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- https://chem.libretexts.org/Bookshelves/Analytical_Chemistry/Instrumental_Analysis_(LibreTexts)/35%3A_AppendiciesThe appendices gathered here provide a lengthy introduction to the analysis of data, a variety of tables that contain critical values for the statistical analysis of data and standard oxidation-reduct...The appendices gathered here provide a lengthy introduction to the analysis of data, a variety of tables that contain critical values for the statistical analysis of data and standard oxidation-reduction potential, a discussion of activity, and a list of acronyms and abbreviations used in this textbook and in other resources for analytical chemistry.
- https://chem.libretexts.org/Bookshelves/Analytical_Chemistry/Instrumental_Analysis_(LibreTexts)/35%3A_Appendicies/35.04%3A_Critical_Values_for_F-TestThe following tables provide values for F(0.05,νnum,νdenom) for one-tailed and for two-tailed F-tests. To use these tables, we first decide whether the situation calls for a ...The following tables provide values for F(0.05,νnum,νdenom) for one-tailed and for two-tailed F-tests. To use these tables, we first decide whether the situation calls for a one-tailed or a two-tailed analysis and calculate F exp Next, we compare Fexp to F(0.05,νnum,νdenom) and reject the null hypothesis if Fexp>F(0.05,νnum,νdenom). Table \PageIndex1: Critical Values of F for a One-Tailed F-Test
- https://chem.libretexts.org/Bookshelves/Analytical_Chemistry/Instrumental_Analysis_(LibreTexts)/35%3A_Appendicies/35.05%3A_Critical_Values_for_Dixon's_Q-TestThe following table provides critical values for Q(α,n), where α is the probability of incorrectly rejecting the suspected outlier and n is the number of samples in the data set...The following table provides critical values for Q(α,n), where α is the probability of incorrectly rejecting the suspected outlier and n is the number of samples in the data set. There are several versions of Dixon’s Q-Test, each of which calculates a value for Q ij where i is the number of suspected outliers on one end of the data set and j is the number of suspected outliers on the opposite end of the data set.
