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Welcome to Algebra 2!. Get out your homework Get out catalogs Get out writing utensils Put bags on the floor Be quiet!!!. Curve Fitting with Linear Models. Section 2-7. Definitions. Regression is the statistical study of the relationship between sets of data

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Presentation Transcript
welcome to algebra 2
Welcome to Algebra 2!
  • Get out your homework
  • Get out catalogs
  • Get out writing utensils
  • Put bags on the floor
  • Be quiet!!!

2-7: Curve Fitting with Linear Models

curve fitting with linear models

Curve Fitting with Linear Models

Section 2-7

2-7: Curve Fitting with Linear Models

definitions
Definitions
  • Regression is the statistical study of the relationship between sets of data
  • Scatterplot is a graph that helps understand the form, direction, and strength of the relation; individual points
  • Correlation is the strength and direction of the linear relationship between two variables
  • Correlation Coefficient is the measure of how well the data set is fit by through a model; represented by r
  • -1 ≤ r ≤ 1
  • Line of Best Fit is the line that best approximates a set of data

2-7: Curve Fitting with Linear Models

steps of curve fitting
Steps of Curve Fitting
  • Identify and list all of the data points
  • Put data into calculator – use STAT key
  • STAT  CALC  and select LinReg (ax + b) to get the equation

2-7: Curve Fitting with Linear Models

example 1
Example 1

The table shows the U.S. daily oil production y (in thousands of barrels x years) after 1994.

  • Use the graphing calculator to find and graph the equation of the best-fitting line.
  • Use the equation from part (a) to predict the daily oil production in 2009.

2-7: Curve Fitting with Linear Models

your turn
Your Turn

Use the graphing calculator to find and graph the equation of the best-fitting line.

2-7: Curve Fitting with Linear Models

assignment a2 2
Assignment A2-2
  • P. 146: #1, 3, 5 + 9, 10, 11

2-7: Curve Fitting with Linear Models

assignment a2 4 a2 6
Assignment A2-4 A2-6
  • P. 146: #1, 3, 5

2-7: Curve Fitting with Linear Models

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