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2017　Probability and Statistics for Engineering and Sciences A

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Academic unit or major
School of Engineering
Instructor(s)
Berrar Daniel 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
Mon9-10(W611)  Thr9-10(W611)  
Group
A
Course number
XEG.G301
Credits
2
Academic year
2017
Offered quarter
3Q
Syllabus updated
2017/9/7
Lecture notes updated
-
Language used
English
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Course description and aims

This course covers the fundamentals of probability and statistics, with a focus on applications from engineering and the sciences. The course begins with an introduction to graphical data representation and descriptive statistics. Topics in probability include discrete and continuous random variables, probability rules, probability distributions, the law of large numbers, the central limit theorem, and expected value. Topics in statistics include sampling distributions, estimation of population parameters, confidence intervals, and significance testing. The course does not focus exclusively on concepts from the frequentist paradigm, but also introduces Bayesian statistics (Bayes' theorem, Bayesian hypothesis testing). The goal of this course is that the students acquire a solid statistical literacy that enables them to interpret statistical information and graphs and learn how to choose the appropriate statistical methodologies and tools to analyze data scientifically. To achieve this goal, the course includes many real-world examples from engineering and the sciences.

Student learning outcomes

After successful completion of this course, the students will
(1) understand how to interpret various graphical representations of statistical information;
(2) understand the key elements of probability and statistics;
(3) be able to analyze data scientifically with the appropriate statistical methodologies and tools;
(4) be able to adequately communicate analytical results in an interdisciplinary environment.

Keywords

histogram; box-and-whiskers plot; mean; variance; standard deviation; quartile; sample space; events; marginal probability; joint probability; conditional probability; random variable; Bayes theorem; central limit theorem; law of large numbers; Gamma function; binomial probability distribution; normal probability distribution; sampling distribution; confidence interval; significance testing; p-value; Bayesian hypothesis test.

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

Classes usually begin with a real-world example to motivate a statistical concept. This concept is then formally described, and mathematical proofs are given where appropriate. Then, we will solve the real-world problem together.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction; organizing and graphing data; bar charts and histograms None.
Class 2 Measures of dispersion and position; box-and-whiskers plot Revise contents of previous class; complete assignment
Class 3 Introduction to probability theory; marginal probability, joint probability, conditional probability Revise contents of previous class; complete assignment
Class 4 Probability rules; introduction to Bayes' theorem and Bayesian statistics Revise contents of previous class; complete assignment
Class 5 Multiplication and addition rules Revise contents of previous class; complete assignment
Class 6 Bayesian theorem: Applications Revise contents of previous class; complete assignment
Class 7 Probability distribution of discrete random variables; expected value Revise contents of previous class; complete assignment
Class 8 Binomial coefficient; Gamma function; binomial probability distribution Revise contents of previous class; complete assignment
Class 9 Probability distribution of a continuous random variable; normal probability distribution Revise contents of previous class; complete assignment
Class 10 Approximation of the binomial by the normal distribution; sampling distributions Revise contents of previous class; complete assignment
Class 11 Central limit theorem; sampling distribution of the sample mean and sample proportion Revise contents of previous class; complete assignment
Class 12 Point estimates and confidence intervals Revise contents of previous class; complete assignment
Class 13 Student's t-distribution and its applications Revise contents of previous class; complete assignment
Class 14 Significance testing; p-value; statistical inference Revise contents of previous class; complete assignment
Class 15 Bayesian hypothesis testing Revise contents of previous class

Textbook(s)

None required. Course materials are provided during class.

Reference books, course materials, etc.

Dimitri P. Bertsekas and John N. Tsitsiklis (2008) Introduction to Probability. Athena Scientific; 2nd edition; ISBN: 978-1-886529-23-6.

Assessment criteria and methods

Students' course grades will be based on the final exam.

Related courses

  • IEE.A204 ： Probability for Industrial Engineering and Economics
  • IEE.A205 ： Statistics for Industrial Engineering and Economics
  • ICT.M202: Probability and Statistics (ICT)

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Knowledge of elementary algebra and calculus is required.

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