1. Unit 3 Sections 9.2 – Day 1

2.  After we determine that a relationship between two variables exists (correlation), the next step is determine the line that best fits our data.  This line is called a regression line.  Sometimes called a line of best fit.  Regression lines allow us to predict the value of y, given a value of x. Section 9.2

3.  The points on the line serve as the “predicted values” for our set of data.  The data points are known as our “observed values.”  The differences between these predicted values and the observed values are known as residuals.  The line of best fit is determined as the line where the sum of the squares of the residuals is a minimum.

4. Section 9.2

5. Equation of a Regression Line Section 9.2  The equation of the line is in the form:  Note: Round all decimals to three decimal places

6. Constructing a Linear Regression  Compute the value of the line of best fit for the data obtained in the study of age and blood pressure given below: SubjectAge (x)Pressure (y) A43128 B48120 C56135 D61143 E67141 F70152 Section 9.2

7.  Using our linear regression, determine what the pressure for a person 50 years old would be?  Using our linear regression, how old would a person with a determine what the pressure level of 145 be?

8. Constructing a Linear Regression  Determine the line of best fit for the data obtained in a study of the number of absences and the final grades of seven randomly selected students from a statistics class. Subject# of Absences (x)Final Grade (y) A682 B286 C1543 D974 E1258 F590 G878 Section 9.2

9.  Using our linear regression, determine what the grade would be for a student with 7 absences?  Using our linear regression, determine how many absences a person with a 95% would have?

10. Homework:  Pgs. 490 (13 – 18)  Note: 17 and 18 are to be completed using the formula.  Read and take notes on Section 9.2 (pg 488)