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Lecture
Understanding Statistics & Experimental Design
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Related lectures (30)
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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.
Understanding Statistics & Experimental Design
Covers basic probability theory, signal detection theory, statistics, and meta-statistics, explaining effect sizes, power, and hypothesis testing.
Variance and Covariance: Properties and Examples
Explores variance, covariance, and practical applications in statistics and probability.
Elements of Statistics: Probability, Distributions, and Estimation
Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.
Signal Detection Theory
Explores Signal Detection Theory and its impact on decision-making processes in statistics and experimental design.
Comparison Statistics: Hypothesis Testing and ANOVA
Explores comparison statistics, hypothesis testing, ANOVA, correlation, and linear regression.
Probability and Statistics
Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.
Describing Data: Statistics and Hypothesis Testing
Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.
Statistical Analysis: Boxplot and Normal Distribution
Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.
Statistics for Data Science: Introduction to Statistical Methods
Covers the fundamental concepts of statistics and their application in data science.
