Whitaker, Michael D.
Overview
| Works: | 15 works in 20 publications in 1 language and 12 library holdings |
|---|---|
| Roles: | Author |
Publication Timeline
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Most widely held works by
Michael D Whitaker
Diabetes : managing insulin(
Recording
)
1 edition published in 2011 in English and held by 2 WorldCat member libraries worldwide
1 edition published in 2011 in English and held by 2 WorldCat member libraries worldwide
Endocrinology update by
Michael D Whitaker(
Recording
)
2 editions published between 2008 and 2011 in English and held by 2 WorldCat member libraries worldwide
2 editions published between 2008 and 2011 in English and held by 2 WorldCat member libraries worldwide
New directions in diabetes(
Recording
)
2 editions published in 2007 in English and held by 1 WorldCat member library worldwide
2 editions published in 2007 in English and held by 1 WorldCat member library worldwide
Osteoporosis and other bone problems(
Recording
)
2 editions published in 2007 in English and held by 1 WorldCat member library worldwide
2 editions published in 2007 in English and held by 1 WorldCat member library worldwide
A gland review : managing thyroid and parathyroid diseases(
Recording
)
1 edition published in 2012 in English and held by 1 WorldCat member library worldwide
1 edition published in 2012 in English and held by 1 WorldCat member library worldwide
Diabetes : designs for management(
Recording
)
1 edition published in 2012 in English and held by 1 WorldCat member library worldwide
1 edition published in 2012 in English and held by 1 WorldCat member library worldwide
Computing min and max scorings for two-sample ordinal data by
Lyn R Whitaker(
)
2 editions published in 1996 in English and held by 1 WorldCat member library worldwide
Ordinal response variables often occur in practice. For example, in clinical trials a subject's response to a drug regime might be categorized as negative, none, fair, or good. There are several common approaches to analyzing two-sample ordinal response data. These procedures applied to the same data can lead to contradictory conclusions. In an attempt to reconcile contradictory results and provide guidance to the practitioner, Kimledorf, Sampson and Whitaker (1992) propose an alternative approach. They find the scores which when assigned to the levels of the ordinal response variable maximize a two-sample test statistic and the scores that minimize that same statistic. Since many of the two-sample statistics are related by monotonic transformations, these extreme scores are in fact extreme scores for several test statistics. Both minimized and maximized test statistics falling into the rejection region clearly indicate a difference between the two populations or treatments. On the other hand if neither of the two extreme statistics fall in the rejection region then no matter what scores are used there will be no significant difference in the two populations. In this paper we review the KsW procedure and its implementation in SAS software
2 editions published in 1996 in English and held by 1 WorldCat member library worldwide
Ordinal response variables often occur in practice. For example, in clinical trials a subject's response to a drug regime might be categorized as negative, none, fair, or good. There are several common approaches to analyzing two-sample ordinal response data. These procedures applied to the same data can lead to contradictory conclusions. In an attempt to reconcile contradictory results and provide guidance to the practitioner, Kimledorf, Sampson and Whitaker (1992) propose an alternative approach. They find the scores which when assigned to the levels of the ordinal response variable maximize a two-sample test statistic and the scores that minimize that same statistic. Since many of the two-sample statistics are related by monotonic transformations, these extreme scores are in fact extreme scores for several test statistics. Both minimized and maximized test statistics falling into the rejection region clearly indicate a difference between the two populations or treatments. On the other hand if neither of the two extreme statistics fall in the rejection region then no matter what scores are used there will be no significant difference in the two populations. In this paper we review the KsW procedure and its implementation in SAS software
Diabetes : diagnosis and complications by
Michael D Whitaker(
Recording
)
1 edition published in 2014 in English and held by 1 WorldCat member library worldwide
1 edition published in 2014 in English and held by 1 WorldCat member library worldwide
Update on skeletal health(
Recording
)
1 edition published in 2012 in English and held by 1 WorldCat member library worldwide
1 edition published in 2012 in English and held by 1 WorldCat member library worldwide
Thinking outside the bones(
Recording
)
1 edition published in 2006 in English and held by 0 WorldCat member libraries worldwide
1 edition published in 2006 in English and held by 0 WorldCat member libraries worldwide
Diabetes Today(
Recording
)
2 editions published in 2004 in English and held by 0 WorldCat member libraries worldwide
2 editions published in 2004 in English and held by 0 WorldCat member libraries worldwide
Thinking outside the bone(
Recording
)
1 edition published in 2006 in English and held by 0 WorldCat member libraries worldwide
1 edition published in 2006 in English and held by 0 WorldCat member libraries worldwide
Contemporary management of diabetes(
Recording
)
in English and held by 0 WorldCat member libraries worldwide
in English and held by 0 WorldCat member libraries worldwide
Advances in Women's Health : Genital Infections(
Recording
)
1 edition published in 2006 in English and held by 0 WorldCat member libraries worldwide
1 edition published in 2006 in English and held by 0 WorldCat member libraries worldwide
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