1. Welcome to Algebra 2! Get out your homework Get out catalogs Get out writing utensils Put bags on the floor Be quiet!!! 3/2/2016 2-7: Curve Fitting with Linear Models1

2. Curve Fitting with Linear Models Section 2-7 3/2/2016 2-7: Curve Fitting with Linear Models2

3. 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 3/2/2016 2-7: Curve Fitting with Linear Models3

4. Steps of Curve Fitting 1.Identify and list all of the data points 2.Put data into calculator – use STAT key 3.STAT  CALC  and select LinReg (ax + b) to get the equation 3/2/2016 2-7: Curve Fitting with Linear Models4

5. Example 1 The table shows the U.S. daily oil production y (in thousands of barrels x years) after 1994. a)Use the graphing calculator to find and graph the equation of the best-fitting line. a)Use the equation from part (a) to predict the daily oil production in 2009. 3/2/2016 2-7: Curve Fitting with Linear Models5 x012345678 y665065606470645062505880582058005750

6. Your Turn Use the graphing calculator to find and graph the equation of the best-fitting line. 3/2/2016 2-7: Curve Fitting with Linear Models6 x700074007800810085008800920095009800 y56.054.551.950.047.345.643.141.639.9

7. Assignment A2-2 P. 146: #1, 3, 5 + 9, 10, 11 3/2/2016 2-7: Curve Fitting with Linear Models7

8. Assignment A2-4 A2-6 P. 146: #1, 3, 5 3/2/2016 2-7: Curve Fitting with Linear Models8