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Engineering Online

ISE 537 Statistical Model for System Analytics in ISE

3 Credit Hours

In this course, graduate students will learn basic data science methodologies. Examples of the methodologies include linear regression, generalized linear models, regularization and variable selection, and dimensionality reduction. In addition, students will also learn how to use these methods to solve real-world Industrial Engineering-related problems by analyzing industrial datasets and projects.

Prerequisite

Basic knowledge in statistics and probability.

Course Objectives

The purpose of this course is to teach students in Industrial Engineering some basic knowledge in data analytics and use data analytics methodologies to solve real-world applications in the Industrial Engineering domain.

Course Outcomes

  • Use linear regression to analyze data from industrial engineering applications. This includes the ability to 1) estimate the coefficients of linear regression, 2) build confidence intervals for the coefficients, 3) diagnose the high influence points and outliers, and 4) interpret the regression results
  • Use variable selection methods to identify the crucial variables in industrial engineering applications. This includes the ability to use and interpret 1) Ridge regression, 2) LASSO, 3) Forward stepwise regression, 4) Forward stage-wise regression, 5) Nonnegative Garrote, 6) LARS, 7) Adaptive LASSO, 8) group LASSO, and 9) Elastic Net
  • Use generalized linear models to analyze data for applications in industrial engineering. This includes the ability to 1) choose the appropriate link functions, 2) estimate parameters, 3) build the confidence interval of estimated coefficients, 4) diagnose outliers, and 5) interpret the regression results
  • Use commonly used dimensionality reduction methods to analyze industrial engineering data.
  • Use R packages to analyze real-world industrial engineering data.

Course Requirements

Grading Policy:

Midterm exam25%
Final exam25%
Assignments30%
Project20%

Textbook

No textbook is required. Some references are listed below:

  • Rencher, Alvin C., and G. Bruce Schaalje. Linear models in statistics. John Wiley & Sons, 2008.
  • Faraway, Julian J. Linear models with R. Chapman and Hall/CRC, 2016.
  • Faraway, Julian J. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Chapman and Hall/CRC, 2016.
  • Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. New York: Springer series in statistics, 2001.

Updated: 10/30/2022