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dc.contributor.advisorMcCullough, Kristinen_US
dc.contributor.authorOu, Sinongen_US
dc.date.accessioned2017-05-18T19:28:08Z
dc.date.available2017-05-18T19:28:08Z
dc.date.issued2017-05-18
dc.identifier.urihttp://commons.lib.niu.edu/handle/10843/17630
dc.description.abstractWith the trend to apply statistics to predict sport games, the purpose of this paper is to find a model that can help to predict the percentage of games won for NBA teams during a season based on data for team and individual player performance. Multiple linear regression is used to build a predictive model, while time series analysis is used to assist with model selection. Great care is taken here, because statistical software will build a model regardless of collinearity, which means the model contains highly correlated variables, and despite whether regression assumptions are met. A general model that can predict all teams’ performance is found. The model basically fits every team, and even the worst predictions look decent. However, each team has its own philosophy, so each has different significant factors. Thus models built for individual teams perform better.en_US
dc.format.extent24 pagesen_US
dc.language.isoen_USen_US
dc.publisherNorthern Illinois Universityen_US
dc.rightsNIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.en_US
dc.subjectMultivariate Linear Regressionen_US
dc.subjectTime Seriesen_US
dc.subjectCollinearityen_US
dc.titleBuilding the Winning Percentage Model to Predict Regular Season Results of NBA Teams Based on Regression and Time Series Analysis of Common Basketball Statisticsen_US
dc.type.genreDissertation/Thesisen_US
dc.typeTexten_US
dc.contributor.departmentDivision of Statisticsen_US
dc.description.degreeB.S. (Bachelor of Science)en_US


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