O. Geoffrey Okogbaa, Ph.D., PE

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Presents a SunCam continuing education series on the topic of:

Industrial, Mechanical & Electrical Engineering

Contact author at:
okogbaa@aol.com
Professor of Industrial & management Systems Engineering
University of South Florida
Tampa, FL 33647

O. Geoffrey Okogbaa is Professor of Industrial & Management System Engineering at the University of South Florida (USF). He very recently completed a five-year tenure as the Vice-Chancellor/President, Federal University Wukari in Nigeria.

For a little more than 30 years, Mr. Okogbaa has been teaching graduate and undergraduate courses in Probability and Statistics for Engineers, Reliability Engineering, Process Control, Design of Experiments and related courses, first at the University of Cincinnati for about 6 years and then at the University of South Florida for the past 25 years. A Registered Professional Engineer in the State of Florida, He is a Fellow of the Institute of Industrial and Systems Engineers, A Life Member of the Society of Reliability Engineers, an Inactive member of ASQ, and an Elected Fellow of the Nigerian Academy of Engineering. He previously served as Faculty Intern and Interim Associate Dean of the Graduate School at the University of South Florida. He currently serves as a Member of the Board of Directors of RAMS, the IEEE Reliability and Maintainability Symposium, and the premier organization for Reliability and Maintainability worldwide. His research interests are in the areas of Macro and Nano-Reliability, Engineering Design, Process Control & Operational Strategies, and Safety Critical Systems. He has received funding exceeding $8.1 million in grants and contracts from NSF, NASA, NIOSH, State Agencies, and other organizations to support research, instructional, and community outreach projects these areas. He serves as a consultant to major corporations in these and related areas. Dr. Okogbaa was awarded a US patent as an Inventor.

Most of the materials presented in this course were adapted from his course notes and his practical as well as his consulting experiences over the past several decades.

Course Title Rating Hours Price

275-What Every Engineer Should Know About Engineering Probability and Statistics I

(4) 25 Reviews

5 $112.50

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

The concept of probabilistic design is quite pervasive across the engineering disciplines because of its implication on engineering design decisions. Typically, assumptions and simplification of engineering and other related natural processes are often idealistic and do not consider uncertainties inherent in those processes and phenomenon (be they mechanical, chemical, electrical, biological, etc). There is also the tendency to assume that the situation is either deterministic or qualitative or both. Under certain circumstances such assumptions may suffice. However, in the realm of engineering design, such assumptions and simplifications are not acceptable as uncertainties are unavoidable in almost all engineering analyses and design activities. Thus, any recommendations that are formulated without proper identification and assessment of the inherent risks and uncertainties would not only be invalid but would paint a wrong picture of the situation under consideration.

The purpose of this course therefore is to present the fundamental concepts of probability and statistics from the perspective of engineering practice. As part of the learning objective, the course would demonstrate:

The role of probability and statistics in engineering design decisions, and
The concepts of variability

Additionally, the student will be able to:

Develop an appreciation of the notion of events, the sample space and the real line.
Understand the notion of enumeration and counting techniques that are applicable in probability and statistics analyses.
Explore the meaning of density and mass functions with respect to their relationship to random variables.
Discover some of the common discrete and continuous distributions that are employed in describing engineering problem situations and scenarios

276-What Every Engineer Should Know About Engineering Probability and Statistics II

(4.5) 11 Reviews

5 $112.50

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

The concept of probabilistic design is quite pervasive across all engineering disciplines because of its implication on engineering design decisions. Quite often because of the complexity of the processes and the difficulty in explicating the inherent relationships, assumptions about engineering and other related natural processes are simplified and so do not consider uncertainties inherent in those processes and phenomenon. Safety factors and safety margins have often been employed to overcome the need for probabilistic designs. Under certain circumstances such assumptions may suffice. However, in the realm of engineering design, such assumptions and simplifications may not be acceptable as uncertainties are unavoidable in almost all engineering analyses and design activities. Therefore any recommendation developed without proper identification and assessment of the inherent risks and uncertainties would not only be invalid but would paint an unrealistic and unrepresentative picture and thus could jeopardize public safety.

This second course in the series focuses on an important area of engineering analyses and design, namely Statistical Inference. Statistical Inference is about how we analyze data and use the information to make decisions about a given engineering problem. The process of explicating the complexities of the data to yield information that would eventually be used to make design or mission decisions is known as inference or more appropriately Statistical Inference. If we examine the relationship between the population and the sample (as we did in the first course) we note that there is sort of a symbiotic (parent-population, offspring-sample) relationship between the two. Probability deals with the population with its parameters (parent values) while Statistical Inference deals with the sample and its statistic (values computed from the sample and used to estimate the population or universe parameters). The following areas would be covered in the course but not necessarily in the order shown:

The Point Estimates for the Mean and the variance.
The Central Limit Theorem (CLT) and its role in estimating parameters of a population.
Sampling distributions for means and variances with variance both known and unknown
Sampling distribution for two means & two variances with variance known and unknown
Point Estimator, Interval Estimators and Tests of Hypothesis
Error of estimation and the effect on sample size (n).
Type I and Type II errors and the effect on ample size n
Confidence Intervals for one means and one variances
Confidence Intervals for two means and two variances

Due to the nature of the materials, a significant number of numerical examples have been included to provide better insight into the materials presented. At the end, the engineer should feel well equipped to explore the important area of Statistical Inference and what it offers with regards to Engineering design decisions.

283-What Every Engineer Should Know About the Design and Analysis of Engineering Experiments I

(3.6) 8 Reviews

5 $112.50

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

Design of experiment is an activity that every Engineer should take very seriously. Engineers are called upon every day to make decisions regarding programs, processes and systems that have significant implications on the safety and well-being of society, be they chemical processes, the environment, infrastructure, machinery and equipment, and others. And while Engineers are known for sound and fact based judgment, those laudable qualities and characteristics may not be enough and may not serve them well in certain circumstances. This is especially true when they are called upon to make decisions regarding variables and factors whose underlying distributions are stochastic and thus have uncertain, albeit questionable, predictability. Handling these situations requires an understanding of the formal schemes and structures necessary to deal with variability, bias, and randomness.

This is the first of a two-course sequence in this subject area. As the prerequisite to the second course, it provides the Engineer with the rudimentary, but necessary, toolkit needed to plan, design and analyze basic engineering experiments and to make recommendations about design and operational decisions. It sets the stage for the second course, where more robust and higher level designs are explored, including Factorial designs, Fractional designs, Nested designs, Confounding schemes and Regression Analysis. The second course also addresses a fundamental problem of design, namely cost and resource utilization, and also the all important issue of missing values. While the two courses are not strictly about mathematics and statistics, they do utilize those subject matters to further elucidate how to plan, design, and analyze engineering experiments. Some of the areas covered in this course include:

The Role of Experiments in the Engineering Design Process
The Role of Statistics and Probability in Engineering Design
Purpose and Nature of Planned Experiments
Important Issues in Planned Experiments
The Effects of Changes in the Independent Variables
The Effect of Noise in An Experiment
Restrictions on Randomization
Single Factor Experiments including Model Analysis
Randomized Block Designs
Latin and Other Designs
Incomplete Block Designs

285-What Every Engineer Should Know About the Design and Analysis of Engineering Experiments II

(3) 3 Reviews

5 $112.50

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

Design of experiment is an activity that every Engineer should take very seriously. Engineers are called upon everyday to make decisions regarding programs, processes and systems that have significant implications on the safety and well being of society, be they chemical processes, the environment, infrastructure, machinery and equipment, and others. Engineers are known for sound and fact based judgment but while those qualities and characteristics are laudable, they may not be enough and may not serve them well. This is especially true when they are called upon to make decisions regarding variables and factors whose underlying distributions are stochastic and thus have uncertain and questionable predictability. Handling these situations requires an understanding of the formal schemes and structures necessary to deal with variability, bias, and randomness.

This second course, in the two-course sequence, focuses on some of the more practical issues that engineers encounter during the design and analysis of experiments. This course focuses on more robust and higher level designs such as Factorial designs, Confounding Schemes Fractional designs, Fixed and Random factors, Expected Mean Squares, Nested or Hierarchical designs, and Regression Analysis. The course also addresses, with realistic examples, some of the common problem in design of experiments, namely, missing data or missing values. It also provides practical justification for confounding, which arises due to the physical limitation as it relates to acquiring all the needed data. It addresses the issue of cost and resource utilization where fractional factorial designs are used because the cost to run full higher order designs is prohibitive. The course has a very practical bent and while there are theoretical foundations undergirding the material, the course itself utilizes basic arithmetic for computation and analysis. Some of the areas covered in the course include:

The Role of Experiments in the Engineering Design Process
Missing Values for Randomized Block and Latin Designs
Factorial Designs for 2^f and 3^f
Confounding Schemes for 2^f and 3^f
Fractional Factorial Designs for 2^f and 3^f
Modeling of Fixed and Random Effects and Expected Mean Square (EMS)
Nested/Hierarchical Designs
Regression Analysis

290-What Every Engineer Should Know About Reliability I

(2.2) 4 Reviews

4 $90.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

Reliability Engineering is concerned with the design, implementation, and prediction of the life profiles of a system or component using a disciplined analysis approach that has strong roots in statistics, mathematics and engineering. Given a system, subsystem or component, one of the major challenges of the discipline is to understand the inherent failure mechanisms that govern the system and the development of the appropriate analytical scheme to determine the system's life profiles. The problem becomes even more acute given the phenomenon of aging and related transient phenomenon as well as the practical realities of little or no data. Today, these challenges still persist especially as companies try to shorten the time to market in order to gain market share.

This first in a two-course sequence has examined some of the basic issues related to reliability such as:

Understand the various viewpoints of reliability, especially the engineering design viewpoint.
The use of nonparametric approach to estimate the reliability and hazard function functions
Understand the performance measures used to characterize reliability.
Appropriate reliability based intervention strategies that lead to optimally maintained system.
Availability, Maintainability and related Performability measures.

Under these broad themes, the topics to be covered include:

Reliability Models
Static Reliability
Reliability Improvement
Reparable Systems-Availability Models
System Redesign
Maintenance

The second sequence will focus on the all important area of dependency analysis, interference theory, data analysis and testing.

293-What Every Engineer Should Know About Reliability II

(3) 1 Review

4 $90.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

Reliability Engineering is concerned with the design, implementation, and prediction of the life profiles of a system or component using a disciplined analysis approach that has strong roots in statistics, mathematics and engineering. Given a system, subsystem or component, one of the major challenges of the discipline is to understand the inherent failure mechanisms that govern the system and the development of the appropriate analytical scheme to determine the system's life profiles. The problem becomes even more acute given the phenomenon of aging and related transient phenomenon, as well as the practical realities of little or no data. Today, these challenges still persist especially as companies try to shorten the time to market in order to gain market share.

This second in a two-course sequence has examined some more practical issues related testing and parameter estimation as well as some topology or configurations that are practical and realistic but have not received enough attention.

Some of the issues addressed include:

Understand the various viewpoints of reliability, especially the engineering design viewpoint.
The use of nonparametric approach to estimate the reliability and hazard function functions
Understand the performance measures used to characterize reliability.
Appropriate reliability based intervention strategies that lead to optimally maintained system.
Availability, Maintainability and related Performability measures.

Under these broad themes, the topics to be covered include:

Reliability Models
Static Reliability
Reliability Improvement
Reparable Systems-Availability Models
System Redesign
Maintenance

The second sequence will focus on the all important area of dependency analysis, interference theory, data analysis and testing.

297-What Every Engineer Should Know About Statistical Process/Quality Control I

(4.8) 11 Reviews

5 $112.50

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

The American National Standards Institute (ANSI) defines Quality Assurance (QA) as "All of those planned or systematic actions necessary to provide adequate confidence that an item will perform satisfactorily in service". A more operational definition of quality is the one that defines as: "Fitness for Use" This points to the inescapable fact that it is the customer rather the producer or manufacturer that determines what quality is or should be.

There is a tendency to think of quality as a recent development or phenomenon. However, the basic idea of making a quality product with high degree of uniformity has been around for as long as man has made a product the idea that statistics may be instrumental in assuring the quality of manufactured products goes as far back as the advent of modern production. The widespread use of statistical methods in problems of quality control is even more recent. Many problems encountered in the manufacturing or of product and services and the associated supply chains exhibit process characteristics and as such are amenable to statistical treatment or analysis. Statistical Process/Quality control refer to three special techniques:

Process/Qualitycontrol,
Acceptance control,
Parameter design and the establishment of tolerances.

The course places emphases on the significance of process control rather than inspection as a means of reducing rework and nonconformance. Many experts agree that inspection (especially human inspection) does not add value to quality and thus is a necessary but non-value adding activity.

This first in a two-course sequence will focus on Process/Quality control with emphasis on:

Historical review of Statistical Process/Quality Control
Cost of Quality (Cost of Poor Quality)
Quality Auditing Process
The difference between Quality of Design and Quality of conformance
Differences and similarities between SQC and SPC
Total Quality Management (TQM)
The Three Gurus of TQM
Lean Six Sigma
Off-line Control and On-Line Control
Shewhart Control Charts--Interpreting Shewhart Control Charts
Process Capability Evaluation

The second course will focus on acceptance sampling and will explore some of the Military and Commercial Standards that have been developed to aid acceptance control.

345-What Every Engineer Should Know about Statistical Process/Quality Control II

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5 $112.50

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

The focus of this course is to provide an understanding of the principles of Quality Assurance with a focus on Design for Robustness, Quality Loss, Loss Function Computation, and Acceptance control as well as current definitions, terminologies, inherent assumptions that are applicable in industry and as required by the US Government. It also introduces the student to the relevant Military Standards and other Government publications used in the industry. The course also further develops the concepts of system design, parameter design, and tolerance design, as the foundational elements of Robust Product Design. It also goes into a detailed analysis of the use of MIL-STAD-1916 and MIL-HDBK-1916 in establishing attribute-based Acceptance Sampling plans. The course is replete with numerical examples on the computation of the probability of acceptance (Pa) and other important parameters for single, double and multiple sampling plans including; the Average Outgoing Quality (AOQ), the Average Outgoing Quality Limit (AOQL), the Average Total Inspection (ATI), as well as the Average Sampling Number (ASN).

The course is concept based and uses basic arithmetic to develop the fundamental aspects of the techniques. The topics covered include:

Designing for Robustness

System Design, Parameter Design, Tolerance Design, Process Capability (Cp, Cpk) and Process Performance (Pp, Ppk), Process Errors, Quality Loss, and Loss Function

Acceptance Control

Lot Acceptance Sampling Plans (LASP):

Single sampling plans (SSP), Double sampling plans (DSP), Multiple sampling plans (MSP), Sequential Sampling Plans (SSP), Skip Lot Sampling Plans (SLSP), Operating Characteristics Curves (OC Curves), AOQ Curve

MIL- STD-1916 and MIL-HDBK-1916

Requirements and Applicability of MIL-STD-1916 and MIL-SHDBK-1916

Preferred sampling plans
Determination of sampling plan: Verification Level (VL), Code Letter (CL)
Sampling of lots or batches
Disposition of nonconforming product

391-What Every Engineer Should Know About Regression Analyses

(4.2) 4 Reviews

5 $112.50

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

Modern computing technologies and Big data have significantly changed the discourse on data mining and data efficacy. In any system where quantities change, it is of interest to look at the effects if any, of the system variables. Indeed, there may be a relationship (in our case statistical relationship) which may be approximated by a simple mathematical relationship. At other times, the mathematical or functional relationship may be complicated. Still there may be situations where there does not seem to be meaningful relationships between the variables and yet we might want to express or relate those variables by some sort of mathematical equations.

Regression Analysis is one of the most important statistical techniques used for data mining applications. It is a statistical methodology that helps estimate the strength and direction of the relationship between two or more variables, more specifically regressor and response variables and provides detailed insight that can be applied to further improve system outcomes. The importance of regression analysis lies in its singular focus on data which means numbers and figures that ultimately define a business entity. In Regression Analyses, two types of variables are of major concern, namely the regressor or predictive variables also known as independent variables, and the response variable.

The independent or predictor variable is one that is not random but is controlled (sometimes observed such as the amount of rainfall on a plot of land when the interest is on the effect of rainfall on crop yield) during an experiment. The dependent or response variable cannot be controlled but is rather measured as an outcome of the manipulation (or observation in the case of rainfall) of the independent variable and thus is a random variable. In this course, we will focus primarily on the following elements of Regression Analyses, namely:

Parameters & Estimates
Probability Distribution of the Parameters
Covariance between two variables
Simple hypothesis tests involving parameters including one- and two-sided t and F tests
Confidence Interval for the parameters
Orthogonal Columns, Diagonal and Symmetric Matrices
Estimation of model R2, Adjusted R2, (?? or r) to assess data efficacy
Coefficient of Variation (CV)
Multicollinearity and Variance Inflation Factors

484-What Every Engineer Should Know About Engineering Economic Analysis, Part I

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6 $135.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

This first course is a practical guide in Engineering Economic Analysis for the Professional Engineer. Although somewhat lengthy, the idea was to develop a course filled with practical examples that would serve as a ready reference for the Professional Engineer. There are more examples with drawings in this course material than the materials and references and that is the reason for the length of the document.

No unnecessary assumptions were made, and every formula was developed from first principles so that the engineer would feel comfortable that they at least can see and know where the formulas came from. We were careful to provide the formulas for the computation of the interest factors since we did not provide any interest tables.

Particular emphasis was given to different interest periods as well as nominal and effective interest rates. Both the internal rate of return (IRR) and the external rate of return(ERR) were given detailed treatments with copious practical examples. A lot of details on the Gradient series (Arithmetic and Geometric) were provided with succinct and very detailed drawings and practical examples. Detailed drawing and examples were also provided to deal with the topic of Capitalized Cost (CC) or Capital worth(CW).

The questions on quiz were ALL taken from the course material, every single one. So, there are no magic or tricky questions because we developed this as a hands-on practical course for the Professional Engineer.

The follow up second course focuses on the other remining topics such as Financial Decision Making Among Alternatives, Replacement Analysis, Decision Making Involving Risk, Break-Even and Minimum Cost Analysis, among others.

We hope that the Professional Engineer would be excited to have a ready reference for this important topic of Engineering Economic Analysis.