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Lecture
Understanding Statistics & Experimental Design
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Related lectures (31)
Estimators and Confidence Intervals
Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.
Sampling Theory: Statistics for Mathematicians
Covers the theory of sampling, focusing on statistics for mathematicians.
Linear Regression: Estimation and Inference
Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.
Two-Sample T-Test
Explains the two-sample t-test for comparing means of independent samples, including hypothesis testing steps and test statistic calculation.
Statistical Inference
Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.
Hypothesis Testing: State of Nature
Explores hypothesis testing, emphasizing the state of nature and the importance of choosing the most powerful test.
Understanding Statistics & Experimental Design
Explores unequal variances, replication, power, effect size, biases, and their impact on research outcomes.
Statistical Hypothesis Testing
Covers statistical hypothesis testing, likelihood estimation, and confidence intervals construction.
Interval Estimation
Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.
Distribution Theory of Least Squares
Explores the distribution theory of least squares estimators in a Gaussian linear model, focusing on precision and confidence intervals construction.
Red Influence: Attractiveness, Desirability, and Status
Explores the effects of red on attractiveness, desirability, and status, emphasizing statistical analysis and the challenges of replication and publication bias.
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