Genomic Data Analysis: Identifying Differentially Expressed Genes

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Describing Data: Statistics and Hypothesis Testing

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Estimating R: Multiple Testing and P-values

Explores the estimation of R in multiple testing scenarios and the challenges of P-value calculations.

Genomic Data Analysis: Microarray Technology

Explores microarray technology for genomic data analysis, covering Affymetrix GeneChip Probe Arrays, data preprocessing, normalization, differential expression analysis, and method comparisons.

Descriptive Statistics: Hypothesis Testing

Introduces descriptive statistics, hypothesis testing, p-values, and confidence intervals, emphasizing their importance in data analysis.

Hypothesis Testing: Q-Q Plots and Non-Parametric Tests

Covers hypothesis testing, Q-Q plots, and non-parametric tests in statistics.

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.

T-tests and Empirical Tests

Explores t-tests, z-tests, and empirical tests for sample comparison and hypothesis testing.

Introduction to t tests

Covers the basics of t tests, including hypothesis testing, significance levels, P-values, Z-tests, and the t distribution.

Z-Test: Hypothesis Testing

Introduces hypothesis testing, focusing on the Z-test and critical regions.