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Analyze the provided regression output and descriptive statistics from the dataset of 17 cities regarding their cost of living, and use this information to recommend a city for expanding a second office. The dataset includes variables such as the cost of a 3-bedroom apartment, monthly transportation pass, prices of wine, bread, milk, and coffee, alongside the overall cost of living index. The regression results should be interpreted to identify significant predictive variables. Based on this analysis, select the most suitable city and justify your choice in a concise executive summary, not exceeding one page in 12-point Times New Roman font.

Paper For Above instruction

The decision to expand a company's operations into new international markets requires careful analysis of multiple economic indicators, particularly the cost of living, which directly influences operational costs and employee compensation expectations. In this scenario, data from 17 cities worldwide were analyzed using multiple linear regression (MLR) to identify key predictors impacting the overall cost of living. The goal is to leverage regression outputs and descriptive statistics to recommend the most strategic city for establishing a new office, considering cost efficiencies and potential impact on company expenses.

The regression analysis provided several insights into the significant factors affecting the cost of living across these cities. Notably, the variables identified as significant predictors included the rent in the city center, the price of a monthly public transportation pass, and the cost of certain consumables such as bread, milk, wine, and coffee. Among these, rent and transportation costs often emerge as the most substantial expenses in urban living, and their statistical significance in the model suggests their strong influence on the overall cost index. The coefficient estimates illustrate the relationship's direction and magnitude, with higher rent and transportation costs correlating with increased cost of living indices.

Descriptive statistics such as mean, median, minimum, maximum, and quartiles further facilitate city comparisons. For instance, the city with the lowest cost of living index among the sample is Mumbai, with an index of 31.74, significantly below the median value of 82.2. Conversely, New York exhibits the highest index at 100, serving as the baseline. When examining the significant predictors, Mumbai also displays the lowest rent and transportation costs, making it a cost-efficient candidate for expansion, especially considering the company's headquarters are based there, potentially simplifying operational logistics and employee relocation expenses.

Further analysis of the data reveals that cities like Prague and Warsaw also fall below the median in the cost of living indices, with relatively lower rent and transportation costs. However, Mumbai stands out due to its combination of affordability across multiple cost factors and the availability of talent pools, infrastructure, and market growth potential. Cities like Seoul, Tokyo, and Amsterdam, while having high living costs and being in the upper quartile, are less attractive for cost-saving considerations.

Based on the significant predictors and descriptive statistics, Mumbai emerges as the most suitable location for expanding the company’s operations. Its low rent, transportation, and commodity prices translate into reduced operational expenses, aligning with the company's goal to optimize costs. While other cities like Prague and Warsaw offer cost benefits, Mumbai’s overall economic profile and potential for growth provide a strategic advantage. Therefore, Mumbai is recommended as the optimal choice for establishing a second office, supporting efforts to expand international reach while maintaining cost efficiency.

References

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