DB Week #2

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Statistics

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Apr 3, 2024

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DB Week #2 Using the real-estate data provided, I was able to find the mean, median, mode, and standard deviation of both the selling price of the real-estate and the square footage. SELLING PRICE SQUARE FEET MEAN 221,102.86 2,223.81 MEDIAN 213,600 2,200 MODE 188,300 2,100 STANDARD DEVIATION 47,105.40 248.66 There are two ways to describe the quantitative variables provided in the example; measures of location and dispersion (Lind, Marchal, & Wathen, 2019). Measures of location are the averages of numerical data, including the mean, median, and mode of a data set (Lind, Marchal, & Wathen, 2019). In comparison, dispersion measures show the variations in data, including the standard deviation (Lind, Marchal, & Wathen, 2019). The data provided can be used to calculate the distribution of the area of homes that are a part of the real estate. The mode is significantly lower than the mean and median of the two data sets; therefore, it should not be the method used to determine the data's averages. The median selling price is $213,600, and the median of the square feet is 2,200, which falls in-between the mean and the mode, but more so towards the mean. The median is unaffected by significantly higher or lower numbers in the data, which makes it the best method for averaging the data set. “The standard deviation is commonly used as a measure to compare the spread in two or more sets of observations” (Lind, Marchal, & Wathen, 2019, p. 78). The standard deviation of

the selling price compared to the square feet' standard deviation is considerably greater. This more significant deviation is the result of a broader spread of the variables for the selling price. Matthews et al. (2014) state that “one of the primary goals of any statistical procedure is to make quantifiably reliable inferences” (para. 1). In a recent article by the visual and data journalism team for BBC News (2020), there is statistical information in charts spread throughout the article that display percentages of Covid- 19 cases and deaths. The article shows the number of daily Coronavirus cases per continent. According to the graph, it shows that, as of September, Asia has the most cases out of the six continents, with Latin America and the Caribbean following. Researchers presenting statistical data need to make sure that they do so impartially and fairly. Misleading and inaccurate data can be the result of manipulation of data. This is an unethical practice that is sometimes used by businesses to gain unfair advantages over competitors. Ephesians 4:25 says, “Therefore, having put away falsehood, let each one of you speak the truth with his neighbor, for we are members one of another” (ESV). Applying this verse to statistics, we can see that God calls us to be honest with the research we present as we should not mislead one another.

References Lind, D., Marchal, W., & Wathen, S. (2019). Basic statistics for business & economics. McGraw-Hill Education. Matthews, M., Stasny, E., & Wolfe, D. (2014). Two-sample partially sequential median test procedures using ranked set sample data. Journal of Applied Statistical Science, 22 (1), 1- 19. https://search-proquest-com.ezproxy.regent.edu/docview/1864054809? accountid=13479&pq-origsite=summon The Visual and Data Journalism Team. (2020, September 2). Coronavirus pandemic: Tracking the global outbreak. BBC News. https://www.bbc.com/news/world-51235105

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That is precisely right Matthew! I found an interesting article that states, “while numbers don’t lie, they can in-fact be used to mislead with half-truths” (Lebied, 2018, para. 7). Some of the common types of the misuse of statistical data are misleading data visualizations, purposeful and selective bias, data fishing, flawed correlation, faulty polling, and the use of percentage change in combination with small sample size (Lebied, 2018). One example of the misuse of statistics was the 2015 planned parenthood chart that showed the increasing number of abortions in relation to cancer screenings (Lebied, 2018). The chart they released was highly exaggerated and didn’t provide a y-axis, so the data was misplaced on the chart. References Lebied, M. (2018, August 8). Misleading statistical examples – discover the potential for misuse of statistics & data in the digital age. Datapine. https://www.datapine.com/blog/misleading-statistics-and-data/

Hello Emma, I found an article that demonstrates the effects of real estate amenities on the property pricing that you might find beneficial. For example, in Guangzhou, China, the housing value can be affected by “window orientation, green-space view, floor height, proximity to wooded areas and water bodies, and exposure to traffic noise” (Jim & Chen, 2006, p. 1). One example of this is if the property has a garden bordering a body of water, the premium rises by 28% (Jim & Chen, 2006). I am familiar with this as I grew up on the Outer Banks, North Carolina, where a beachside rental raises the price significantly. Other amenities such as pools, hot tubs, movie theaters, and whether it is oceanfront are price raising aspects in a vacation destination. Waterfront property also affects living prices and raises insurance expenses. References Jim, C., Chen, W. (2006, November 28). Impacts of urban environmental elements on residential housing prices in Guangzhou (China). Landscape and Urban Planning, 78 (4), 422-434. https://www.sciencedirect.com/

Hello Matthew, I agree, there is a misuse of graphs and statistical data today. Altman (1980) states that “it is unethical to carry out bad scientific experiments … statistical methods are one aspect of this” (p. 1183). It is misleading to publish statistics that have errors or carry out bad experiments (Altman, 1980). The article states that there can be “56 possible biases that may arise in analytic research” (Altman, 1980, p. 1183). This concept is even more true today as there is easy access to publish publicly viewed data on the internet. References Altman, D. (1980, November 1). Statistics and ethics in medical research. Misuse of statistics is unethical. British Medical Journal. 281 (6249), 1182-1184. https://search-proquest- com.ezproxy.regent.edu/docview/1776137006?pq-origsite=summon

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DB Week #2

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