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Cognitive and fatigue measures in multiple sclerosis patients grouped by whether or not subjects were taking medications with central nervous system (CNS) side effects (mean [+ or -] standard deviation and analysis of covariance results).
TABLE 1 Analysis of Covariance Results for Implementational Variables and Situational Factors Dependent Independent Adjusted Variable [R.
Table 1 Analysis of Covariance with Posttest Least Square Means, Standard Errors (in Parentheses), and cell Sizes for Ethical Awareness, Acceptance of Ethical Responsibility, and Issue Perspective (Scenarios/Dilemmas 1 - 3) Group/Variable SLE SLO CON F p n=110 n=193 n=173 Ratio Value Awareness - 1 2.
Analysis of covariance was selected in this case because of the covariates' preexisting differences.
Analysis of covariance necessitates homogeneity of slopes across the experimental groups.
The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches.
The Groups Test Group Pre-test Post-test Average Standard Average Standard Deviation Deviation Self- 39/8 4/31 44/46 3/62 Regulation The Groups Control Group Pre-test Post-test Average Standard Average Standard Deviation Deviation Self- 40/73 2/96 41/53 4/03 Regulation Table 4: The result of the analysis of covariance to investigate the difference of the test and the control groups in the self-regulation post-test Sig F MS Df SS Source Index 0/033 5/033 72/640 1 72/640 Group 0/230 1/509 21/780 1 21/780 Self-regulation Pre-test -- -- 14/433 27 389/687 Error -- -- 29 476/000 Total
provides an applied and comprehensive treatment of the analysis of covariance, which he says is one of the least understood and most misused of all statistical methods.
CHAPTER 1 AN INTRODUCTION TO GENERAL LINEAR MODELS: REGRESSION, ANALYSIS OF VARIANCE AND ANALYSIS OF COVARIANCE.
Descriptive analyzes were used to determine mean and standard deviation and inferential statistics were used in the analysis of covariance and software SPSS18.
They assume readers have a basic understanding of generalized linear models, such as multiple regression and logistic regression, but do discuss some basic methods in great detail, such as Pearson's correlation and the analysis of covariance.
An analysis of covariance demonstrated a statistically significant difference from placebo on the Sum of Pain Intensity Differences (SPID) over 50 hours using LOCF (last observation carried-forward) as the imputation method, however, the results were not statistically significant using LOCF/BOCF (baseline observation carried-forward), the primary endpoint.

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