How GDP affect the number of licensed food establishments in Singapore
Project Scope
This project aimed to analyze the relationship between Singapore's GDP and the number of licensed food establishments. The analysis provided insights into how economic growth influences the food industry and broader business trends in Singapore.
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The main goal was to use regression analysis to determine whether there is a significant relationship between GDP and the number of licensed food establishments.
A challenge was ensuring data accuracy and correctly interpreting regression statistics to support the hypothesis, including statistical measures like R-squared and p-values. This way we would have accurate, complete, and free of outliers that could skew the results.
Due to time constraints, we needed to balance this project with other academic deadlines, which made it challenging to dedicate sufficient time to deeper analysis
My roles and solution
I was actively involved in gathering and processing data, performing regression analysis, and interpreting results to derive meaningful conclusions. I also collaborated with my team to summarize the findings and align them with the research objectives for the project.
Statistics for GDP
The process began by identifying the dependent variable (GDP) and the independent variable (licensed food establishments). I then used historical data to establish trends and relationships between these variables. By running regression analysis, I quantified this relationship and validated our hypothesis. This structured approach ensured logical progression and statistical rigor.
Statistics for Licensed Food Estabilshements
Inputting the results
Work Process
The project was managed through teamwork and division of responsibilities. Each member took ownership of specific tasks—data collection, analysis, or documentation. I used Excel to generate scatter plots, compute regression outputs, and interpret the summary statistics. We also held multiple group discussions to ensure accuracy and coherence in our findings.
Computing Regression Outputs
Generating Scatter Plots
One significant issue was understanding the more profound implications of the regression results, such as how to interpret R-squared, significance F, and p-values meaningfully. To address this, we revisited statistical concepts in our lecture slides in Politemall and worked collaboratively to clarify doubts. This strengthened both our individual understanding and group confidence in presenting results.
Outcome and Results Achieved
As Singapore's GDP increases, a strong positive correlation exists with the number of licensed food establishments. The correlation coefficient 0.95 (2 d.p.) indicates a solid relationship between GDP and the number of licensed food establishments.
The positive gradient of 0.0193 (3 s.f.) supports the idea that as GDP rises, the number of licensed food establishments tends to increase. Economic growth creates an environment where entrepreneurs are more likely to establish licensed food establishments.
The Y-intercept shows that when X-Variable = 0, there are no Licensed Food Establishments. There will be a negative GDP of -320.3747447.
The linear regression model also predicts that if licensed food establishments reach 70,000, the corresponding GDP would be approximately USD 1,029.94 Billion.
Thus, the number of licensed food establishments is directly proportional to Singapore's GDP.
The analysis demonstrated a statistically significant relationship, with a p-value of 1.044 × 10⁻⁷, confirming the robustness of the findings.
Additionally, the regression model provided predictive insights, such as estimating GDP based on future projections of licensed food establishments. These measurable results provided valuable insights for policymakers and business stakeholders.
