Syllanova

Distribution

AMA1110 · Chapter overview

Distribution

Introduces probability distributions, binomial and Poisson count models, the normal distribution, and normal approximation to the binomial distribution.

Lessons

0 / 0
1

Random Variables, PMF, PDF, CDF, Mean and Variance

Distinguishes discrete and continuous random variables, PMFs, PDFs, and CDFs, then calculates expectation and variance.

FinalComing soon
Coming soon
2

Binomial Distribution

Recognizes binomial settings, translates a context into n, p, and X, and calculates exact binomial probabilities.

ImportantComing soon
Coming soon
3

Poisson Distribution

Recognizes Poisson count models, converts rates to the required interval, and calculates exact Poisson probabilities.

OverviewComing soon
Coming soon
4

Normal Distribution and Normal Approximation to the Binomial

Standardizes normal variables, reads tail probabilities, and approximates eligible binomial distributions using continuity correction.

ImportantComing soon
Coming soon
5

Sampling Distributions, the Central Limit Theorem, and Sample Variance

Explains sampling distributions of the sample mean and sample proportion, when to use the CLT, and how to calculate sample variance.

OverviewComing soon
Coming soon

Basic Mathematics I · AMA1110