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.   Predictor  Coef      SE Coef      T              P Constant 316.27     28.31      11.24     0.002 Elevation -32.660    3.511    −8.79      0.003 S = 11.8603  R-Sq = 95.1% Here, ŷ is the number of frost-free days and the variable x is the elevation measured in thousands of feet. Therefore, each 1000 ft increase in elevation results in a 1 unit increase in x. fill in the blank in part a/part b. a) Recall that the slope of a least-squares line,  ŷ = a + bx, indicates how much ŷ changes when x changes by 1 unit. In part (a), we determined the slope of the line is

Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
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Chapter7: Analytic Trigonometry
Section7.6: The Inverse Trigonometric Functions
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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.

 

Predictor  Coef      SE Coef      T              P
Constant 316.27     28.31      11.24     0.002
Elevation -32.660    3.511    −8.79      0.003
S = 11.8603  R-Sq = 95.1%

Here, ŷ is the number of frost-free days and the variable x is the elevation measured in thousands of feet. Therefore, each 1000 ft increase in elevation results in a 1 unit increase in x. fill in the blank in part a/part b.

a) Recall that the slope of a least-squares line,  ŷ = a + bx, indicates how much ŷ changes when x changes by 1 unit. In part (a), we determined the slope of the line is b = −32.660. This means that for each unit that x increases, the value of ŷ decreases by _______ units.

b) Therefore, when the elevation increases by 1000 ft there are _______ fewer frost-free days.

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