Generalized Linear Models w/ Examples in R, Solution to Problem 2.15
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Question 2.15An experiment was conducted
Question 2.15
An experiment was conducted [11] to determine how to maximize Mermaid meadowfoam flower production (Table 2.9; data set: flowers) for extraction as vegetable oil.
Table 2.9 The average number of flowers per meadowfoam plant (based on ten seedlings) exposed to various levels of lighting at two different times: at photoperiodic floral induction (PFI) or 24 days before PFI. These data are consistent with the results in [11]
Timing LI
150 300 450 600 750 900
At PFI 62.4 77.1 55.7 54.2 49.5 62.0 39.3
Before PFI 77.7 75.4 68.9 78.2 57.2 70.9 62.9
LI stands for "Light intensity (in μmolm−2s−1 )
Plot the average number of flowers produced per plant against the light intensity, distinguishing the two timings. Comment.
Suppose a model with the systematic component Flowers ~ Light + Timing was needed to model the data. What would such a systematic component imply about the relationship between the variables?
Suppose a model with the systematic component Flowers ~ Light * Timing was needed to model the data. What would such a systematic component imply about the relationship between the variables?
Fit the two linear regression models with the systematic components specified above. Which is the preferred model?
The fitted model should use all prior weights as wi=10 for all i . What difference does it make if the prior weights are not defined (which r interprets as wi=1 for all i )?
Plot the systematic component of the preferred regression model on the data.
Interpret the model.
(This problem continues in Problem 3.13.)
My Colab Answers: https://colab.research.google.com/dri...
My Github: https://github.com/nicklausmillican
An experiment was conducted [11] to determine how to maximize Mermaid meadowfoam flower production (Table 2.9; data set: flowers) for extraction as vegetable oil.
Table 2.9 The average number of flowers per meadowfoam plant (based on ten seedlings) exposed to various levels of lighting at two different times: at photoperiodic floral induction (PFI) or 24 days before PFI. These data are consistent with the results in [11]
Timing LI
150 300 450 600 750 900
At PFI 62.4 77.1 55.7 54.2 49.5 62.0 39.3
Before PFI 77.7 75.4 68.9 78.2 57.2 70.9 62.9
LI stands for "Light intensity (in μmolm−2s−1 )
Plot the average number of flowers produced per plant against the light intensity, distinguishing the two timings. Comment.
Suppose a model with the systematic component Flowers ~ Light + Timing was needed to model the data. What would such a systematic component imply about the relationship between the variables?
Suppose a model with the systematic component Flowers ~ Light * Timing was needed to model the data. What would such a systematic component imply about the relationship between the variables?
Fit the two linear regression models with the systematic components specified above. Which is the preferred model?
The fitted model should use all prior weights as wi=10 for all i . What difference does it make if the prior weights are not defined (which r interprets as wi=1 for all i )?
Plot the systematic component of the preferred regression model on the data.
Interpret the model.
(This problem continues in Problem 3.13.)
My Colab Answers: https://colab.research.google.com/dri...
My Github: https://github.com/nicklausmillican
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