Linear Regression: Theory and Applications

Related lectures (29)

Page 3 of 3

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Covers conditional density, independence of random variables, expectation, and variance calculation.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

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

Covers conditional probability distributions and introduces the concept of conditional expected value.

Explores continuous random variables, density functions, and distribution laws with practical examples.

Covers discrete and continuous probability distributions, including joint and conditional probabilities.