General linear models.
This chapter presents the general linear model as an extension to the two-sample t-test, analysis of variance (ANOVA), and linear regression. We illustrate the general linear model using two-way ANOVA as a prime example. The underlying principle of ANOVA, which is based on the decomposition of the value of an observed variable into grand mean, group effect and random noise, is emphasized. Further into this chapter, the F test is introduced as a means to test for the strength of group effect. The procedure of F test for identifying a parsimonious set of factors in explaining an outcome of interest is also described.
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Edward H
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Department of Biostatistical Sciences, Wake Forest University Health Sciences, Winston-Salem, NC, USA.
D000704:Analysis of Variance; D000818:Animals; D001920:Bradykinin / Q000097:blood; D003627:Data Interpretation, Statistical; D004285:Dogs; D005260:Female; D006689:Hodgkin Disease / Q000276:immunology; D006728:Hormones / Q000627:therapeutic use; D006801:Humans; D016014:Linear Models; D008297:Male; D051379:Mice; D009369:Neoplasms / Q000628:therapy; D010147:Pain Measurement / Q000379:methods; D011498:Protein Precursors / Q000097:blood
D006728:Hormones; D011498:Protein Precursors; C009858:bradykininogen; D001920:Bradykinin