Event probability is the chance that a specific outcome or event occurs. The opposite of an event is a nonevent. Event probability is also called predicted probability. The event probability estimates the likelihood of an event occurring, such as drawing an ace from a deck of cards or manufacturing a non-conforming part. The probability of an event ranges from 0 (impossible) to 1 (certain).
Each performance in an experiment is called a trial. For example, if you flip a coin 10 times and record the number of heads, you perform 10 trials of the experiment. If the trials are independent and equally likely, you can estimate the event probability by dividing the number of events by the total number of trials. For example, if you flip 6 heads out of 10 coin tosses, the estimated probability of the event (flipping heads) is:
Number of events ÷ Number of trials = 6 ÷ 10 = 0.6
A cumulative event probability estimates the likelihood of a set of events occurring (for example, the probability of rolling 4 or less on a die, which is the summation of the probability of rolling a 1, 2, 3, and 4).
In binary logistic regression, a response variable has only two possible values, such as the presence or absence of a specific disease. You can enter binary response data in Minitab by indicating columns for the number of events and the number of trials. The event probability is the likelihood that the response for a specific factor or covariate pattern is 1 or an event (for example, the likelihood that a woman older than 50 will develop type-2 diabetes).
In ordinal and nominal logistic regression, a response variable can have three or more categories. The event probability is the likelihood that a specific factor or covariate pattern has a specific response category. Cumulative event probability is the likelihood that the response for a specific factor or covariate pattern falls into category k or below, for each possible k, where k equals the response categories, 1…k.