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Course Description
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Course Name
Probability & Statistics
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Host University
Maynooth University
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Location
Dublin, Ireland
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Area of Study
Mathematics, Statistics
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Language Level
Taught In English
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Prerequisites
EE106 Engineering Mathematics 1, EE112 Engineering Mathematics 2.
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Course Level Recommendations
Upper
ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.
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ECTS Credits
5 -
Recommended U.S. Semester Credits2
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Recommended U.S. Quarter Units3
Hours & Credits
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Overview
Indicative Syllabus
? Probability Theory
? Introduction to Probability Theory and Forms of Data Presentation - Contingency Tables
? Review of Basic Set Theory
? Definition of Events in terms of sets
? Complementary and Mutually Exclusive Events.
? Interpretation of Probability in terms of relative frequencies.
? Axioms of Probability Theory
? Addition Formula for probability
? Conditional Probability
? Bayes Theorem and Law of Total Probability
? Concept of Independent Events
? Network Problems and Determination of Reliability of Networks
? Counting Techniques and Application to Sample Spaces with large numbers of sample points
? Discrete Random Variables and the Probability mass function
? Special Discrete Probability Distributions ? Binomial, Poisson Distribution
? Application of Poisson distribution to engineering problems - Queuing Theory
? Expected Value and Variance, Chebychev's Inequality
? Continuous Random Variables (Probability Density Function and Cumulative Distribution Function)
? Properties of a probability density, expected value and variance.
? The Gaussian (Normal) distribution and its properties and importance in application.
? The exponential distribution and its relation to the Poisson process and queuing theory
? The Gamma distribution and the Weibull Distribution and their application to modelling times to failure
? Statistics
? Introduction to Statistics ? Inference and Estimation of Parameters.
? The central limit theorem.
? Large/Small Sample confidence interval estimates for the population mean and the T-distribution.
? Large Sample confidence interval estimates for a population proportion.
? Introduction to hypothesis testing and the idea behind the process.
? Hypothesis testing on a population mean (large and small samples).
? Hypothesis testing on a population proportion (large sample).
? Categorical Data: Chi-squared goodness of fit test. Chi-squared independence test.
? Simple Linear Regression. Correlation/Causation. Prediction Intervals. Hypothesis testing.
? Discussion of relation of studied material to simple engineering experiment design.
Course Disclaimer
Courses and course hours of instruction are subject to change.
Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.
ECTS (European Credit Transfer and Accumulation System) credits are converted to semester credits/quarter units differently among U.S. universities. Students should confirm the conversion scale used at their home university when determining credit transfer.
Please reference fall and spring course lists as not all courses are taught during both semesters.
Please note that some courses with locals have recommended prerequisite courses. It is the student's responsibility to consult any recommended prerequisites prior to enrolling in their course.