東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Development of a prediction model fo...

Long, Joseph Charles.

FindBook

Google Book

Amazon

博客來

Development of a prediction model for the NCAA Division-I Football Championship Subdivision.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Development of a prediction model for the NCAA Division-I Football Championship Subdivision./
作者:	Long, Joseph Charles.
面頁冊數:	35 p.
附註:	Source: Masters Abstracts International, Volume: 51-06.
Contained By:	Masters Abstracts International51-06(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1536350
ISBN:	9781303045745

Development of a prediction model for the NCAA Division-I Football Championship Subdivision.
Long, Joseph Charles.

Development of a prediction model for the NCAA Division-I Football Championship Subdivision. - 35 p.

Source: Masters Abstracts International, Volume: 51-06.

Thesis (M.S.)--North Dakota State University, 2013.

This thesis investigates which in-game team statistics are most significant in determining the outcome in a NCAA Division-I Football Championship Subdivision (FCS) game. The data was analyzed using logistic and ordinary least squares regression techniques to create models that explained the outcome of the past games. The models were then used to predict games where the actual in-game statistics were unknown. A random sample of games from the 2012 NCAA Division-I Football Championship Subdivision regular season was used to test the accuracy of the models when used to predict future games. Various techniques were used to estimate the in-game statistics in the models for each individual team in order to predict future games. The most accurate technique consisted of using three game medians with respect to total yards gained by the teams in consideration. This technique correctly predicted 78.85% of the games in the sample data set when used with the logistic regression model.

ISBN: 9781303045745Subjects--Topical Terms:

517247
Statistics.

Development of a prediction model for the NCAA Division-I Football Championship Subdivision.
LDR:01853nam a2200277 4500 001 1959595
005 20140520124445.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303045745
035 $a (MiAaPQ)AAI1536350
035 $a AAI1536350
040 $a MiAaPQ $c MiAaPQ
100 1 $a Long, Joseph Charles. $3 2095057
245 1 0 $a Development of a prediction model for the NCAA Division-I Football Championship Subdivision.
300 $a 35 p.
500 $a Source: Masters Abstracts International, Volume: 51-06.
500 $a Adviser: Rhonda C. Magel.
502 $a Thesis (M.S.)--North Dakota State University, 2013.
520 $a This thesis investigates which in-game team statistics are most significant in determining the outcome in a NCAA Division-I Football Championship Subdivision (FCS) game. The data was analyzed using logistic and ordinary least squares regression techniques to create models that explained the outcome of the past games. The models were then used to predict games where the actual in-game statistics were unknown. A random sample of games from the 2012 NCAA Division-I Football Championship Subdivision regular season was used to test the accuracy of the models when used to predict future games. Various techniques were used to estimate the in-game statistics in the models for each individual team in order to predict future games. The most accurate technique consisted of using three game medians with respect to total yards gained by the teams in consideration. This technique correctly predicted 78.85% of the games in the sample data set when used with the logistic regression model.
590 $a School code: 0157.
650 4 $a Statistics. $3 517247
650 4 $a Business Administration, Sports Management. $3 1669318
690 $a 0463
690 $a 0430
710 2 $a North Dakota State University. $b Statistics. $3 2095058
773 0 $t Masters Abstracts International $g 51-06(E).
790 $a 0157
791 $a M.S.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1536350