By Brian S. Everitt
Because the first variation of this publication used to be released, S-PLUS has developed markedly with new tools of study, new graphical procedures, and a handy graphical consumer interface (GUI). this day, S-PLUS is the statistical software program of selection for lots of utilized researchers in disciplines starting from finance to medication. Combining the command line language and GUI of S-PLUS now makes this ebook much more appropriate for green clients, scholars, and someone with out the time, endurance, or heritage had to battle through the various extra complicated manuals and texts out there.
The moment version of A guide of Statistical Analyses utilizing S-Plus has been thoroughly revised to supply a superb advent to the most recent model of this strong software program process. every one bankruptcy specializes in a specific statistical procedure, applies it to at least one or extra info units, and exhibits tips on how to generate the proposed analyses and pictures utilizing S-PLUS. the writer explains S-PLUS capabilities from either the Windows® and command-line views and obviously demonstrates easy methods to swap among the 2.
This instruction manual presents the appropriate automobile for introducing the interesting percentages S-PLUS, S-PLUS 2000, and S-PLUS 6 carry for information research. all the info units utilized in the textual content, besides script records giving the command language utilized in every one bankruptcy, can be found for obtain from the web at http://www.iop.kcl.ac.uk/iop/Departments/BioComp/splus.shtml
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Additional info for A handbook of statistical analyses using S-PLUS
Use of multiple comparison tests to examine in more detail which Poison means and which Treatment means differ. 1 ANOVA dialog showing main effects model for the data on survival times of rats. , the differences between the observed values and those predicted by the model. A normal probability plot of the residuals will lie used to assess assumption (1>, and a plot of residuals against fitted values can lie used t o evaluate assumption (2). Departures from a ‘horizontal band’ shape give cause for concern.
3. 4. Reanalyse the survival times of rats data after taking a log transformation. Examine the residuals from whatever model you now find is appropriate to assess whether the normality and constant variance assumptions are met more satisfactorily than when modelling the raw data. Reanalyse the slimming data after removing the two possible outliers identified in the text. Are the conclusions from the analysis the same as those discussed in the text? Use the predict function on the results of a main effects analysis of variance model for the survival time data to find the fitted values.
3 Normal probability plot of residuals from main effects model fitted to rat survival data. 5 confirms the highly significant main effects of both Poison and Treatment. The normal plot of the residuals suggests some departure from normality and the residuals vs. fitted values plot has a ‘wedge shape’ consistent with a departure from the constant variance assumption. Both findings suggest that analysing the survival times after a suitable transformation might be more appropriate than analysing the raw data.
A handbook of statistical analyses using S-PLUS by Brian S. Everitt