Probability and Statistics

The Bayes’ theorem is a probability and statistics mathematical theory named after Reverend Thomas Bayes. It helps estimate the likelihood of an occurrence based on the probability of a previous event, for instance, a natural catastrophe. The Bayes’ theorem has several uses, including Bayesian inference in the healthcare business – predicting the chance of developing […]

This article briefly discusses the Bernoulli trial, Binomial distribution, and Bernoulli distribution. The first step is to familiarise ourselves with the idea and operation of the Bernoulli trials. Because Bernoulli trials have just two possible outcomes, success or failure, they are sometimes called binomial tests. On the other hand, a binomial distribution results in many

What exactly is the Binomial Distribution, and how does it function? The binomial distribution, which predicts the odds of obtaining one of two outcomes given a set of parameters, is an example of a typical probability distribution. It is a summary of the number of trials that were done with the same chance of attaining

In probability and statistics theory, the binomial distribution can be defined as the probability which has a discrete nature and applies to events that have only 2 possible results within the experiment either failure or success. In binomial the prefix refers to twice or two. A few situations where instances of the binomial distribution are

Data Caselets-based problems appear practically in  Data Interpretation parts of all competitive tests. Given the significance of this question type, you must understand the principles of Data Caselets. The following study material notes on caselets shall help you in the same.  A Caselet is a series of information presented in paragraph form. There are no

The Cumulative Distribution Function (CDF) of a random variable with real-valued X, assessed at x, is the probability function that X will assume a value less than or equal to x. It is used to describe in a table the probability distribution of random variables. And with this data, we can simply generate a CDF

Statistics classifies events into dependent and independent events. According to basic logic, the existence or absence of one event can provide clues to subsequent events. Discover the difference between dependent and separate events. A dependent event provides information about another event. Unless it provides information about other events, an event is considered independent. It is

Introduction Events can be defined as a set of particular outcomes for any given random experiment being conducted. Collecting a set of all possible outcomes while performing a random experiment is called a sample space. The sample space can indicate all the possible outcomes of an experiment. So, in a way, events and sample space

A random variable’s probability distribution represents the distribution of probabilities across its values. The probability distribution of a discrete random variable, x, is characterised by the probability mass function, indicated by f. (x). This function reports the chance that each random variable’s value is true. The probability function for a discrete random variable must meet

A group of outcomes of a random experiment can be defined as an event in probability. All possible results of an experiment are represented by the sample space. As a result, in probability, occurrences can be thought of as subsets of the sample space. Probability events are the results of random experimentation. In probability, any