Probability and Statistics: Basics and Applications

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 3 of 4

Probability and Statistics

Explores joint random variables, conditional density, and independence in probability and statistics.

Testing: t-tests

Covers t-tests, p-values calculation, and comparison of coefficients.

Statistical Significance: Maximum Likelihood Estimation and Confidence Intervals

Explores type I and type II errors, critical values, and confidence intervals in statistical significance.

Continuous Random Variables

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

Probabilities and Statistics: Key Theorems and Applications

Discusses key statistical concepts, including sampling dangers, inequalities, and the Central Limit Theorem, with practical examples and applications.

Statistical Hypothesis Testing: Unilateral and Bilateral Pairs

Explores unilateral and bilateral pairs in statistical hypothesis testing, covering critical values, test statistics, and p-values.

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Data Analysis: Correlation Measures

Covers the basics of data analysis, focusing on statistical concepts and correlation measures.

Probability and Statistics: Data Modeling and Analysis

Explores PDF forms, statistics, boxplots, density curves, and data analysis methods.

Generalized Linear Models: A Brief Review

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.