Statistics for Data Science: Introduction to Statistical Methods

Related lectures (30)

Page 2 of 3

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

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

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Explores estimating parameters through confidence intervals in linear regression and statistics.

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

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

Covers the concept of property testing using statistical methods.

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.

Explores linear regression analysis of ozone data using statistical models.