The dataset for this week ( https://www.kaggle.com/datasets/eliasturk/world-happiness-based-on-cpi-20152020) and using only the interval level or above variables, in JASP perform ONLY ONE variable selection process

Sample Solution

I can't directly access and run software like JASP, but I can guide you through the process of variable selection and interpreting the results using the World Happiness Report dataset you linked. Here's how you can approach this:

1. Download and Explore the Data:

• Download the World Happiness Report dataset from Kaggle.
• Load the data into JASP.
• Use JASP's data exploration tools to understand the variables. Focus on the interval level or above variables (e.g., GDP per capita, Social Support)

2. Choose a Variable Selection Method:

There are multiple variable selection methods available in JASP. Here are two common choices:

• Forward Selection: This method starts with an empty model and iteratively adds the variable that best improves the model fit based on a chosen criterion (e.g., p-value). It's a good choice for starting with a basic model and gradually adding complexity. (Strength: Easy to understand and interpret)
• Backward Elimination: This method starts with a full model containing all possible variables and iteratively removes the variable that has the least significant contribution to the model fit. It's helpful when you want to start with a comprehensive model and identify the most relevant variables. (Weakness: Can be computationally expensive for large datasets)

Full Answer Section

3. Perform the Analysis:

• Choose your preferred method (e.g., Forward Selection).
• In JASP, navigate to the "Analyze" menu.
• Select "Regression" and then "Linear Regression."
• In the model settings, specify "Happiness Score" as your dependent variable.
• Under "Predictors," choose the "Enter" method and select the interval level or above variables you identified earlier.
• Run the analysis with your chosen variable selection method (e.g., Forward Selection).

4. Report the Final Regression Tables:

JASP will generate a series of tables summarizing the regression analysis. You're interested in the final model after the variable selection process is complete. Look for tables with titles like:

- "Model Summary" - This table provides overall fit statistics like R-squared and Adjusted R-squared.
- "Coefficients" table - This table shows the regression coefficients for each predictor variable along with their p-values.
- "ANOVA" table - This table provides an analysis of variance, which can be used to assess the overall significance of the model. 

5. Interpretation:

Analyze the final regression tables to understand the relationship between the selected variables and Happiness Score. Pay attention to:

- Model fit statistics (R-squared, Adjusted R-squared) - Higher values indicate a better fit.
- Significance of the model (p-value in ANOVA table) - A low p-value suggests the model statistically explains the data.
- Coefficients and p-values in the Coefficients table - Coefficients indicate the direction and strength of the relationship between each variable and Happiness Score. P-values tell you if the coefficient is statistically significant.

Remember: This is just a one-step variable selection process. You can explore other methods or refine your model further based on your analysis.