Project Title: Exploring Global Happiness and Wellbeing: An Analysis of Factors Influencing Life Satisfaction Across Countries from 2008 to 2023

Project Theme: The project aims to investigate the determinants of happiness and life satisfaction among nations, focusing on key factors such as economic development, social support, health, freedom, generosity, corruption perception, and emotional wellbeing. By analyzing data from the World Happiness Report spanning the years 2008 to 2023, the study seeks to gain deeper insights into the complex relationships that contribute to global happiness trends and disparities.

Project Steps:

1. Data Collection and Preprocessing:

• Obtain the World Happiness Report dataset for the years 2008 to 2023, including variables such as Life Ladder (happiness score), Log GDP per capita, Social support, Healthy life expectancy at birth, Freedom to make life choices, Generosity, Perceptions of corruption, Positive affect, and Negative affect.
• Check for missing data and handle any inconsistencies or outliers.
• Group the data by country and perform necessary transformations to prepare it for analysis.

2. Descriptive Statistics and Data Visualization:

• Calculate summary statistics for each variable to gain a general understanding of the dataset.
• Create data visualizations such as bar charts, scatter plots, and heatmaps to visualize the distribution and relationships between different variables.
• Analyze the trends in the overall global happiness index and compare happiness scores across countries and regions.

3. Longitudinal Analysis of Global Happiness Trends:

• Conduct a time-series analysis to examine how the overall happiness index has changed over the years from 2008 to 2023.
• Identify countries that have experienced significant changes in happiness levels during this period.

4. Correlation Analysis:

• Perform correlation analysis to explore the relationships between various factors and happiness scores.
• Identify which factors show strong positive or negative correlations with life satisfaction.

5. Multiple Regression Analysis:

• Build a multiple regression model to assess the combined impact of economic development, social support, health, freedom, generosity, corruption perception, positive affect, and negative affect on the life satisfaction of individuals in different countries.
• Interpret the regression coefficients and assess the significance of each variable in predicting happiness scores.

6. Regional Comparison:

• Compare happiness scores and key factors across different regions and continents.
• Analyze regional patterns and identify potential cultural, economic, or social influences on happiness levels.

7. Impact of Specific Events and Policies:

• Investigate how specific social, political, or economic events have affected happiness scores in individual countries.
• Analyze the impact of government policies and initiatives aimed at promoting wellbeing and happiness.

8. Conclusion and Recommendations:

• Summarize the key findings and insights gained from the analysis.
• Discuss the implications of the study's results on understanding happiness and wellbeing at a global level.
• Provide recommendations for policymakers and governments based on the identified factors that have the most significant influence on life satisfaction.

9. Presentation and Reporting:

• Create visual presentations summarizing the research findings, including graphs, charts, and tables.
• Write a comprehensive report documenting the research process, data analysis methods, results, and conclusions.

10. Future Directions:

• Suggest potential areas for further research and exploration within the realm of happiness and wellbeing studies.
• Discuss limitations of the current study and propose ways to improve data collection and analysis for future research.

Note: Throughout the project, it is essential to use appropriate statistical techniques and data visualization tools to ensure robust and accurate analysis of the dataset. Additionally, the project should adhere to ethical considerations regarding data privacy and confidentiality.