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Case_Study_essay - Case Study Housing Prices Quantitative...

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Case Study: Housing Prices Quantitative Methods 201 Samantha Kelly Ryan Scarff 0760906 Section AD Nicolene Solaro 0828203 Section AB
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Introduction The object of this case study is to provide information on housing prices and the different variables that may affect their prices. This information is useful in explaining the housing prices based on variables such as size, location, and features of the house. This data has practical applications to for clients who are interested in the efficient analysis of housing prices when buying or selling a house. In our report, the sample data contains 108 houses and from these houses we compare the price in terms of school district, heating system, and age. Conclusions will then be drawn based off these predictions and presented in an understandable medium. Data Analysis Part A: Application of Descriptive Statistics To start, we have created a histogram to show the frequencies of the selling prices of the houses. The data ranges from 59 to 195 with a median of 92.4695 and an average of 97.993. From this data we can say that the distribution is skewed because the median and average are different. The standard deviation for the data is 26.4295, indicating that there is variability between the individual values and the average. There are many descriptive tools that are useful when interpreting the data. The histogram is one of these tools (See Appendix A). The histogram shows us the frequencies of the selling prices and from this we can find the five number summary. The five number summary includes the sample minimum, lower quartile, the median, the upper quartile, and the sample maximum. In the data from the selling prices the sample minimum is 59, the lower quartile is 79.3, the median is 92.4695, the upper quartile is 112.225, and the sample maximum is 195. Once we break the data up and bring in other variables we can use different tools, such as scatter plots, to interpret the other sets of data.
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