1 / 10

CHAPTER nine

CHAPTER nine. Correlation and Regression. Section 9.1. Correlation. Definitions. Correlation is a relationship between 2 variables. Data is often represented by ordered pairs (x, y) and graphed on a scatter plot X is the independent variable Y is the dependent variable.

garry
Télécharger la présentation

CHAPTER nine

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CHAPTER nine Correlation and Regression

  2. Section 9.1 Correlation

  3. Definitions • Correlation is a relationship between 2 variables. • Data is often represented by ordered pairs (x, y) and graphed on a scatter plot • X is the independent variable • Y is the dependent variable

  4. Types of correlation:

  5. CORRELATION COEFFICIENT • A numerical measure of the strength and direction of a linear relationship between 2 variables x and y. • -1 < r < 1 The closer to -1 or 1, the stronger the linear correlation. The closer to 0, the weaker the linear correlation.

  6. Section 9.2 Linear regression

  7. Regression Line The line whose equation best fits the data in a scatter plot. We can use the equation to predict the value y for a given value of x. Recall: basic form of a line is y = mx + b We’ll use this form, but calculate m and b differently…

  8. Finding m and b

  9. Find the equation of the regression line • 18. The square footages and sale prices (in thousands of dollars) of seven homes. Use the line of regression to predict the sale price of a home when x = 1450 sq feet.

More Related