A distribution is a way of representing the possible outcomes or values of a variable and how frequently each outcome occurs. It can be shown as a graph, table, or mathematical formula.
A discrete distribution is one where the variable can only take on a finite or countable number of values, while a continuous distribution is one where the variable can take on any value within a certain range. For example, the number of children in a family is discrete, while the height of a person is continuous.
A statistical model is a simplified representation of a real-world phenomenon or process. It uses mathematical equations and statistical techniques to describe the relationships between variables and make predictions about future outcomes.
Fitting a distribution to data involves finding the best distribution that describes the data and its underlying patterns. This can help with understanding the data, making predictions, and identifying any outliers or unusual values.
The Central Limit Theorem states that the sum of a large number of independent, identically distributed variables will tend towards a normal distribution, regardless of the underlying distribution of the individual variables. This is important in statistics as it allows us to make inferences about a population based on a sample of data.