6 Lectures / 26 Sections / 28h 57m 20s
Lecture 1:Introduction to Statistics and Data Analysis
Lecture 2:Probability
Lecture 3:Random Variables and Probability Distributions
Lecture 4:Mathematical Expectation
Lecture 5:Some Discrete Probability Distributions
Lecture 6:Some Continuous Probability Distributions
Introduction
Duration| 28h 57m 20s
Total Lectures| 6 Lectures 26 Sections

This course was lectured in Chinese, and the videos include English captions.

Course keywords:
隨機變數(Random Variable), 期望值(Mathematical Expectation), 機率分配(Probability Distribution), 動差(Moments), 動差生成函數(Moment-Generating Function)

一、課程說明(Course Description)
本課程強調與說明機率模式與統計方法在工業工程的各種應用。目的在讓學生瞭解統計原理及技
巧,並應用於工程問題分析。
This course emphasizes and illustrates the use of probabilistic models and
statistical methodology that is employed in countless applications in
industrial engineering. It gives the student an understanding of the logic
behind statistical techniques as well as practice in using them.

二、指定用書(Text Books)
Walpole, R.E., Myers, R.H., Myers, S. L. and Ye, Keying (2016). Probability
and Statistics For Engineers and Scientists. (9th edition). Global Edition,
Pearson Education.

三、參考書籍(References)
1.Johnson, R. A. (2011). Probability and Statistics for Engineers (8th
edition). Prentice-Hall Inc.
2.Ross, S. M. (2009). Introduction to Probability and Statistics (4th
edition). Elsevier Academic Press.
3.Keller, G. and Warrack, B. (2014). Statistics for Management and Economics
(10th edition). Duxbury.
4.Walpole, R.E., Myers, R.H., Myers, S. L. and Ye, Keying (2007, 2012).
Probability and Statistics For Engineers and Scientists. (8th and 9th
editions). International Edition, Prentice-Hall Inc.

四、教學方式(Teaching Method)
Lecturing and discussion

五、教學進度(Syllabus)
Ch 1: Introduction to Statistics and Data Analysis.
Ch 2: Probability.
Ch 3: Random Variables and Probability Distributions.
Ch 4: Mathematical Expectations.
Ch 5: Some Discrete Probability Distributions.
Ch 6: Some Continuous Probability Distributions.
Ch 7: Functions of Random Variables. (Optional)

六、成績考核(Evaluation)
Midterm Exam (30%)
Final Exam (40%)
Others (30%)
-Quizzes
-Class Participation
-Homework Assignments/Practices/Discussions

Lectures
Lecture 1:Introduction to Statistics and Data Analysis
Section 1 - Overaview, Types of Data, Graphical Techniques
01:27:41
Section 2 - Graphical Techniques, Numerical Descriptive Measures
01:14:38
Lecture 2:Probability
Section 1 - Sample Space, Events
58:06
Section 2 - Counting Sample Points
01:17:10
Section 3 - Probability of an Event, Additive Rules
01:27:42
Section 4 - Conditional Probability, Independence and the Product Rules
01:22:15
Section 5 - Bayes' Rule
38:39
Lecture 3:Random Variables and Probability Distributions
Section 1 - Concept of a Random Variable, Discrete Probability Distributions
01:17:43
Section 2 - Continuous Probability Distributions
38:20
Section 3 - Joint Probability Distributions
01:23:01
Lecture 4:Mathematical Expectation
Section 1 - Mean of a Random Variable
01:06:54
Section 2 - Variance and Covariance
01:13:16
Section 3 - Means and Variances of Linear Combinations of Random Variables
01:26:47
Section 4 - Chebyshev's Theorem
44:39
Lecture 5:Some Discrete Probability Distributions
Section 1 - Introduction and Motivation
45:41
Section 2 - Binomial and Multinomial Distributions
01:24:21
Section 3 - Hypergeometric Distribution
01:12:12
Section 4 - Negative Binomial and Geometric Distributions
49:25
Section 5 - Poisson Distribution and the Poisson Process
01:21:57
Lecture 6:Some Continuous Probability Distributions
Section 1 - Continuous Uniform Distribution
41:15
Section 2 - Normal Distribution, Areas under the Normal Curve
01:26:29
Section 3 - Applications of the Normal Distributions
31:17
Section 4 - Normal Approximation to the Binomial
01:01:10
Section 5 - Exponential Distributions
01:08:02
Section 6 - Gamma Distributions
01:34:42
Section 7 - Gamma and Exponential Distributions: Selected Exercises
43:58
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Prof. Wu Chien-Wei

Dept.  Industrial Engineering and Engineering Management

Research Field

Quality Engineering and Management, Process Capability Analysis, Statistical Inference and Applications, Statistical Process Control, Six Sigma Methodology and Applications, Data Analysis

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