Model Selection in Statistics

Related lectures (31)

Page 3 of 4

Explains statistical methods in quantitative analysis, emphasizing precision, accuracy, and sample representation.

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Explains the Law of Large Numbers and its application to random variables.

Explains how constants affect the mean and variance of random numbers.

Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.

Provides an overview of hypothesis testing and confidence intervals in statistics, including practical examples and key concepts.

Introduces the t-test for assessing categorical effects on quantitative outcomes, covering hypothesis testing, assumptions, and alternative tests.

Covers the process of making pizza, sampling, averages, dispersion, residuals, and normal distribution.

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.