titled document -. G kji - Google Search Spanish We want to write the least-squares equation y = a + bx, using the given printout. Predictor Coef SE Coef Constant 314 , 38 28 , 31 11.24 0.802 Elevation -33. 082 3.511 -8.79 S = 11.8683 R-Sq = 07. TM Recall that a is the Intercept and b is the slope of the line. Since the elevation Is the explanatory variable, x, the coefficient of the x-term Is the slope of the line. From the printout, this corresponds to the table value in the "Elevation" row, and "Coef" column. Therefore, b = -33.092 -33.092 The Intercept is given by the value of the constant term. There is no variable, but the Minitab printout classifies this value as a "coefficient." (Note: This may-or may not coincide with your previous experience with the terms "constant" and "coefficient, " but it is mathematically correct for reasons that we will not explore in this tutorial.) The value in the "Constant" row and "Coef" column is 314 38. Therefore, 8 - 314.38 314.35 substitute these values into the least-square equation. 5 + bx 314.38 314.36 -33.092 -33.092 Step 2 (b) For each 1000-foot Increase in elevation, how many fewer frost-free days are predicted? We want to determine how many fewer frost-free days are predicted when the elevation Increases by 1000 ft. Here, p is the number of frost-free days and the variable x is the elevation measured in thousands of feet. Therefore, each 1000 it Increase in elevation results in a i unit Increase In r. Recall that the slope of a least-squares line, / = a + bx, Indicates how much y changes when x changes by 1 unit. In part (a), we determined the slope of the line is b - -33.092. This means that for each unit that x Increases, the value of j decreases by -33.092 se units. Therefore, when the elevation Increases by 1000 ft there are |1 * fewer frost-free days