Annual high temperatures in a certain location have been tracked for several years. Let X represent the number of years after 2000 and Y the high temperature. Based on the data shown below, calculate the linear regression equation using technology (each constant to 2 decimal places). X y 3 35.01 4 37.78 5 37.35 6 36.72 7 40.09 8 40.36 9 40.83 10 41.3 11 40.57 12 42.64 13 44.21 14 42.58 46.05 45.12 15 16 The equation is y = Interpret the slope For each additional 33.75 years, the annual high temperature will increase by 1 degree on average. For each additional year, the annual high temperature will increase by 33.75 degrees on average. For each additional year, the annual high temperature will increase by 0.74 degrees on average. For each additional 0.74 years, the annual high temperature will increase by 1 degree on average. X + Interpret the y-intercept O In 2000, the temperature was about 33.75. O In 2003, the temperature was about 33.75. O In 2003, the temperature was about 0.74. O In 2016, the temperature was about 45.12. O It does not make sense to interpret the intercept in this scenario.
Annual high temperatures in a certain location have been tracked for several years. Let X represent the number of years after 2000 and Y the high temperature. Based on the data shown below, calculate the linear regression equation using technology (each constant to 2 decimal places). X y 3 35.01 4 37.78 5 37.35 6 36.72 7 40.09 8 40.36 9 40.83 10 41.3 11 40.57 12 42.64 13 44.21 14 42.58 46.05 45.12 15 16 The equation is y = Interpret the slope For each additional 33.75 years, the annual high temperature will increase by 1 degree on average. For each additional year, the annual high temperature will increase by 33.75 degrees on average. For each additional year, the annual high temperature will increase by 0.74 degrees on average. For each additional 0.74 years, the annual high temperature will increase by 1 degree on average. X + Interpret the y-intercept O In 2000, the temperature was about 33.75. O In 2003, the temperature was about 33.75. O In 2003, the temperature was about 0.74. O In 2016, the temperature was about 45.12. O It does not make sense to interpret the intercept in this scenario.
Chapter6: Exponential And Logarithmic Functions
Section6.8: Fitting Exponential Models To Data
Problem 3TI: Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to...
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Transcribed Image Text:Annual high temperatures in a certain location have been tracked for several years. Let X represent the
number of years after 2000 and Y the high temperature. Based on the data shown below, calculate the linear
regression equation using technology (each constant to 2 decimal places).
X
y
3
35.01
4
37.78
5
37.35
6
36.72
7 40.09
8
40.36
9
40.83
10
41.3
11 40.57
12 42.64
13
44.21
14 42.58
15 46.05
16
45.12
The equation is ĝ ŷ =
X +
Interpret the slope
O For each additional 33.75 years, the annual high temperature will increase by 1 degree on average.
O For each additional year, the annual high temperature will increase by 33.75 degrees on average.
For each additional year, the annual high temperature will increase by 0.74 degrees on average.
O For each additional 0.74 years, the annual high temperature will increase by 1 degree on average.
Interpret the y-intercept
O In 2000, the temperature was about 33.75.
O In 2003, the temperature was about 33.75.
O In 2003, the temperature was about 0.74.
O In 2016, the temperature was about 45.12.
O It does not make sense to interpret the intercept in this scenario.
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