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.
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
✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Give a lecture and give some exercise problems. Solutions for the exercise problems are also reviewed.
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 |
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.
Miyakawa, Masami. Statistical methodology. Tokyo: Kyoritsu Shuppan; ISBN-13: 978-4320016132. (Japanese)
Final exam and exercise problems.
Students must have successfully completed "Probability for Industrial Engineering and Economics" or have equivalent knowledge.