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APM395/595: PROBABILITY AND STATISTICS FOR ENGINEERS PROFESSOR: TEACHING ASSISTANT: Chuck Kroll (470-6699) 424 Baker Lab [email protected] Ethan Bodnaruk 410 Baker Lab [email protected] OFFICE HOURS: To be announced. In general, will be during the 2 days before a homework assignment is due, and by appointment. MAILBOXES: The course mailbox is located in the hallway outside of Baker 402. LECTURES: Tuesdays and Thursdays 2:00 – 3:20 pm, 145 Baker Lab For APM595 and ERE496 Students: Thurs., 12:30 – 1:25, 437 Baker Lab TEXT: J.L. Devore, Probability and Statistics for Engineering and the Sciences, 8th edition [available for purchase at the SU Bookstore] Course reader [available for purchase in Bray Hall] PREREQUISITES: COURSE OBJECTIVES: First-year calculus, computer skills To provide an introduction to probability and statistical theory, statistical techniques, and uncertainty analysis with examples drawn from civil, environmental, agricultural, water resources, geospatial, and related engineering and environmental disciplines. TOPICS INCLUDE: Descriptive statistics including visual and numerical data presentation; Probability theory, including Bayes Theorem, conditional probability, independence, and counting; Theory of discrete and continuous probability distributions, including the introduction to a variety of commonly implemented probability distributions, parameter estimation techniques, and percentile and moment estimation; Classical hypothesis testing and confidence interval estimation; Linear regression and model building; and An introduction to ANOVA (if time is available). COURSE OUTCOMES: By the end of the semester students should be able to: Understand to need for statistical techniques in aiding with many aspects of their professional practice Apply their knowledge of statistical and other mathematical techniques to better understand, interpret, and solve engineering and environmental problems Analyze and interpret data as well as conceptualize the design of experiments Effectively communicate highly technical information in a clear and concise manner GRADING: NOTE: Quizzes Homework Semester Projects Classroom Participation Exam 1, Oct 13 Exam 2, Nov 10 Final 10% 15% 15% 5% 15% 15% 25% HOMEWORK ASSIGNMENTS WILL COUNT TOWARDS YOUR FINAL GRADE! IF YOU DON'T DO THE HOMEWORK YOUR HIGHEST POSSIBLE GRADE IS A B! PROBLEM SETS: Problem sets will be due in class, usually every Thursday. In general, late assignments will be accepted only with prior permission from the TA or Professor. For grading purposes, the lowest homework grade will be dropped. You will be required to use a variety of computer software packages in this class. You will learn to use the statistical package R (which is located in all computer clusters on campus and is available for free download online). ACADEMIC INTEGRITY: Any acts of plagarism, cheating, etc. will result in a grade of zero for the work submitted, as well as other possible academic actions. TENTATIVE COURSE SCHEDULE: Week of: Topics: August 7 Visual displays of data, Numerical displays of data Sections in Text: 1.1 – 1.4 September 3 Elements of probability, Conditional probability, Bayes theorem, Independence 2.1 – 2.2 2.4 – 2.5 September 10 Counting: Combinations and Permutations 2.3 September 17 Discrete random variables, Probability and cumulative distribution functions, Expectation and moments of discrete random variables 3.1 – 3.3 September 25 Bernoulli, geometric, and binomial distributions, Continuous random variables, expectations and moments of continuous random variables 3.4 – 3.5 4.1 – 4.2 October 1 Uniform and normal distributions 4.1 – 4.3 October 8 Review for Exam 1/Exam 1 October 15 Normal approximation to binomial distribution Lognormal distribution, Poisson and Gamma distributions 4.3 4.5 3.6, 4.4 October 22 Joint probability distributions, Covariance and correlation coefficients, Central limit theorem, Bias, variance, and mean square error, Parameter estimation 5.1 – 5.2, 5.3 – 5.4, 6.1 – 6.2 October 29 Hypothesis testing, Type I and II errors P-values, choice of hypothesis 8.1 – 8.2 8.4 – 8.5 November 5 Review for Exam 2/Exam 2 November 12 Simple and multiple linear regression, Parameter estimation, Model inferences and predictions November 19 Thanksgiving Break November 26 Probability plot correlation coefficients and tests of normality, Confidence intervals for the mean 14.2 7.1 – 7.3 December 3 Confidence intervals for the variance and standard deviation, course summary 7.4 12.1 – 12.5 13.4 – 13.5