Problems 5 and 6. you do not have to compute the slope and y intercept for the least squares line. You can use the SPSS output in each problem. The Linear Regression Equation: The regression equations are listed below if you prefer to compute the coefficients. Y = mx+b m= b =ymx = 5 . The data below are ages and systolic blood pressures (measured in millimeters of mercury) of 9 randomly selected adults. Complete the section below. (Section 9.2) Age, x 38 41 45 48 51 53 Pressure, y 116 120 123 57 131 61 142 65 145 148 150 152 SPSS Output: Model Summary Adjusted R Std. Error of the R R Square Square Estimate Model 1 = 1.488 + 60.461 960a 921 910 4.199 a. Predictors: (Constant), Age Coefficients Standardized "intercept Unstandardized Coefficients Coefficients B Std. Error Beta Sig. Model 60.461 8.515 (Constant) 7.101 000 1 1.488 165 .960 9.034 Age .000 a. Dependent Variable: Pressure a. Equation of Regression Line: - .28- 60.46 f b. Predicted Blood Pressure at 54 years old: Predicted Blood Pressure at 25 years old: d. Do you think both of these predictions are accurate? 5