Probability and Statistics: Basics and Applications

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Related lectures (32)

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.

Estimators and Confidence Intervals

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

Central Limit Theorem: Properties and Applications

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

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