Scatter Beds: A Case Study

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As shown in Table 2, the correlation coefficient between price and square foot is 0.686, indicating that they have a positive relationship. Larger homes tend to have a higher price per sq. ft. The correlation coefficient between price and beds is 0.294, meaning that they have a positive but not strong relationship. Also, there are positive but not strong relationships between price and baths, between price and garage, between price and pool, between price and number of fireplaces, between price and days the home was on the market for sale, and between price and location. On the other hand, the correlation coefficient between price and age is -0.424, indicating that they have a negative relationship. Older property is less expensive than newer …show more content…
As shown in the scatter plot, the trendline indicates that larger homes are more likely to be sold at higher price. Thus, the result of this plot is consistent with the correlation results in (2).

Figure 2: Scatter Plot depicting the relationship between price and the number of baths

As shown in Figure 2, there is a positive relationship between sales price and the number of baths. Thus, the result of this plot is consistent with the correlation results in (2).

Figure 3 Scatter Plot depicting the relationship between price and age

As shown in Figure 3, there is a negative relationship between price and age of properties. It means that new homes cost more than older homes. Thus, the result of this plot is consistent with the correlation results in (2).

Figure 4 Scatter Plot depicting the relationship between price and days the home was on the market for sale

As shown in Figure 4, there is a positive but very weak relationship between price and DOM. Thus, the result of this plot is consistent with the correlation results in (2).

Part II: Contrasting Characteristics of homes in Summerlin and in Henderson

Table 3: Descriptive Statistics (Average Values)
Variable Summerlin

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