A cumulative distribution function, \(F(x)\), gives the probability that the random variable \(X\) is less than or equal to \(x\): $$ P(X ≤ x) $$ By analogy, this concept is very similar to the cumulative relative frequency.

Unconditional Probability Unconditional probability (also known as marginal probability) is simply the probability that the occurrence of an event does not, in any way, depend on any other preceding events. In other words, unconditional probabilities are not conditioned on the…

Defining properties of a probability refers to the rules that constitute any given probability. These are:

Odds for and against an event represent a ratio of the desired outcomes versus the field. In other words, the odds for an event are the ratio of the number of ways the event can occur to the number of…

Probability is a measure of the likelihood that something will happen. We usually express probabilities as percentages, from 0% (impossible to happen) to 100% (guaranteed). We can express almost any event as a probability. For instance, we can gauge the…

Covariance Covariance is a measure of how two variables move together. The sample covariance of X and Y is calculated as follows: $$ \mathrm{S}_{\mathrm{XY}}=\frac{\sum_{\mathrm{i}=1}^{\mathrm{N}}\left(\mathrm{X}_{\mathrm{i}}-\overline{\mathrm{X}}\right)\left(\mathrm{Y}_{\mathrm{i}}-\overline{\mathrm{Y}}\right)}{\mathrm{n}-1} $$ A major drawback of covariance is that it is difficult to interpret since its value…