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hi Soumyajit, 1.) parametric tests are not based on signs or ranks. you must have meant 'non-parametric' methods. Nonparametric test make less strict demands of the data. The central limit theorem does not apply strictly. When we apply parametric tests, underlying conditions or assumptions must be met, particularly for smaller sample sizes. For example, for the one-sample t test (comparing a sample mean to make an inference about a population parameter such as the population mean), for example, requires that the observations be drawn from a normally distributed population. For two independent samples, the t test has the additional requirement that the population standard deviations be equal. If these assumptions/conditions are violated, the resulting P-values and confidence intervals may not be trustworthy3. However, normality is not required for the Wilcoxon signed rank or rank sum tests to produce valid inferences about whether the median of a symmetric population is 0 or whether two samples are drawn from the same population. Thus signs and ranks are only used when the underlying assumptions are violated. I usually perform a visual inspection of the data via histograms, frequency tables. In some instances, if the sample is large enough, and in statistical terms this means greater than 30, then the data is strong enough to withstand non normality.....some 'purists' prefer to 'transform' the data via some geometric transformation to 'normalize' i.e. log 10 transformation etc. That decision you make as the person more intimate with the data. Some Commonly Used Statistical Tests Normal theory based test Corresponding nonparametric test Purpose of test t test for independent samples Mann-Whitney U test; Wilcoxon rank-sum test Compares two independent samples Paired t test Wilcoxon matched pairs signed-rank test Examines a set of differences Pearson correlation coefficient Spearman rank correlation coefficient Assesses the linear association between two variables. One way analysis of variance (Ftest) Kruskal-Wallis analysis of variance by ranks Compares three or more groups Two way analysis of variance Friedman Two way analysis of variance Compares groups classified by two different factors 2.) In regression analysis, we are trying to assess how much of the variance/variability we can predict in the Y or dependent variable from the independent or predictor variables. The 'predictor' variables are the variables that we can manipulate and it allows us to make a prediction of the Y value (outcome). Best, Paul E. Alexander ________________________________ From: soumyajit choudhury <[log in to unmask]> To: [log in to unmask] Sent: Sun, January 23, 2011 8:02:07 AM Subject: Concepts Hi, My questions are: Why parametric techniques are based on ranks/signs ? How regression analysis and predictable variable are inter-related? Soumyajit