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Normality?

Compare one or two populations. Experimental Design?. Independent Samples. Blocked Samples. Normality?. Non-normal distribution. Normal distribution. Number of populations?. Number of populations?. Compare two ore more populations. see Page 2. Kruskal-Wallis Test. Kruskal-Wallis Test.

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Normality?

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  1. Compare one or two populations Experimental Design? Independent Samples Blocked Samples Normality? Non-normal distribution Normal distribution Number of populations? Number of populations? Compare two ore more populations see Page 2 Kruskal-Wallis Test Kruskal-Wallis Test Wilcoxon Rank Sum Test Wilcoxon Signed Rank Sum Test • Reminder: Ordinal data will be treated like non-normal distributions: • Independent samples: Wilcoxon Rank Sum Test • Blocked Samples: Sign Test • More than two populations: Kruskal-Wallis Test Wilcoxon Rank Sum Test Wilcoxon Signed Rank Sum Test Page

  2. Number of populations? One or two populations Two or more populations see Page 4 Compare two or more populations Interval Nominal Experimental Design? see Page 3 Independent Samples Blocked Samples One-way analysis of variance Randomized block analysis (two-way) of variance Randomized block analysis of variance Anova: Single Factor Anova: Two-Factor Without Replication Anova: Two-Factor With Replication Page

  3. Number of categories? More than two categories Two categories Chi-squared of a contigency table Chi-squared of a contigency table Nominal Contingency Table Contingency Table Page

  4. Interval Nominal Type of descriptive measurement? Number of categories? see Page 5 see Page 6 One or two populations Describe a population Problem objective? Compare two populations Data Type? Data Type? Interval Nominal Type of descriptive measurement? Number of categories? see Page 7 see Page 8 Page

  5. Interval Type of descriptive Measurement? Central Location Variability t test and estimator of test and estimator of t-test: Mean t-estimate: Mean Chi-squared Test: Variance Chi-squared Estimate: Variance Page

  6. Nominal Number of categories? Two categories More than two categories Chi-squared goodness of fit Chi-squared goodness of fit z test and estimator of p z-test: Proportion z-Estimate: Proportion Page

  7. F test and estimator of F-Test Two-Sample for Variances F-Estimate of the Ratio of Two Variances t test and estimator of t test and estimator of (Equal-Variances) t test and estimator of (Unequal-variances) t-Test: Paired Two Sample for Means t-Estimate: Mean Interval Type of descriptive Measurement? Central Location Variability Independent Samples Experimental Design? Matched Pairs Population Variances? Unequal Equal T-Test: Two Sample Assuming Unequal Variances t-Test: Two Sample Assuming Equal Variances Page

  8. Nominal Number of categories? More than two categories Two categories chi-squared goodness of fit Chi-squared of a contigency table Contingency Table z test and estimator of p1 – p2 z-test: 2 Proportions z-Estimate: 2 Proportions Page

  9. More than two categories Two categories z test of p or the chi-squared goodness of fit Chi-squared goodness of fit Number of categories? Number of categories? More than two categories More than two categories Two categories Two categories z test of p or the chi-squared goodness of fit Chi-squared of a contigency table Chi-squared of a contigency table Chi-squared of a contigency table Problem Objective? Compare populations Describe a population Number of categories? Number of populations? Two populations Two or more populations

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