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Real time employees overtime predictor model
Stearns, Shaun Gang, Isaac K.
Stearns, Shaun
Gang, Isaac K.
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Conference paper
Adobe PDF, 608.79 KB
Authors
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Time Period
Advisors
Original Date
Digitization Date
Issue Date
2021-03
Type
Conference paper
Genre
Keywords
New York City public employees,Overtime predictor model,Scheffe Test,R-Studio,Statistical analysis,Quaity planning and improvement
Subjects (LCSH)
Citation
Stearns, S., Gang, I.K. (2021). Real time employees overtime predictor model. Proceedings of the 2021 IEMS Conference, 27, 9-15. https://doi.org/10.62704/10057/24720
Abstract
Employers often struggle with a cost-efficient way to schedule workers and handle overtime. Paying employees overtime is a very inefficient and expensive way to keep a department going. In this paper, we will analyze data from New York City public workers to explore what factors influence the amount of overtime workers have accumulated. We are particularly interested in the factors that predict an increase in overtime and how to possibly adapt them. To accurately answer these questions, we use RStudio to analyze the predictor variables that would impact overtime for each New York City borough. We further analyzed the differences in overtime per Borough using ANOVA. Follow-up tests were performed using pairwise comparisons; differences per borough were corrected for multiple comparisons using Scheffe's method. After thorough analysis, we perform multiple regressions on each Borough.
Table of Contents
Description
Published in SOAR: Shocker Open Access Repository by Wichita State University Libraries Technical Services, May 2022.
Publisher
Association for Industry, Engineering and Management Systems
Journal
Book Title
Series
Proceedings of the 2021 IEMS Conference
v.27
v.27
Digital Collection
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Archival Collection
PubMed ID
ISSN
2690-3210 (print)
2690-3229 (online)
2690-3229 (online)