISO and Malcolm Baldrige criteria.
Shuxia (Susan) Lu is a professor in the Systems Science & Industrial Engineering Department at Binghamton University.
- BS, Hebei University of Technology
- MS, Tianjin University
- PhD, Texas Tech University
- Statistical process control
- Information technology
- Computer integrated manufacturing
Mohammad T. Khasawneh is a distinguished professor and chair in Systems Science and Industrial Engineering Department at Binghamton University .
- Director, Watson Institute for Systems Excellence
- Director, Healthcare Systems Engineering Center
- Director, Human Factors and Ergonomics Laboratory
- Graduate Program Director, Executive Master of Science in Health Systems
- SUNY Chancellor's Award for Excellence in Teaching
- PhD, Industrial Engineering, Clemson University
CANCELLATIONS AND REFUNDS
Please note our cancellation and refund policy: All cancellations must be received in writing (email) before the course start date to receive a refund. No refunds for cancellations or non-attendance will be given after this date. All refunds will be assessed a 10% administrative fee. Submit your cancellation request to firstname.lastname@example.org.