2021 Statistics for Industrial Engineering and Economics

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Academic unit or major
Undergraduate major in Industrial Engineering and Economics
Miyakawa Masami  Suzuki Sadami 
Course component(s)
Lecture / Exercise    (ZOOM)
Day/Period(Room No.)
Tue1-2(W934)  Fri1-2(W934)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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Course description and aims

This course focuses on probability theory and statistics with an emphasis on solving problems by quantitative analysis in industrial engineering. Topics in probability include discrete and continuous random variables, probability distributions, the law of large numbers, and the central limit theorem. Topics in statistics include sample mean and variance, estimating distributions (point estimation and confidence intervals), hypotheses testing (t-test, Chi-square test, F-test and analysis of variance), correlation, and regression with some example in our everyday life problems.

This course intends to learn statistical way of thinking and apply statistical methodology and tools to industrial engineering and everyday life problems.

Student learning outcomes

By the end of this course, students will be able to:
(1) Understand the basic concepts of probability, random variables, probability distribution, parameter estimation and hypotheses testing.
(2) Compute and interpret basic statistics using numerical and graphical techniques.
(3) Use statistical methodology and tools in the engineering problem-solving process.
(4) Apply statistical way of thinking to industrial engineering and everyday life problems.


point estimation, interval estimation, hypotheses testing, t-test, Chi-square test, F-test (ANOVA; analysis of variance), Multiple regression analysis

Competencies that will be developed

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

Class flow

Give a lecture and give some exercise problems. Solutions for the exercise problems are also reviewed.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Mean and Variance Understand basic statistics
Class 2 Random Variables and Probability Distributions Understand about random variables and probability distributions
Class 3 Estimating Parameters Calculate estimating parameters and confidence intervals/confidence level
Class 4 Estimation of the Mean Estimation of the mean based on normal distribution
Class 5 Estimation of the Variance Estimation of basic statistics utilizing various distributions
Class 6 Test for the Mean Testing hypotheses about parameters of normal distribution and t-distribution
Class 7 Test for the Variance Testing hypotheses about parameters of Chi-square distribution and F-distribution
Class 8 Chi-Squared Goodness-of-fit Test Chi-Squared Goodness-of-fit Test and contingency tables
Class 9 One-way ANOVA (analysis of variance) Testing the equality of three or more means at one time by using variance
Class 10 Two-way ANOVA (analysis of variance) Understand the main effects of two independent variables and interaction effect between them
Class 11 Maximum Likelihood Estimation Understand Maximum Likelihood Estimation
Class 12 Distributions of Statistics and Sum of Squares Understand various distributions and calculate sum of squares
Class 13 Determination of testing methods Understand how to determine the testing methods
Class 14 Multiple Regression Analysis Modeling and calculating with multiple regression analysis

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.


Nothing in particular. Provide handouts when needed.

Reference books, course materials, etc.

Miyakawa, Masami. Statistical methodology. Tokyo: Kyoritsu Shuppan; ISBN-13: 978-4320016132. (Japanese)

Assessment criteria and methods

Final exam and exercise problems.

Related courses

  • IEE.A204 : Probability for Industrial Engineering and Economics
  • IEE.A331 : OR and Modeling
  • IEE.C302 : Quality Management

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

Students must have successfully completed "Probability for Industrial Engineering and Economics" or have equivalent knowledge.

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