Lecture 22: Two Probability Laws and Hypothesis Tests for Averages
by
UC Berkeley
/ Deborah Nolan
Lecture Description
This video lecture, part of the series Statistics 20, 003 - Introduction to Probability and Statistics by Prof. Deborah Nolan, does not currently have a detailed description and video lecture title. If you have watched this lecture and know what it is about, particularly what Mathematics topics are discussed, please help us by commenting on this video with your suggested description and title. Many thanks from,
- The CosmoLearning Team
- The CosmoLearning Team
Course Index
- Lecture 1: Collecting Data
- Lecture 2: The R Project for Statistical Computing
- Lecture 3: Distribution of Quantitative Variables
- Lecture 4: Variables and Functions in R
- Lecture 6: Body Mass Index, Numeric Summaries of Quantitative Data
- Lecture 7: Graphics Composition Overview
- Lecture 8: Scatter Plots
- Lecture 8: Time
- Lecture 9: Numerical Summaries of Data
- Lecture 10: Fisher's "Lady Tasting Tea" Experiment
- Lecture 11: Designs of Experiments
- Lecture 12: Design of Experiments
- Lecture 13: The Box Model
- Lecture 14: Informal Analysis of an Experiment
- Lecture 15: Deck of Cards
- Lecture 16: Conditional Probability
- Lecture 17: Pascal's & Fermat's Solutions
- Lecture 18: R and Probability
- Lecture 19: Hypothesis Testing
- Lecture 20: Roulette Wheel
- Lecture 21: 0-1 Boxes and Hypothesis Tests
- Lecture 22: Two Probability Laws and Hypothesis Tests for Averages
- Lecture 23: Central Limit Theorem
- Lecture 24: Hypothesis Tests for Averages
- Lecture 25: Survey Design
- Lecture 26: Project Overview
- Lecture 27: Sampling
- Lecture 28: Featured Plots
- Lecture 29: More on Sampling
- Lecture 30: Correlation and Regression
- Lecture 31: Fitting Lines to Data
- Lecture 33: More on Regression
- Lecture 34: Regression Concepts
- Lecture 35: Emails
- Lecture 36
Course Description
For students with mathematical background who wish to acquire basic concepts. Relative frequencies, discrete probability, random variables, expectation. Testing hypotheses. Estimation. Illustrations from various fields. Basic modules of the course:
1. Graphics, Descriptive Statistics, Exploratory Data Analysis
2. Experiments, Hypothesis Testing, Randomization
3. Probability, Random Variables, Simulation
4. Survey Sampling, Bootstrap, Confidence Intervals
5. Observational Studies, Regression, Modeling
2. Experiments, Hypothesis Testing, Randomization
3. Probability, Random Variables, Simulation
4. Survey Sampling, Bootstrap, Confidence Intervals
5. Observational Studies, Regression, Modeling
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