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However, when I calculate the difference in probabilities for individuals by putting in values of 0 and 1 into the insurance variable and the mean value for wealth, the difference in undernutrition is only .04.That is calculated as follows: In layman's terms, the question is why doesn't insuring an individual change their probability of being under-nourished as much as the odds ratio indicates it does?For Discovery, Joseph Boyle and Michael Gara are executive producers and Greg Wolf is coordinating producer.Also Read: Discovery Closes Deal to Acquire Scripps, Tweaks Name of Newly Combined Company It's hard to believe it was once called The Learning Channel, as it looks like ever since they took a turn towards downscale reality shows, TLC and Discovery have nurtured stars with a knack for causing trouble.We can also convert between the odds and probabilities: $$ \text=\frac ~~~~~~~~~~~~~~~~ \text=\frac $$ (With these formulas it can be difficult to recognize that the odds is the LHS at top, and the probability is the RHS, but remember that it's the is just the odds of something divided by the odds of something else; in the context of logistic regression, each $\exp(\beta)$ is the ratio of the odds for successive values of the associated covariate when all else is held equal.What's important to recognize from all of these equations is that probabilities, odds, and odds ratios do not equate in any straightforward way; just because the probability goes up by .04 very much does imply that the odds or odds ratio should be anything like .04!It took me quite a while to solve; I'm not sure why is that not well-known formula. Suppose, there are 10 persons admitted to the university; 7 of them are men.

Would you like to answer one of these unanswered questions instead?

For any given set of values in your logistic regression model, there may be some point where $$ \exp(\beta_0 \beta_1x)-\exp(\beta_0 \beta_1x') =\frac-\frac $$ for some given $x$ and $x'$, but it will be unequal everywhere else.

(Although it was written in the context of a different question, my answer here contains a lot of information about logistic regression that may be helpful for you in understanding LR and related issues more fully.) Well the answer is simple when you are willing to keep all variables constant and vary one variable.

When the odds are presented this way, they're called "Las Vegas odds".

However in statistics, we typically divide through and say the odds are .8 instead (i.e., 4/5 = .8) for purposes of standardization.

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