Date on Master's Thesis/Doctoral Dissertation


Document Type

Master's Thesis

Degree Name

M. Eng.


Industrial Engineering

Degree Program

JB Speed School of Engineering

Committee Chair

Bai, Lihui

Committee Co-Chair (if applicable)

Gentili, Monica

Committee Member

Hawkins, Nick

Committee Member

Gerber, Erin

Author's Keywords

machine learning; regression; Gaussian regression


Two indices published monthly by the Logistics and Distribution Institute (LoDI) predict changes in logistics and distribution activity levels nationally and regionally and are useful for organizations when planning projects and expenses. This research validates the current linear regression model, updates the index conversion method, and introduces machine learning models.

New source data are introduced to the models to validate the current linear regression model and a comparative analysis verifies that the current source data are robust. A rolling average is used for index conversion in place of a fixed reference month to reflect recent changes in employment levels.

Three linear machine learning models are tested on the data. Patterns among the residuals indicate non-linearity. Four non-linear models are tested on the data and compared to the linear models. The non-linear models are found to be more accurate than the linear models for both the national index and regional index.