Lectures (Video)

- 1. Sampling and Data
- 2. Descriptive Statistics
- 3. Probability Topics
- 4. Discrete Distributions
- 5. Continuous Random Variables
- 6. The Normal Distribution
- 7. The Central Limit Theorem
- 8. Confidence Intervals
- 9. Hypothesis Testing - Single Mean and Single Proportion
- 10. Hypothesis Testing - Two Means, Two Proportions, Paired Data
- 11. The Chi-Square Distribution
- 12. Linear Regression and Correlation

## Introduction to Statistics II - Lecture 12

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Lecture 12 - Linear Regression and Correlation
Professionals often want to know how two or more variables are related. For example, is there a relationship between the grade on the second math exam a student takes and the grade on the final exam? If there is a relationship, what is it and how strong is the relationship? In another example, your income may be determined by your education, your profession, your years of experience, and your ability. The amount you pay a repair person for labor is often determined by an initial amount plus an hourly fee. These are all examples in which regression can be used. The type of data described in the examples is bivariate data - "bi" for two variables. In reality, statisticians use multivariate data, meaning many variables. This lecture covers the simplest form of regression, "linear regression" with one independent variable. This involves data that fits a line in two dimensions. You will also study correlation which measures how strong the relationship is.
Dr. Barbara Illowsky, Susan Dean
Collaborative Statistics (Connexions) http://cnx.org Date accessed: 2009-01-17 License: Creative Commons Attribution 2.0 |