DISTRIBUIÇÃO DE POISSON DE PROBABILIDADE # 02

POISSON PROBABILITY DISTRIBUTION In probability theory and statistics, the Poisson distribution is a discrete random variable probability distribution that expresses the probability of a series of events occurring in a given period of time if these events occur regardless of when the last event occurred. What is a Poisson Distribution? Learn how to calculate Definition of the Poisson Distribution Discrete Random Variables and Probability Distribution Poisson distribution pdf Poisson distribution solved exercises Poisson distribution excel Binomial distribution Poisson distribution calculator Poisson calculator Exponential distribution Poisson distribution graph -------------------------------------------------------------------------------------------------------------------------------------------------------- tags: Poisson distribution, Poisson Probability, Poisson probability, Poisson distribution, Poisson distribution examples, Poisson distribution, Poisson distribution probability distribution, Poisson distribution examples, Poisson probability example, Poisson distribution applications, Poisson distribution formula, Poisson probability, Poisson probability examples, Poisson distribution applications, Poisson probability exercises, Poisson statistics, Poisson distribution solved exercises. 1. Descriptive statistics and exploratory data analysis: graphs, diagrams, tables, descriptive measures (position, dispersion, skewness, and kurtosis). 2 Probability. 2.1 Basic definitions and axioms. 2.2 Conditional probability and independence. 2.3 Discrete and continuous random variables. 2.4 Probability distribution. 2.5 Likelihood function. 2.6 Probability density function. 2.7 Expected value and moments. 2.8 Special distributions. 2.9 Conditional distributions and independence. 2.10 Transformation of variables. 2.11 Laws of large numbers. 2.12 Central limit theorem. 2.13 Random samples. 2.14 Sampling distributions. 3 Statistical inference. 3.1 Point estimation: estimation methods, properties of estimators, sufficiency. 3.2 Interval estimation: confidence intervals, credibility intervals. 3.3 Hypothesis testing: simple and compound hypotheses, significance levels and test power, Student's t-test, chi-square test. 4 Linear regression analysis. 4.1 Least squares and maximum likelihood criteria. 4.2 Linear regression models. 4.3 Inference on model parameters. 4.4 Analysis of variance. 4.5 Residual analysis. 5 Sampling techniques: simple random, stratified, systematic, and cluster sampling. 5.1 Sample size.