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The name Log5 is due to Bill James [1] but the method of using odds ratios in this way dates back much farther. This is in effect a logistic rating model and is therefore equivalent to the Bradley–Terry model used for paired comparisons , the Elo rating system used in chess and the Rasch model used in the analysis of categorical data.
The sample odds ratio n 11 n 00 / n 10 n 01 is easy to calculate, and for moderate and large samples performs well as an estimator of the population odds ratio. When one or more of the cells in the contingency table can have a small value, the sample odds ratio can be biased and exhibit high variance .
The log diagnostic odds ratio can also be used to study the trade-off between sensitivity and specificity [5] [6] by expressing the log diagnostic odds ratio in terms of the logit of the true positive rate (sensitivity) and false positive rate (1 − specificity), and by additionally constructing a measure, :
The wins ratio or odds of winning is the ratio of the team's wins against the league to its losses against the league. [James did not seem aware at the time that his quality measure was expressible in terms of the wins ratio. Since in the quality model any constant factor in a quality measure eventually cancels, the quality measure is today ...
This exponential relationship provides an interpretation for : The odds multiply by for every 1-unit increase in x. [22] For a binary independent variable the odds ratio is defined as where a, b, c and d are cells in a 2×2 contingency table. [23]
In probability theory and computer science, a log probability is simply a logarithm of a probability. [1] The use of log probabilities means representing probabilities on a logarithmic scale (,], instead of the standard [,] unit interval.
Your debt-to-income ratio (DTI) is your total monthly debt payments divided by your total gross monthly income. It helps lenders determine your approval odds and the likelihood of you being able ...
These CI are what's often used in epidemiology for calculation the CI for relative-risk and odds-ratio. [54] The way it is done there is that we have two approximately Normal distributions (e.g., p1 and p2, for RR), and we wish to calculate their ratio. [b]