Probability and Statistics: Data Modeling and Analysis

Related lectures (31)

Page 1 of 4

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Covers the normal distribution, including its properties and calculations.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Explores variance, covariance, and practical applications in statistics and probability.

Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Covers properties and calculations related to the normal distribution, including probabilities and quantiles.

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.