A marketing company based out of New York City is doing well and is looking to expand internationally.

Full Answer Section

• • Compare the descriptive statistics of the other cities to NYC. This will provide context.

3. City Selection Based on Descriptive Statistics:

• "Best Potential" City:
  • Consider factors that would be attractive to a marketing company:
    • Lower cost of living (to reduce operating expenses).
    • Reasonable cost of housing (for potential employees).
    • Affordable transportation.
    • Consider the cost of things that employees would enjoy, such as wine, and coffee.
  • For each significant variable, identify cities that have values that are favorable compared to NYC.
  • Look for cities that consistently rank well across multiple significant variables.
  • Consider the overall cost of living index.
• Cities Below the Median:
  • For each significant variable, identify cities with values below the median.
  • These cities represent the lower end of the cost spectrum.
• Cities in the Upper 3rd Quartile:
  • For each significant variable, identify cities with values in the upper 3rd quartile.
  • These cities represent the higher end of the cost spectrum.

4. Recommendation and Justification:

• Choose a City (or Cities): Based on your analysis, recommend the city (or cities) where the marketing company should open a second office.
• Justify Your Choice:
  • Explain how the city's cost of living and other factors align with the company's goals.
  • Use specific data points from your MLR and descriptive statistics to support your claims.
  • Address any potential drawbacks of your chosen city.
  • Rank the cities from best to worst, if multiple cities are chosen.
• Example Justification Points:
  • "City X has a significantly lower cost of living index than NYC, and its average rent for a 3-bedroom apartment is [amount] lower."
  • "City Y has a very affordable transportation system, with a monthly pass costing only [amount]."
  • "City Z has a very high Q3 for rent, so therefore it is not recommended."

Example Analysis (Hypothetical):

Let's imagine (for illustrative purposes) that your MLR output shows that "3-bedroom apartment" and "monthly transportation pass" are the only significant variables.

• Descriptive Statistics:
  • You find that City A has a significantly lower average rent and transportation pass cost than NYC.
  • City B has a moderate rent but a very low transportation cost.
  • City C has very high rent.
• Recommendation:
  • You might recommend City A as the top choice, as it offers significant cost savings in both housing and transportation.
  • City B could be a second choice.
  • City C would not be recommended.

Important Notes:

• Remember to use the actual data from your Excel output.
• Consider qualitative factors that might be relevant (e.g., availability of talent, market potential, cultural fit).
• Clearly explain your reasoning and provide evidence for your claims.
• Consider the overall cost of living index when making your final decision.

Sample Answer

Identifying Significant Variables from the MLR Output:

• Look for P-values: In your MLR output, the "P-value" column (or "Significance F" for the overall model) indicates the statistical significance of each variable.
  • Variables with P-values less than 0.05 are generally considered statistically significant.
  • List the variables (3-bedroom apartment, transportation pass, wine, bread, milk, coffee) that have significant P-values.

2. Descriptive Statistics Analysis of Significant Predictors:

• Extract Values: For each significant variable, extract the following from your descriptive statistics:
  • Mean
  • Median
  • Minimum (Min)
  • Maximum (Max)
  • First Quartile (Q1)
  • Third Quartile (Q3)
• Compare to New York:
  • Find the corresponding values for each significant variable in New York City (the baseline).