WMWodds()
Example 1. Replicate analysis of Newcombe (2006b)
Example 2. Data of Holmes and Williams (1954), used by Agresti (1980)
Example 3. Should "Super HDL" have been touted as effective?
Example 4. Statistical planning: Monte Carlo studies with WMWodds()
Example 5. A "congruent" plot: Using Q-scores to visualize WMWprob.
Example 6. Medians equal, yet WMWodds = 1.44, and 95% CI: [1.23, 1.68]
Example 7. Medians unequal, yet WMWodds = 1.0 & 95% CI: [0.794, 1.260]
Example 1. Replicate analysis of Newcombe (2006b)
Example 2. Data of Holmes and Williams (1954), used by Agresti (1980)
Example 3. Should "Super HDL" have been touted as effective?
Example 4. Statistical planning: Monte Carlo studies with WMWodds()
Example 5. A "congruent" plot: Using Q-scores to visualize WMWprob.
Example 6. Medians equal, yet WMWodds = 1.44, and 95% CI: [1.23, 1.68]
Example 7. Medians unequal, yet WMWodds = 1.0 & 95% CI: [0.794, 1.260]
Example 5. A "congruent" plot: Using Q-scores to visualize WMWprob
By
WMWodds()
Example 1. Replicate analysis of Newcombe (2006b)
Example 2. Data of Holmes and Williams (1954), used by Agresti (1980)
Example 3. Should "Super HDL" have been touted as effective?
Example 4. Statistical planning: Monte Carlo studies with WMWodds()
Example 5. A "congruent" plot: Using Q-scores to visualize WMWprob.
Example 6. Medians equal, yet WMWodds = 1.44, and 95% CI: [1.23, 1.68]
Example 7. Medians unequal, yet WMWodds = 1.0 & 95% CI: [0.794, 1.260]
Example 1. Replicate analysis of Newcombe (2006b)
Example 2. Data of Holmes and Williams (1954), used by Agresti (1980)
Example 3. Should "Super HDL" have been touted as effective?
Example 4. Statistical planning: Monte Carlo studies with WMWodds()
Example 5. A "congruent" plot: Using Q-scores to visualize WMWprob.
Example 6. Medians equal, yet WMWodds = 1.44, and 95% CI: [1.23, 1.68]
Example 7. Medians unequal, yet WMWodds = 1.0 & 95% CI: [0.794, 1.260]