- Using R For Code No Other Languages Are Acceptable 1 (94.85 KiB) Viewed 90 times
Using R for code, no other languages are acceptable
-
answerhappygod
- Site Admin
- Posts: 899603
- Joined: Mon Aug 02, 2021 8:13 am
Using R for code, no other languages are acceptable
Using R for code, no other languages areacceptable
An study was conducted to determine how to maximize Mermaid meadowfoam flower production for extraction as vegetable oil. data set: flowers in GLMsdata package. 1. Plot the average number of flowers produced per plant against the light intensity, distin- guishing the two timings. Comment. 2. 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? 3. Suppose a model with the systematic component Flowers Light + Timing + Light: Timing was needed to model the data. What would such a systematic component imply about the relationship between the variables? 4. Fit the two linear regression models with the systematic components specified above. Give the regression tables and comment. 5. Perform a diagnostic analysis on the fitted linear regression models. Present the residuals plots and comment. 6. Identify any influential observations or outliers. 7. What is the best model? Why? 8. Interpret the final model (best model).
An study was conducted to determine how to maximize Mermaid meadowfoam flower production for extraction as vegetable oil. data set: flowers in GLMsdata package. 1. Plot the average number of flowers produced per plant against the light intensity, distin- guishing the two timings. Comment. 2. 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? 3. Suppose a model with the systematic component Flowers Light + Timing + Light: Timing was needed to model the data. What would such a systematic component imply about the relationship between the variables? 4. Fit the two linear regression models with the systematic components specified above. Give the regression tables and comment. 5. Perform a diagnostic analysis on the fitted linear regression models. Present the residuals plots and comment. 6. Identify any influential observations or outliers. 7. What is the best model? Why? 8. Interpret the final model (best model).