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The logarithm of the ratio of frequencies of two different categoric and mutually exclusive outcomes such as healthy and sick.
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Table 2 shows the logit regression results where the dependent variables are the log odds of a household having a mortgage and the log odds of a household having a home equity loan.
Per unit increase in USC, the log odds of laptop ownership increases by .12 or 12%; per unit increase in AFF, the log odds of tablet ownership increases by .19 or 19%.
The log odds are the logarithm of the odds ratio, in other words, the coefficient (normalized by the standard error).
The above equation depicts the log odds of student i being in category j on the dependent variable, relative to [J.sub.1], which is the reference category of stopped out.
On the difference scale for log odds ratios, the total effect can be defined as the sum of NDE and NIE and the proportion mediated is then NIE/NDE + NIE.
We calculated Spearman's rank correlation between the area-adjusted frequency of predicted log odds ratios and the bin rank of ordered log odds ratios with leave 1-out cross validation.
To ensure this approach as appropriate, the linearity assumption was first confirmed by checking the plot between the log odds of survival and plasma HMGB-1 levels.
where logit is the estimated log odds of Y =1, P is the estimated probability of Y = 1, [X.sub.i] is the ith predictor entered into the models, and i is the coefficient associated with the ith predictor for i = 1, ..., k.
Thinking of odds is easier than to thinking about log odds. Using odds, the logistic regression equation can be written as under:
Next we explore the analytical properties of the PHLD, deriving its moments, median, quantiles, hazard function, and log odds function, and summarize them below.
The table also lists log odds estimates ranked from highest to lowest in magnitude by absolute value.