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In probability theory, odds provide a measure of the probability of a particular outcome. Odds are commonly used in gambling and statistics.For example for an event that is 40% probable, one could say that the odds are "2 in 5", "2 to 3 in favor", or "3 to 2 against".
Decimal odds are a single value, greater than 1, representing the amount to be paid out for each unit bet. For example, a bet of £40 at 6 − 4 (fractional odds) will pay out £40 + £60 = £100. The equivalent decimal odds are 2.5; £40 × 2.5 = £100. We can convert fractional to decimal odds by the formula D = (b + a) ⁄ b.
For a typical second-order reaction with rate equation = [] [], if the concentration of reactant B is constant then = [] [] = ′ [], where the pseudo–first-order rate constant ′ = []. The second-order rate equation has been reduced to a pseudo–first-order rate equation, which makes the treatment to obtain an integrated rate equation much ...
An odds ratio (OR) is a statistic that quantifies the strength of the association between two events, A and B. The odds ratio is defined as the ratio of the odds of event A taking place in the presence of B, and the odds of A in the absence of B. Due to symmetry, odds ratio reciprocally calculates the ratio of the odds of B occurring in the presence of A, and the odds of B in the absence of A.
An alternative method of calculating the odds is to note that the probability of the first ball corresponding to one of the six chosen is 6/49; the probability of the second ball corresponding to one of the remaining five chosen is 5/48; and so on. This yields a final formula of
In mathematics, a rate is the quotient of two quantities, often represented as a fraction. [1] If the divisor (or fraction denominator) in the rate is equal to one expressed as a single unit, and if it is assumed that this quantity can be changed systematically (i.e., is an independent variable), then the dividend (the fraction numerator) of the rate expresses the corresponding rate of change ...
The odds strategy is optimal, that is, it maximizes the probability of stopping on the last 1. The win probability of the odds strategy equals w = Q s R s {\displaystyle w=Q_{s}R_{s}} If R s ≥ 1 {\displaystyle R_{s}\geq 1} , the win probability w {\displaystyle w} is always at least 1/ e = 0.367879... , and this lower bound is best possible .
In other words, the difference between the logarithm of the odds of having poor or fair health minus the logarithm of odds of having poor health is the same regardless of x; similarly, the logarithm of the odds of having poor, fair, or good health minus the logarithm of odds of having poor or fair health is the same regardless of x; etc. [2]