Many engineering problems have inherent uncertainties; hence, it is important to learn tools from probability and statistics to understand, model, and manage these uncertainties. Topics covered in this course include combinatorial analysis, axioms of probability, conditional probability and independence, random variables, descriptive statistics, sampling distribution, tests of hypotheses based on small sample sizes, and regression. There will be an emphasis on applying these tools using computer-based methods; hence, there is a computer laboratory component. The course will also briefly cover numerical solutions of non-linear and differential equations. Weekly hours: 3 Lecture hours and 1.5 Practicum/Lab hoursPrerequisite(s): MATH 134 or (MATH 123 and MATH 124).
Many engineering problems have inherent uncertainties; hence, it is important to learn tools from probability and statistics to understand, model, and manage these uncertainties. Topics covered in this course include combinatorial analysis, axioms of probability, conditional probability and independence, random variables, descriptive statistics, sampling distribution, tests of hypotheses based on small sample sizes, and regression. There will be an emphasis on applying these tools using computer-based methods; hence, there is a computer laboratory component. The course will also briefly cover numerical solutions of non-linear and differential equations. Weekly hours: 3 Lecture hours and 1.5 Practicum/Lab hoursPrerequisite(s): MATH 134 or (MATH 123 and MATH 124).
