Calculating the Sum of Deviations

Oct 13, 2025 by recoveryshake.com 33 views

Exploring the Relationship Between Distance and Average Selling Price of 3-Bedroom Homes

In the world of real estate, understanding the factors that influence the selling price of properties is crucial for agents and investors alike. One such factor is the distance of a property from a major city like New York City. In this article, we will delve into the relationship between the distance of 3-bedroom homes from New York City and their average selling prices. We will use a dataset of 6 homes to explore this relationship and identify any patterns or correlations.

The dataset consists of 6 homes, each with its distance from New York City and average selling price. The data is presented in the table below:

Distance (miles)	Average Selling Price (dollars)
10	450,000
20	380,000
30	320,000
40	280,000
50	240,000
60	200,000

To determine the relationship between the distance of the homes from New York City and their average selling prices, we need to calculate the correlation coefficient. The correlation coefficient measures the strength and direction of the linear relationship between two variables.

Let's denote the distance as x and the average selling price as y. We can calculate the correlation coefficient using the following formula:

r = Σ[(xi - x̄)(yi - ȳ)] / (√[Σ(xi - x̄)²] * √[Σ(yi - ȳ)²])

where xi and yi are the individual data points, x̄ and ȳ are the means of the two variables, and Σ denotes the sum.

Calculating the Means

To calculate the correlation coefficient, we need to find the means of the distance and average selling price variables.

x̄ = (10 + 20 + 30 + 40 + 50 + 60) / 6 = 30 ȳ = (450,000 + 380,000 + 320,000 + 280,000 + 240,000 + 200,000) / 6 = 320,000

Calculating the Sum of Deviations

Next, we need to calculate the sum of the deviations of each data point from the mean.

Σ(xi - x̄) = (10 - 30) + (20 - 30) + (30 - 30) + (40 - 30) + (50 - 30) + (60 - 30) = -20 + -10 + 0 + 10 + 20 + 30 = 30 Σ(yi - ȳ) = (450,000 - 320,000) + (380,000 - 320,000) + (320,000 - 320,000) + (280,000 - 320,000) + (240,000 - 320,000) + (200,000 - 320,000) = 130,000 + 60,000 + 0 - 40,000 - 80,000 - 120,000 = -150,000

Calculating the Correlation Coefficient

Now, we can calculate the correlation coefficient using the formula:

r = Σ[(xi - x̄)(yi - ȳ)] / (√[Σ(xi - x̄)²] * √[Σ(yi - ȳ)²]) = (-20 * 130,000) + (-10 * 60,000) + (0 * 0) + (10 * -40,000) + (20 * -80,000) + (30 * -120,000) / (√[(-20)² + (-10)² + 0² + 10² + 20² + 30²] * √[130,000² + 60,000² + 0² + (-40,000)² + (-80,000)² + (-120,000)²]) = -2,600,000 - 600,000 + 0 - 400,000 - 1,600,000 - 3,600,000 / (√[400 + 100 + 0 + 100 + 400 + 900] * √[16,900,000,000 + 3,600,000,000 + 0 + 1,600,000,000 + 12,800,000,000 + 14,400,000,000]) = -8,400,000 / (√1,500 * √48,500,000,000) = -8,400,000 / (√1,500 * 6,960,000) = -8,400,000 / 10,368,000 = -0.81

Interpreting the Correlation Coefficient

The correlation coefficient is -0.81, which indicates a strong negative linear relationship between the distance of the homes from New York City and their average selling prices. This means that as the distance from New York City increases, the average selling price of the homes decreases.

In this article, we explored the relationship between the distance of 3-bedroom homes from New York City and their average selling prices. We used a dataset of 6 homes to calculate the correlation coefficient, which revealed a strong negative linear relationship between the two variables. This suggests that agents and investors should consider the distance of a property from New York City when determining its selling price.

Based on the findings of this study, we recommend that real estate agents and investors consider the following:

When pricing a 3-bedroom home, consider the distance from New York City and adjust the price accordingly.
When evaluating the potential of a property, consider the distance from New York City and its impact on the selling price.
When developing a marketing strategy, highlight the proximity of the property to New York City as a selling point.

By considering the distance of a property from New York City, agents and investors can make more informed decisions and increase their chances of success in the real estate market.
Q&A: Exploring the Relationship Between Distance and Average Selling Price of 3-Bedroom Homes

In our previous article, we explored the relationship between the distance of 3-bedroom homes from New York City and their average selling prices. We found a strong negative linear relationship between the two variables, indicating that as the distance from New York City increases, the average selling price of the homes decreases. In this article, we will answer some of the most frequently asked questions about this relationship and provide additional insights.

A: The correlation coefficient is a statistical measure that indicates the strength and direction of the linear relationship between two variables. In this study, the correlation coefficient of -0.81 indicates a strong negative linear relationship between the distance of the homes from New York City and their average selling prices.

A: By understanding the relationship between the distance of a property from New York City and its average selling price, you can make more informed decisions when pricing, evaluating, and marketing properties. For example, if you are a real estate agent, you can adjust the price of a property based on its distance from New York City. If you are an investor, you can consider the distance of a property from New York City when evaluating its potential.

A: While this study provides valuable insights into the relationship between the distance of 3-bedroom homes from New York City and their average selling prices, there are some potential limitations to consider. For example, the study is based on a small sample size of 6 homes, which may not be representative of the larger population of 3-bedroom homes in the area. Additionally, the study does not control for other factors that may influence the selling price of a property, such as the condition of the property, the quality of the neighborhood, and the local economy.

A: The relationship between the distance of a property from a major city and its average selling price is not unique to New York City. You can apply this knowledge to other areas of the real estate market by considering the distance of a property from a major city and its impact on the selling price. For example, if you are a real estate agent in Los Angeles, you can consider the distance of a property from downtown Los Angeles when pricing and marketing properties.

A: The study has several potential implications for real estate agents and investors. For example, real estate agents can use this information to adjust the price of a property based on its distance from New York City. Investors can consider the distance of a property from New York City when evaluating its potential. Additionally, the study highlights the importance of considering the distance of a property from a major city when making decisions in the real estate market.

A: To further explore this relationship using additional data, you can collect more data on the distance of 3-bedroom homes from New York City and their average selling prices. You can also consider collecting data on other factors that may influence the selling price of a property, such as the condition of the property, the quality of the neighborhood, and the local economy. By analyzing this additional data, you can gain a deeper understanding of the relationship between the distance of a property from New York City and its average selling price.

In this article, we answered some of the most frequently asked questions about the relationship between the distance of 3-bedroom homes from New York City and their average selling prices. We highlighted the significance of the correlation coefficient, provided insights into how to apply this knowledge in the real estate market, and discussed potential limitations and implications of the study. By understanding this relationship, real estate agents and investors can make more informed decisions and increase their chances of success in the real estate market.