The posterior probability in Bayesian statistics is the revised or updated probability of an event occurring after new information has been taken into account.
The posterior probability is calculated by updating the prior probability using Bayes’ theorem.
In statistical terms, the posterior probability is the probability that event A will occur given that event B occurred.
The chi-square statistic (χ2) is a measure of the difference between the observed and expected frequencies of the outcomes of a set of events or variables.
Non-linearity is a mathematical term that describes a situation where the relationship between the independent variable and the dependent variable cannot be predicted along a straight line.
The Poisson distribution, named after the French mathematician Siméon Denis Poisson, can be used to estimate how many times an event can occur within “X” time periods.
Unconditional probability reflects the probability that some event will occur without taking into account any other possible influences or previous results.
The 90/10 retirement investment strategy involves investing 90% of investment capital in low-cost S&P 500 index funds, and the remaining 10% in short-term government bonds.
A ballpark figure is a rough estimate of what something might mean in numerical terms when a more precise number is estimated, such as the cost of a product.