








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.
Lessons:
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 ttest 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 leastsquare 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 leastsquare 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:
1
availability:
Available online by appointment
fee comments:
n.a.

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Probability and Statistics for Engineers II


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


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duration:
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fee:
99US$ (990lp)
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Abeer Yasin



description of :
Teacher's qualifications:
Courses taught on ground and online:
College Algebra
PreCalculus
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
VUE
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)
ARC (XLISPSTAT)
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.
PLaplace Operator and Applications in Engineering and Medicine
Functional analysis.
Dynamical systems.
Pure and Applied Mathematics in Medicine:
BioFluid Dynamics.
BioFluid Mechanics
Biomedical Engineering
Statistics and Applied Mathematics in Medicine:
Obesity.
Glomerular Filtration Rate estimation (eGFR).
Cystatin C and Nephrology.
Cystatin C and Cardiology
Cystatin C
GDF15 (Growth Differentiation Factor15)
Beta Trace Protein
Modeling of Mathematical Phenomena in Nephrology and Cardiology
's preferred teaching style:
OnlineVirtual class room


