A Contractor Records The Areas, In Square Feet, Of A Small Sample Of Houses In A Neighborhood To Determine Data About The Neighborhood. The Areas Are:${ 2,400; 1,750; 1,900; 2,500; 2,250; 2,100 }$Which Of The Following Represents The

Mar 8, 2025 by ADMIN 235 views

**Understanding the Neighborhood Data: A Statistical Analysis of House Areas**

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Introduction

In the field of statistics, collecting and analyzing data is crucial to understand various aspects of a community or neighborhood. A contractor, in this case, has recorded the areas of a small sample of houses in a neighborhood to gather information about the neighborhood. The data collected includes the areas of six houses in square feet. In this article, we will analyze the given data and determine the appropriate statistical representation.

The Given Data

The areas of the six houses in the neighborhood are recorded as follows:

2,400 square feet
1,750 square feet
1,900 square feet
2,500 square feet
2,250 square feet
2,100 square feet

Understanding the Data

To understand the data, we need to calculate the mean, median, mode, and range of the given areas. These statistical measures will help us determine the central tendency and variability of the data.

Mean

The mean is the average of all the values in the dataset. To calculate the mean, we add up all the values and divide by the total number of values.

import numpy as np
areas = [2400, 1750, 1900, 2500, 2250, 2100]

mean_area = np.mean(areas)
print("Mean Area:", mean_area)

Median

The median is the middle value of the dataset when it is arranged in ascending order. If the dataset has an even number of values, the median is the average of the two middle values.

# Calculate the median
median_area = np.median(areas)
print("Median Area:", median_area)

Mode

The mode is the value that appears most frequently in the dataset. In this case, we can see that there is no value that appears more than once, so the dataset is said to be "modeless."

Range

The range is the difference between the largest and smallest values in the dataset.

# Calculate the range
range_area = max(areas) - min(areas)
print("Range Area:", range_area)

Analyzing the Data

Now that we have calculated the mean, median, mode, and range, we can analyze the data to understand the neighborhood better.

The mean area of the houses in the neighborhood is approximately 2,133.33 square feet.
The median area is 2,100 square feet.
The range of the areas is 1,400 square feet (from 1,750 to 2,150).
The dataset is modeless, meaning there is no value that appears more than once.

Conclusion

In conclusion, the contractor's data collection and analysis have provided valuable insights into the neighborhood. The mean, median, mode, and range have helped us understand the central tendency and variability of the data. This information can be used to make informed decisions about the neighborhood, such as determining the average size of houses, identifying areas of high and low density, and planning for future development.

Recommendations

Based on the analysis, the following recommendations can be made:

The contractor should continue to collect data from a larger sample of houses to get a more accurate representation of the neighborhood.
The data should be analyzed further to identify any patterns or trends that may be relevant to the neighborhood.
The contractor should consider using other statistical measures, such as the standard deviation and variance, to get a more comprehensive understanding of the data.

Future Directions

The analysis of the contractor's data has opened up new avenues for research and exploration. Some potential future directions include:

Analyzing the relationship between the size of houses and their location in the neighborhood.
Examining the impact of zoning regulations on the size and density of houses in the neighborhood.
Investigating the relationship between the size of houses and the socioeconomic characteristics of the residents.

By continuing to collect and analyze data, the contractor can gain a deeper understanding of the neighborhood and make informed decisions about its development.

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Introduction

In our previous article, we analyzed the data collected by a contractor on the areas of a small sample of houses in a neighborhood. We calculated the mean, median, mode, and range of the data and provided recommendations for further analysis. In this article, we will address some of the most frequently asked questions about the data and provide additional insights.

Q&A

Q: What is the purpose of collecting data on house areas?

A: The purpose of collecting data on house areas is to gain a better understanding of the neighborhood and its characteristics. This information can be used to make informed decisions about the neighborhood, such as determining the average size of houses, identifying areas of high and low density, and planning for future development.

Q: How does the mean, median, and mode relate to each other?

A: The mean, median, and mode are all measures of central tendency, but they are calculated differently. The mean is the average of all the values in the dataset, the median is the middle value of the dataset when it is arranged in ascending order, and the mode is the value that appears most frequently in the dataset.

Q: What is the difference between the mean and median?

A: The mean and median can be different because the mean is sensitive to extreme values, while the median is not. In this dataset, the mean is 2,133.33 square feet, while the median is 2,100 square feet. This is because the mean is pulled up by the larger values in the dataset, while the median is not affected by these values.

Q: What is the range of the data?

A: The range of the data is the difference between the largest and smallest values in the dataset. In this case, the range is 1,400 square feet (from 1,750 to 2,150).

Q: Is the dataset modeless?

A: Yes, the dataset is modeless because there is no value that appears more than once.

Q: What are some potential future directions for analysis?

A: Some potential future directions for analysis include:

Analyzing the relationship between the size of houses and their location in the neighborhood.
Examining the impact of zoning regulations on the size and density of houses in the neighborhood.
Investigating the relationship between the size of houses and the socioeconomic characteristics of the residents.

Q: How can the data be used to make informed decisions about the neighborhood?

A: The data can be used to make informed decisions about the neighborhood by:

Determining the average size of houses.
Identifying areas of high and low density.
Planning for future development.