DSpace at EWHA: Generalized additive models using Excel macro and VBA

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DC Field	Value	Language
dc.contributor.author	석혜은	-
dc.creator	석혜은	-
dc.date.accessioned	2016-08-25T02:08:14Z	-
dc.date.available	2016-08-25T02:08:14Z	-
dc.date.issued	1997	-
dc.identifier.other	OAK-000000023395	-
dc.identifier.uri	https://dspace.ewha.ac.kr/handle/2015.oak/173967	-
dc.identifier.uri	http://dcollection.ewha.ac.kr/jsp/common/DcLoOrgPer.jsp?sItemId=000000023395	-
dc.description.abstract	Excel v7.0 is recently very popular as a spreadsheet and useful software tool in data analysis for many non-statistician users, and specially offers a linear regression analysis module for the users. However, if the dependence of E(Y) on X in a regression model is far from linear, the procedure may lead them to an inadequate model. To avoid this situation, they might need to use another statistical software other than EXCEL, which makes them to spend extra time to learn it. Thus, several well-known modern regression techniques such as GAM(Generalized Additive Models) and ACE(Alternating Conditional Expectation) are programmed in EXCEL MACRO and VBA(Visual Basic for Application) and integrated herein. EXCEL v7.0 with this procedure helps the users get closer to an adequate interpretation of a relationship between response and predictors. ;엑셀(EXCEL)V7.0은 비통계학자들이 자료분석을 할 때 많이 사용하는 스프레드시트(spreadsheet)이며 유용한 소프트웨어(software)이다. 또한, 엑셀은 선형회귀모듈을 제공한다. 그러나, 회귀모형에서 X에 대한 E(Y)의 종속관계가 선형이 아니라면, 그 프로시져는 부적합한 모형으로 적합될 것이다. 그들에게는 엑셀이 아닌 다른 통계 소프트웨어를 습득하기 위해 이외의 시간(extra time)이 요구된다. 그래서, 최근에 잘 알려진 GAM(Generalized Additive Models)과 ACE(Alternating Conditional Expectation) Procedure를 엑셀 매크로와 VBA(Visual Basic for Application)로 구현해 보았다. 엑셀이 이러한 프로시져를 포함한다면, response와 predictor의 관계를 좀 더 정확하게 판단하는데 도움이 될 것이다.	-
dc.description.tableofcontents	CONTENTS ABSTRACT Ⅰ. INTRODUCTION = 1 Ⅱ. Scatterplot Smoothers = 4 2.1 Polynomials = 4 2.2 Natural Splines = 5 2.3 (Cubic) Smoothing Splines = 5 2.4 Locally-Weighted Running-Line Smoothers (Loess) = 6 2.5 Kernel Smoothers = 7 2.6 Supersmoother = 7 2.7 Examples = 8 Ⅲ. Additive Models = 11 3.1 Additive Models = 11 3.2 Fitting Additive Models = 12 3.3 Numerical Example = 12 Ⅳ. Generalized Additive Models = 14 4.1 Fisher Scoring for Generalized Linear Models = 14 4.2 Local Scoring for Generalized Addative Models = 17 4.3 Numerical Examples = 19 Ⅴ. Response Transformation Models = 22 5.1 Alternating Conditional Expectation (ACE) Algorithm = 22 5.2 Numerical Examples = 24 Ⅵ. Manual = 27 Ⅶ. Conclusion = 29 REFERENCE = 30 논문초록 = 31 감사의 글 = 32	-
dc.format	application/pdf	-
dc.format.extent	2244330 bytes	-
dc.language	eng	-
dc.publisher	The Graduate School of Ewha Women's University	-
dc.subject	Excel	-
dc.subject	VBA	-
dc.subject	macro	-
dc.subject	model	-
dc.title	Generalized additive models using Excel macro and VBA	-
dc.type	Master's Thesis	-
dc.format.page	32 p.	-
dc.identifier.thesisdegree	Master	-
dc.identifier.major	대학원 통계학과	-
dc.date.awarded	1998. 2	-