語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
A Statistical Model for Predicting F...
~
ProQuest Information and Learning Co.
A Statistical Model for Predicting Failure of Institutions of Higher Education Using Financial and Other Institutional Parameters.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
A Statistical Model for Predicting Failure of Institutions of Higher Education Using Financial and Other Institutional Parameters./
作者:
Evans, Glenda Maria.
面頁冊數:
1 online resource (146 pages)
附註:
Source: Dissertation Abstracts International, Volume: 79-09(E), Section: A.
Contained By:
Dissertation Abstracts International79-09A(E).
標題:
Higher education. -
電子資源:
click for full text (PQDT)
ISBN:
9780355929256
A Statistical Model for Predicting Failure of Institutions of Higher Education Using Financial and Other Institutional Parameters.
Evans, Glenda Maria.
A Statistical Model for Predicting Failure of Institutions of Higher Education Using Financial and Other Institutional Parameters.
- 1 online resource (146 pages)
Source: Dissertation Abstracts International, Volume: 79-09(E), Section: A.
Thesis (Ph.D.)--Hampton University, 2018.
Includes bibliographical references
The purpose of this study was to create a bankruptcy predictor for higher education institutions. After a review of existing research, a clear need for a failure prediction method was discovered. The existing methods used by colleges and universities were either created for different sectors of businesses or lacked the transparency needed to allow the administrators the ability to verify rankings that the universities received. In this study, two different predictor models were created. Both utilized log regression models created for that purpose. The first model, the higher education bankruptcy predictor financial model, mimicked the Altman Z-model and contained five financial ratios. The second model, the higher education bankruptcy predictor model, used a larger sample of variables including financial and academic variables and was solved using logit regression. Both models proved to be effective at classifying closed and sustaining universities/colleges.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9780355929256Subjects--Topical Terms:
1148448
Higher education.
Index Terms--Genre/Form:
554714
Electronic books.
A Statistical Model for Predicting Failure of Institutions of Higher Education Using Financial and Other Institutional Parameters.
LDR
:02279ntm a2200349Ki 4500
001
919198
005
20181116131021.5
006
m o u
007
cr mn||||a|a||
008
190606s2018 xx obm 000 0 eng d
020
$a
9780355929256
035
$a
(MiAaPQ)AAI10814542
035
$a
(MiAaPQ)hampton:10130
035
$a
AAI10814542
040
$a
MiAaPQ
$b
eng
$c
MiAaPQ
$d
NTU
100
1
$a
Evans, Glenda Maria.
$3
1193712
245
1 2
$a
A Statistical Model for Predicting Failure of Institutions of Higher Education Using Financial and Other Institutional Parameters.
264
0
$c
2018
300
$a
1 online resource (146 pages)
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
500
$a
Source: Dissertation Abstracts International, Volume: 79-09(E), Section: A.
500
$a
Adviser: Sharad K. Maheshwari.
502
$a
Thesis (Ph.D.)--Hampton University, 2018.
504
$a
Includes bibliographical references
520
$a
The purpose of this study was to create a bankruptcy predictor for higher education institutions. After a review of existing research, a clear need for a failure prediction method was discovered. The existing methods used by colleges and universities were either created for different sectors of businesses or lacked the transparency needed to allow the administrators the ability to verify rankings that the universities received. In this study, two different predictor models were created. Both utilized log regression models created for that purpose. The first model, the higher education bankruptcy predictor financial model, mimicked the Altman Z-model and contained five financial ratios. The second model, the higher education bankruptcy predictor model, used a larger sample of variables including financial and academic variables and was solved using logit regression. Both models proved to be effective at classifying closed and sustaining universities/colleges.
533
$a
Electronic reproduction.
$b
Ann Arbor, Mich. :
$c
ProQuest,
$d
2018
538
$a
Mode of access: World Wide Web
650
4
$a
Higher education.
$3
1148448
650
4
$a
Finance.
$3
559073
650
4
$a
Economics.
$3
555568
655
7
$a
Electronic books.
$2
local
$3
554714
690
$a
0745
690
$a
0508
690
$a
0501
710
2
$a
ProQuest Information and Learning Co.
$3
1178819
710
2
$a
Hampton University.
$b
Business Administration.
$3
1193713
773
0
$t
Dissertation Abstracts International
$g
79-09A(E).
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10814542
$z
click for full text (PQDT)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入