Statistics 21 - Introductory Probability and Statistics for Business
Statistics 21: Introductory Probability and Statistics for Business (Fall 2009, UC Berkeley). Statistics 21 is a service course designed primarily for
Business students. It is not very mathematical, but you need to be comfortable with math at the level of high-school algebra. Taught by Professor Philip B. Stark,
this course covers topics: reasoning and fallacies, descriptive statistics, association, correlation, regression, elements of probability, set theory,
propositional logic, chance variability, random variables, expectation, standard error, sampling, hypothesis tests, confidence intervals, experiments and
observational studies, as well as common techniques of presenting data in misleading ways.
| Lecture 01 - Introduction, Reasoning and Fallacies |
| Lecture 02 - Reasoning and Fallacies |
| Lecture 03 - Data: Types of Data, Displaying Data, Measures of Location |
| Lecture 04 - Measures of spread or variability, Multivariate Data and Scatterplots |
| Lecture 05 - Association, Correlation, Computing the Correlation Coefficient |
| Lecture 06 - Regression, Regression Diagnostics |
| Lecture 07 - Errors in Regression, Counting, Permutations |
| Lecture 08 - Combinations, Card Hands |
| Lecture 09 - Probability: Philosophy and Mathematical Background |
| Lecture 10 - Review |
| Lecture 11 - Set Theory: The Language of Probability |
| Lecture 12 - Probability: Axioms and Fundaments |
| Lecture 13 - Propositional Logic |
| Lecture 14 - The "Let's Make a Deal" (Monty Hall) Problem |
| Lecture 15 - Probability Meets Data |
| Lecture 16 - Random Variables and Discrete Distributions |
| Lecture 17 - The Long Run and the Expected Value |
| Lecture 18 - Standard Error |
| Lecture 19 - The Norman Approximation, Markov's and Chebyshev's Inequalities for Random Variables |
| Lecture 20 - Sampling |
| Lecture 21 - Estimating Parameters from Simple Random Samples |
| Lecture 22 - Confidence Intervals |
| Lecture 23 - Hypothesis Testing: Does Chance Explain the Results? |
| Lecture 24 - Does Treatment Have and Effect? |
| Lecture 25 - Review |