scholarly journals Use of a general analysis of variance program in missing data situations

1971 ◽  
Vol 3 (4) ◽  
pp. 202-203 ◽  
Author(s):  
John F. Walsh
Keyword(s):  
Missing Data ◽  
Analysis Of Variance ◽  
General Analysis ◽  
Variance Program

scholarly journals UNIVARAN : A universal analysis of variance program

1983 ◽  
Vol 15 (6) ◽  
pp. 607-607 ◽  
Author(s):  
Gernot von Collani ◽  
Gerd Waloszek
Keyword(s):  
Analysis Of Variance ◽  
Variance Program

Multiple linear regression is a useful alternative to traditional analyses of variance

1988 ◽  
Vol 255 (3) ◽  
pp. R353-R367 ◽  
Author(s):  
B. K. Slinker ◽  
S. A. Glantz
Keyword(s):  
Missing Data ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Analysis Of Variance ◽  
Continuous Variable ◽  
Parameter Estimates ◽  
Regression Problem ◽  
Regression Approach ◽  
Linear Regression Problem ◽  
Analyze Data

Physiologists often wish to compare the effects of several different treatments on a continuous variable of interest, which requires an analysis of variance. Analysis of variance, as presented in most statistics texts, generally requires that there be no missing data and often that each sample group be the same size. Unfortunately, this requirement is rarely satisfied, and investigators are confronted with the problem of how to analyze data that do not strictly fit the traditional analysis of variance paradigm. One can avoid these pitfalls by recasting the analysis of variance as a multiple linear regression problem. When there are no missing data, the results of a traditional analysis of variance and the corresponding multiple regression problem are identical; when the sample sizes are unequal or there are missing data, one can use a regression formulation to analyze data that cannot be easily handled in a traditional analysis of variance paradigm and thus overcome a practical computational limitation of traditional analysis of variance. In addition to overcoming practical limitations of traditional analysis of variance, the multiple linear regression approach is more efficient because in one run of a statistics routine, not only is the analysis of variance done but also one obtains estimates of the size of the treatment effects (as opposed to just an indication of whether such effects are present or not), and many of the pairwise multiple comparisons are done (they are equivalent to t tests for significance of the regression parameter estimates). Finally, interaction between the different treatment factors is easier to interpret than it is in traditional analysis of variance.

Balanced N-Way Analysis-of-Variance Program

1988 ◽  
Vol 42 (3) ◽  
pp. 226 ◽  
Author(s):  
Robert A. Campbell
Keyword(s):  
Analysis Of Variance ◽  
Variance Program

An analysis of variance program for the evaluation of results of parallel line assays

1982 ◽  
Vol 14 (2) ◽  
pp. 197-205 ◽  
Author(s):  
J.M. Nouchedehi ◽  
Ronald J. White ◽  
C.D.R. Dunn
Keyword(s):  
Analysis Of Variance ◽  
Parallel Line ◽  
Variance Program
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