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Lecture
Understanding Statistics & Experimental Design
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Related lectures (28)
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Estimators and Confidence Intervals
Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.
Air Pollution Analysis
Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.
Understanding Statistics & Experimental Design
Provides an overview of basic probability theory, ANOVA, t-tests, central limit theorem, metrics, confidence intervals, and non-parametric tests.
Confidence Intervals and Hypothesis Testing
Explores confidence intervals, hypothesis testing, and decision-making using test statistics and p-values.
Describing Data: Statistics and Hypothesis Testing
Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.
Hypothesis Testing and Confidence Intervals: Key Concepts
Provides an overview of hypothesis testing and confidence intervals in statistics, including practical examples and key concepts.
Fracture Mechanics: Crack Growth and Weakest Link
Explores fracture mechanics, crack growth, and the weakest link theory, emphasizing the statistical distribution of crack sizes and the significance of the largest crack in material failure.
Sampling Distributions: Theory and Applications
Explores sampling distributions, estimators' properties, and statistical measures for data science applications.
Sampling Distributions: Theory and Applications
Explores sampling theory, limiting distributions, and estimation for statistical analysis.
Probability Distributions: Central Limit Theorem and Applications
Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.
