ENGG2780 Statistics for Engineers

 Course code ENGG2780 Course title Statistics for Engineers 統計及其工程應用 Course description A first course in the fundamentals of statistics and their applications in engineering. Topics include populations and samples, point estimation, confidence intervals, hypothesis testing, and basics of linear regression. 本科教授統計學基礎及其在不同工程領域上的應用。內容包括：母群及樣本、點估計、區間估計、假設檢驗和線性回歸的基本概念。 Unit(s) 2 Course level Undergraduate Exclusion ENGG2430 or 2450 or ESTR2002 or 2005 or 2020 Semester 2 Grading basis Graded Grade Descriptors A/A-:  EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions. B+/B/B-:  GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered. C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems. D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems. F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course. Learning outcomes At the conclusion of the course, students should be able to 1. define and understand the fundamental concepts in statistics 2. identify, formulate, and solve simple statistical problems in engineering and data science application Assessment (for reference only) Essay test or exam：65% Homework or assignment：25% Others：10% Recommended Reading List 1. Jay L. Devore, Probability and Statistics for Engineering and the Sciences, CENGAGE, 9th Edition, 2016 2. Richard A. Johnson, Irwin Miller, and John E. Freund, Miller and Freund’s Probability and Statistics for Engineers, Pearson, 9th Edition, 2017

 CSCIN programme learning outcomes Course mapping Upon completion of their studies, students will be able to: 1. identify, formulate, and solve computer science problems (K/S); 2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S); 3. receive the broad education necessary to understand the impact of computer science solutions in a global and societal context (K/V); 4. communicate effectively (S/V); 5. succeed in research or industry related to computer science (K/S/V); 6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S); 7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V); 8. practise high standard of professional ethics (V); 9. draw on and integrate knowledge from many related areas (K/S/V); Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured