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Probability and Statistics for Engineers II


 with Abeer Yasin

short description:
Probability and Statistics for Engineers part II serves as a continuing course of Probability and Statistics for Engineers part I.
long description:
Probability and Statistics for Engineers II
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 II serves as a continuing course of Probability and Statistics for Engineers part I. The course starts with the definition of a sample as a representative of the entire population once obtained through random sampling. Sampling distribution and the approximation of a binomial distribution by a normal distribution and the central limit theorem will be discussed in details. Control charts and process capability will be represented as an application of sampling distributions. Later, the course will focus on estimation procedures, hypothesis testing and regression analysis, simple and multiple as the procedures used to infer about a population using known sample parameters.

Learning Outcomes:
After successfully completing this course, students will be able to:
• Define a sample, sample parameters, a population and the population parameters.
• Apply the central limit theorem
• Calculate confidence intervals for a population mean or proportion
• Compare two population means when variances are equal or not equal
• Compare two population proportions
• Define regression and calculate the correlation coefficient and parameters of a linear regression model
• Build a linear regression model and use it for estimation and prediction
• Build a multivariable regression model and use it for estimation and prediction.
Lesson 1: Statistics, Sampling Distributions and Control Charts
Lesson 1 starts with a clear distinction between the sample and a population and the measures used for each. Further discussion of the sample properties and measures of its central tendency and dispersion will lead to a clear understanding of sampling distribution and the central limit theorem. Clear understanding of control charts, process capability and the method used for approximating distributions will conclude lesson 1.
Lesson 2: Estimation
Lesson 2 focuses on the definition of a point estimator versus and interval estimator to infer about a population parameter using sample parameters. Confidence intervals for the simple sample case as well as the multiple sample case will be discussed in great details for the parameters of a binomial and a normal distribution. Other intervals such as prediction intervals and tolerance intervals will be represented as an application with engineering problems.
Lesson 3: Hypothesis Testing
In this lesson we learn about rejecting or accepting a certain hypothesis made about a population inferring from a sample. The single sample case will be considered in this lesson in testing the mean and the variance, in case of a normal distribution, the probability of success, in case of a binomial distribution. The multiple sample case will focus on testing the difference between two means when variances are equal or not equal, as well as testing the ratio of variances using the student t-test or the F test where appropriate. This lesson will also introduce the student to chi square test in testing for equality among binomial parameters of n separate populations.
Lesson 4: Simple Regression
Lesson 4 discusses the regression of one variable upon another, a process that characterizes the lesson we learn about probabilistic models, fitting the model through the least-square method, the coefficient of correlation, the coefficient of determination and the appropriate use of the regression model for estimation and prediction and the variables quantities.
Lesson 5: Multiple Regression Analysis
Lesson 5 introduces the student to the possibility of expressing one variable as a combination of a number of independent variables, multiple regression analysis. The lesson will discuss fitting the model using the least-square method, estimation of variance, coefficient of determination, estimating and testing hypothesis about individual coefficient parameters, the use of SPSS (or excel) to obtain the multivariable model and using the model for estimation and prediction.

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 II

live session

Probability and Statistics for Engineers part II serves as a continuing course of Probability and Statistics for Engineers part I.

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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