Continuous Variable: can take on any value between two specified values. Obtained by measuring. Discrete Variable: not continuous variable (cannot take on any value between two specified values).
A continuous random variable X follows a normal distribution, denoted as $X \sim \mathcal{N}(\mu,,\sigma^{2})$. The normal distribution is characterized by its bell ...
The binomial distribution models the number of successes in a fixed number of independent Bernoulli trials, each with the same probability of success. The chi-square distribution is often used in ...
You have been introduced to one example of a discrete random variable, the binomial random variable. Recall that a discrete random variable can only assume a finite number of values. For example, the ...
The normal distribution is a continuous probability distribution that is symmetrical, bell-shaped, and centred around its mean. It is one of the most important distributions in statistics because many ...
The main property of a discrete joint probability distribution can be stated as the sum of all non-zero probabilities is 1. The next line shows this as a formula. The marginal distribution of X can be ...
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