I have my research ready. My professor wants me to add these two modals. Can you add them in my research and do the analyses for them in R. These are the modules: 1. . Conway-Maxwell Poisson (Com-Poisson) Model
2. Spatial Auto-Poisson Model. Please follow these steps when you are doing the analyses. Please feel free to let me know if you think they are not matching my research paper. see the attached for more details. Please include Statistical scripts Screenshots of outputs Simple statistical models Brief report
Methodology
• Conway-Maxwell Poisson (Com-Poisson) Model Setup:
o Define the Com-Poisson model and explain how it generalizes the Poisson model by adding a dispersion parameter to manage over-dispersion.
o Describe the types of data that would benefit from this model, such as energy consumption data with high variability.
o Implementation:
 Explain that you will use R (or Python) to fit the Com-Poisson model, as SPSS does not natively support it. Mention the COMPoissonReg package in R.
 Outline steps in R:
1. Load the data.
2. Install and load the COMPoissonReg package.
3. Fit the Com-Poisson model to your dependent variable.
4. Interpret the output, focusing on the dispersion parameter to confirm over- or under-dispersion.
• Spatial Auto-Poisson Model Setup:
o Define the Spatial Auto-Poisson model, emphasizing its use for spatially correlated data. Mention how it accounts for dependencies across geographic regions.
o Specify that this model is relevant when data includes region-specific energy consumption, which might vary due to factors like regional infrastructure or urbanization.
o Implementation:
 This model is best implemented in R with packages like spdep or CARBayes.
 Outline steps in R:
1. Load geographic data for Saudi Arabia with region codes.
2. Use spdep to create a spatial weights matrix that defines the spatial relationship among regions.
3. Fit the Auto-Poisson model using spautolm() from the spdep package.
4. Interpret spatial autocorrelation parameters to assess the strength of regional influences on energy consumption.
4. Data Analysis and Results
• Com-Poisson Model Results:
o Provide a table of coefficients, standard errors, and the dispersion parameter. Focus on the significance of predictors and the dispersion parameter.
o Example Interpretation: “The Com-Poisson model revealed a significant relationship between population and energy consumption. The dispersion parameter (1.3) indicates over-dispersion, validating the choice of the Com-Poisson model over the standard Poisson.”
• Spatial Auto-Poisson Model Results:
o Include tables showing spatial autocorrelation parameters and coefficients for each predictor.
o Discuss findings related to regional disparities.
o Example Interpretation: “The Spatial Auto-Poisson model showed significant spatial autocorrelation, suggesting that regional characteristics influence energy consumption. This correlation highlights potential infrastructure disparities in energy distribution.”