3. A biologist was recording various temperatures (in degrees Celsius) and predicting the amount of glucose produced by a plant (in milligrams). Suppose the linear equation was the following: ŷ 2.7 +0.0123x = R² a) Based on the coefficient of determination, how would you classify the predictive power of the model? b) Suppose that a residual plot was the following: 25 20 15 Leg 10 5 0 -5 -10 -15 -20 -25 0 5 Fitted Values vs. Residuals 10 15 20 Fitted Value = 2% 25 30 35 Is a linear regression model appropriate for this kind of data? Why or why not?

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3. A biologist was recording various temperatures (in degrees Celsius) and predicting the
amount of glucose produced by a plant (in milligrams). Suppose the linear equation was
the following:
ŷ = 2.7 +0.0123x R²
a) Based on the coefficient of determination, how would you classify the predictive
power of the model?
b) Suppose that a residual plot was the following:
25
20
15
10
J
5
0
-5
-10
-15
Residual
-20
-25
0
5
10
Fitted Values vs. Residuals
15
20
Fitted Value
25
= 2%
30
35
40
Is a linear regression model appropriate for this kind of data? Why or why not?
Transcribed Image Text:3. A biologist was recording various temperatures (in degrees Celsius) and predicting the amount of glucose produced by a plant (in milligrams). Suppose the linear equation was the following: ŷ = 2.7 +0.0123x R² a) Based on the coefficient of determination, how would you classify the predictive power of the model? b) Suppose that a residual plot was the following: 25 20 15 10 J 5 0 -5 -10 -15 Residual -20 -25 0 5 10 Fitted Values vs. Residuals 15 20 Fitted Value 25 = 2% 30 35 40 Is a linear regression model appropriate for this kind of data? Why or why not?
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