Probability
- the estimation of the likelihood of an event outcome will occur
- represented as a percentage or decimal point
- Factors may influence probability
Probability Distributions
- describe how values of a random variable are distributed, providing a mathematical framework for understanding uncertainty in data
- these distributions are fundamental in statistical analysis, enabling
- predictions
- hypothesis testing
- decision-making
Discrete Data
- can take only specified values
Binomial Distribution
- Describes variables with 2 possible outcomes
- the probability distributions of the number of successes in n trials with probability of success
- Describes events that have equal probabilities
Poisson Distribution
- Describes count data
- gives the probability of an event happening a certain number of times within a given interval of time / space
Continuous Data
- can take any value within a given range
- the range can be finite or infinite
Normal Distribution
- Describes data with values that become less probable that farther they are from the average (mean)
- bell-shaped probability density function
- Describes data for which equal-sized intervals (space between data) have equal probability
Log-Normal Distribution
- Describes right-skewed data, which is the probability distribution of a random variable whose logarithm is normally distributed
Exponential Distribution
- Describes data that has higher probabilities for small values then large values
- The probability distribution of time between independent events