Lectures related to Sampling: Inference and Statistics

Hypothesis Testing and Confidence Intervals: An Overview

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

Hypothesis Testing and Confidence Intervals: Key Concepts

Provides an overview of hypothesis testing and confidence intervals in statistics, including practical examples and key concepts.

Statistics & Experimental Design

Explores conditional probability, Framingham studies, effect size, t-test, and sampling error 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.

Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Sampling strategies MOOC: Selected Topics on Discrete Choice

Explores research process, variable types, causality vs correlation, and sampling strategies.

Sampling Theory: Statistics for Mathematicians

Covers the theory of sampling, focusing on statistics for mathematicians.

Sampling Theory: Statistics and Inference

Covers sampling theory, statistics, and inference, focusing on the sampling distribution of statistics.

Concentration Inequalities

Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.

Understanding Statistics & Experimental Design

Covers basic probability theory, signal detection theory, statistics, and meta-statistics, explaining effect sizes, power, and hypothesis testing.

Understanding Statistics & Experimental Design

Explores t-tests, confidence intervals, ANOVA, and hypothesis testing in statistics, emphasizing the importance of avoiding false discoveries and understanding the logic behind statistical tests.

Understanding Statistics & Experimental Design

Provides an overview of basic probability theory, ANOVA, t-tests, central limit theorem, metrics, confidence intervals, and non-parametric tests.

Sampling: Inference and Statistics Log in to Mediaspace to watch this video

Explores sampling, inferential statistics, and effective experimentation in statistics.

Pizza Making Process Log in to Mediaspace to watch this video

Covers the process of making pizza, sampling, averages, dispersion, residuals, and normal distribution.

Review Session: Module 1 Log in to Mediaspace to watch this video

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Basics of Linear Regression Log in to Mediaspace to watch this video

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Normal Distribution: Characteristics and Z-scores Log in to Mediaspace to watch this video

Explores normal distribution characteristics, Z-scores, probability in inferential statistics, sample effects, and binomial distribution approximation.

T-tests and Empirical Tests Log in to Mediaspace to watch this video

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

Statistical Analysis: Data Exploration and Inference Log in to Mediaspace to watch this video

Covers statistical analysis, emphasizing data exploration and inference to quantify uncertainty and draw conclusions.

Exploratory Statistics: Data Analysis Fundamentals Log in to Mediaspace to watch this video

Covers the basics of exploratory statistics, including variables, quantiles, central tendency, dispersion, outliers, and robustness.