Lectures related to Proposal Review Process

Proposal Review Process: Insights from Deplancke

Delves into the proposal review process, red flags, and resubmission tips.

Hypothesis Testing: Neyman-Pearson Framework

On hypothesis testing explores the Neyman-Pearson framework, test functions, errors, and likelihood ratio tests.

Decision Theory: Risk and Hypothesis Testing

Covers decision theory, risk functions, and hypothesis testing in statistical inference.

Explores hypothesis testing in statistics, focusing on decision-making based on sample data and controlling error probabilities.

Hypothesis Testing: Neyman-Pearson Framework

Covers hypothesis testing, types of errors, Neyman-Pearson framework, and one-sided Gaussian tests.

Generalization Error

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

Bayesian Inference: Optimal Decisions

Explores Bayesian inference for optimal decision-making in hypothesis testing scenarios.

Describing Data: Statistics and Hypothesis Testing

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

Understanding Statistics & Experimental Design

Explores unequal variances, replication, power, effect size, biases, and their impact on research outcomes.

Statistics: Hypothesis Testing & Confidence Intervals

Covers hypothesis testing, confidence intervals, data distributions, and statistical significance in data analysis.

Hypothesis Testing and Confidence Intervals: An Overview

Covers hypothesis testing, confidence intervals, and their applications in statistics.

Statistical Hypothesis Testing: Inference and Interpretation

Explores statistical hypothesis testing, including constructing confidence intervals, interpreting p-values, and making decisions based on significance levels.

Hypothesis Testing

Covers hypothesis testing, Neyman-Pearson lemma, ROC curves, and optimal tests with examples and simulations.

Testing t-tests MOOC: Introduction to Discrete Choice Models

Introduces t-tests, focusing on hypothesis testing and comparing coefficients using the t-distribution.

Likelihood Ratio Test: Hypothesis Testing

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Statistics Essentials: The t-test

Introduces the t-test for assessing categorical effects on quantitative outcomes, covering hypothesis testing, assumptions, and alternative tests.

Statistical Hypothesis Testing: Unilateral and Bilateral Pairs

Explores unilateral and bilateral pairs in statistical hypothesis testing, covering critical values, test statistics, and p-values.

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.

Experimental Design in Biostatistics Log in to Mediaspace to watch this video

Introduces experimental design in biostatistics, covering research process, hypothesis testing, ANOVA modeling, and interpretation of results.

Quantile Regression: Linear Optimization Log in to Mediaspace to watch this video

Covers quantile regression, focusing on linear optimization for predicting outputs and discussing sensitivity to outliers, problem formulation, and practical implementation.