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Lecture
Confidence Intervals and T-Test
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Related lectures (32)
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Understanding Statistics & Experimental Design
Provides an overview of basic probability theory, ANOVA, t-tests, central limit theorem, metrics, confidence intervals, and non-parametric tests.
Describing Data: Statistics and Hypothesis Testing
Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.
Statistics & Experimental Design
Explores conditional probability, Framingham studies, effect size, t-test, and sampling error in statistics.
Z-Test: Hypothesis Testing
Introduces hypothesis testing, focusing on the Z-test and critical regions.
Maximum Likelihood Estimation: Multivariate Statistics
Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.
Statistical Tests: T-Tests and ANOVA
Covers the calculation of paired t-tests, advantages/disadvantages of different t-tests, and the concept of ANOVA.
Statistics: Hypothesis Testing & Confidence Intervals
Covers hypothesis testing, confidence intervals, data distributions, and statistical significance in data analysis.
Chi-Square Test: Independence Hypothesis
Explains the Chi-Square test for independence hypothesis and its practical applications.
ANOVA: Partitioning Total SS
Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.
Confidence Intervals and Hypothesis Testing
Explores confidence intervals, hypothesis testing, and ROC curves in statistical analysis.
