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Probability and Statistics for Engineers I


 with Abeer Yasin

short description:
Probability and Statistics for Engineers part I will introduce engineering students to the use of statistics in solving engineering problems in everyday life.
long description:
Probability and Statistics for Engineers I
Instructor: Dr. Abeer Yasin
Course Duration: 4 months
Course Description:
Statistics is the science that is concerned with the different methods for collecting data and observations and organizes or displays such data using a number of methods for further analyses. Data provide the basis for many of the decisions made in our world, therefore the need for the appropriate tools for analysis of such data.
Statistics is closely tided with different fields of science such as mathematics, science, social sciences, sociology and human studies, medial sciences, engineering, as well as biology and genetics. Statisticians focus on samples of data concerning human activities and matters related to mankind hence the importance in the use of statistics at all times and all places.
Probability and Statistics for Engineers part I will introduce engineering students to the use of statistics in solving engineering problems in everyday life. Throughout the course, students will acquire statistical skills needed to collect, display and analyze data for further understanding of the nature of data and the model they therefore represent. The course places emphasis on graphical techniques such as stem-and-leaf plots, box plots and scatter plots to summarize data and display existing patterns. Later, the course will focus heavily on probability, random sampling and probability distributions of discrete, continuous and multivariate nature and their application in the engineering field.

Learning Outcomes:
After successfully completing this course, students will be able to:
• Define statistics and its importance in the engineering industry
• Understand the different types of data used in statistics
• Distinguish between the various statistical tools used to display data and determine the appropriate use of each.
• Acquire the statistical tools needed to calculate measures of central tendency and measure of dispersion.
• Define a random distribution its relationship to probability
• Distinguish between discrete random variables and continuous random variables and determine the situation when each applies to an engineering problem
Lesson 1: Data collection, display and analysis
Lesson 1 will introduce the student to methods used in collecting data, types of data, tables, graphs and charts used to display data. Analysis of data is done using the cause and effect diagram, stem and leaf plot, histogram, box plot and the inter-quartile range, scatter plot and time series chart. More refined tools are then used for further analysis and understanding of the nature of data collected. These tools include measure of central tendency and measures of dispersion.
Lesson2: From Data Tables to Discrete Probability
This lesson will focus on the definition of events, sample spaces and probability. Functions on events such as union and intersection, complementary events, the additive rule and mutually exclusive events, conditional probability, the multiplicative probability and independent events will be discussed in detail with appropriate applications to engineering. Random sampling will be discussed in further details as a method of collection of unbiased data.
Lesson 3: Discrete Probability Distributions
Lesson 3 will discuss in great details random variables and their probability distributions as well as their expected values (the mean). Various examples of discrete probability distributions such as the Bernoulli distribution, the geometric distribution, the negative binomial distribution, the Poisson distribution and the hyper-geometric distribution, will be discussed in further details.
Lesson 4: Continuous Probability Distribution
In lesson 4 continuous random variables, their probabilities and expected values will be demonstrated. Various examples of continuous probability distributions such as the uniform distribution, the exponential distribution, the Gamma distribution, the normal distribution and the beta distribution will be discussed in further details.
Lesson 5: Multivariate Probability Distributions
Lesson 5 discusses in greater details bivariate and marginal probability distributions, conditional probability distributions, independent random variables, expected values of functions of random variables, the multinomial distribution and the moment generating function.

Textbook: Richard L. Scheaffer and James T McClave. Probability and Statistics for Engineers. 4th edition. Duxbury Press (1995).

level of difficulty:
all welcome
minimum class size:
Available online by appointment
fee comments:
 session structure





Probability and Statistics for Engineers I

live session

Probability and Statistics for Engineers part I will introduce engineering students to the use of statistics in solving engineering problems in everyday life.

1h 0m

total duration: 1h 0m over 1 session(s)
comments: n.a.

discussion forums: 1

duration: 1h 0m
fee: 99US$  (990lp)
payment: at booking
delivery method: live online

Quick Help


Abeer Yasin

description of :

Teacher's qualifications:

Courses taught on- ground and online:
 College Algebra
 Pre-Calculus
 Calculus I
 Calculus II
 Calculus III
 Fundamentals of Mathematics
 Elementary Probability and Statistics
 Business Mathematics
 Business Statistics
 Biostatistics
 Advanced Business Statistics
 Discrete Mathematics
 Business Mathematics and Economics
 Business Statistics and Research
 Statistics and Probability
 Statistics I
 Statistics II
 Mathematical Statistics
 Statistical Methods
 Research and Statistics
 Probability and Statistics for Engineers
 Applied Mathematics
 Numerical Analysis
 Engineering Mathematics
 Mathematics for Medical Students
 General Mathematics
 SPSS and Statistics
 Introductory Mathematics
 Intermediate Mathematics
 Introductory Algebra
 Intermediate Algebra
 Linear Algebra I
 Finite Mathematics
 Trigonometry
 Basic Mathematics
 Finite Mathematics
 Complex Analysis
 Ordinary Differential Equations
 Partial Differential Equations
 Fundamentals of Accounting I
 Fundamentals of Accounting II
 Principles of Macroeconomics
 Principles of Microeconomics
 Finite Mathematics
 Linear Algebra
 Quantitative Reasoning for Business (Graduate, Research)
 Doctoral Dissertation Courses
• DOC 722
• DOC 733
• DOC 733 A
• DOC 733B
• DOC 734
• DOC 734 A
• DOC 734 B

Computer skills
• Microsoft Excel
• Microsoft word
• Microsoft power point
• Microsoft environment
• Windows environment.
• Latex MiKTeX (2.7)
• SPSS Statistical Software.
• Graph Pad Prism Statistical Software.
• MedCalc Statistical Software
• Analyze it for Microsoft Excel Statistical Software
• MathCad
• MathXpert
• Equation Wizard
• ESB Stats
• WinPlot
• Microsoft Math
• Math Magic Personal 3.64
• Equation Conversion Manager
• Math Type
• 7Math
• End Note
• Reference Manager
• Project Kick Start
• LMS Chart Maker
• OpenStat
• Assistat (Statistical Assistance, v 7.6 beta)
• AM Statistical Software (product of the American Institutes of Research, v 0.06)
• StatEasy (v 0.4
• PQRS (Probabilities, Quantiles and Random Samples)
• WinIDAMS (Validation, Manipulation and Statistical Analysis of Data, v 1.3, UNESCO)
• InStat (Statistical Services Center, v 3.036)

Research interests:
 Mathematics and Engineering:
 Nonlinear Partial Differential Equations.
 P-Laplace Operator and Applications in Engineering and Medicine
 Functional analysis.
 Dynamical systems.
 Pure and Applied Mathematics in Medicine:
 Bio-Fluid Dynamics.
 Bio-Fluid Mechanics
 Biomedical Engineering
 Statistics and Applied Mathematics in Medicine:
 Obesity.
 Glomerular Filtration Rate estimation (e-GFR).
 Cystatin C and Nephrology.
 Cystatin C and Cardiology
 Cystatin C
 GDF-15 (Growth Differentiation Factor-15)
 Beta Trace Protein
 Modeling of Mathematical Phenomena in Nephrology and Cardiology

's preferred teaching style:
Online-Virtual class room

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