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Evaluation: Asking Price and Square Footage.
Outlier .
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The graph above indicated a statistical straight line with the equation yi = bo + b1 xi + ei . .
Where "e- is the error, differentiating a statistical straight line from a mathematical straight line. Nevertheless no curvature could be observed here. But an outlier was present at the interception y= 220,000 and x= 4,000, which is house 16. However, this would not affect the representative characteristic of this particular sample if this house did not remain an outlier in following graphs.
Evaluation: Asking Price and Time on Market (days).
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This graph was a perfect example for a random scatter. There is no evidence for a relationship or curvature. Nevertheless, this x figure might be questionable. Why should the days on Market affect the Asking or Selling Price? But Amy was thoroughly convinced that we have to reduce Asking Price as well as our Selling Price once we realized that our house was not immediately demanded on the market. .
Evaluation: Asking Price and Number of Rooms.
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This graph too did not show any curvature.
Evaluation: Asking Price and Number of Fireplaces.
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Obviously, these values (AP and Number of Fireplaces) do not plot a curvature in the graph.
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Evaluation: Relationship between Asking Price and Number of Bathrooms.
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The concentration of data points at each value of "x- was linearly increasing. Thus, between the dependent variable and the independent variable there were neither curvature nor outlying points.
Evaluation: Asking Price and Heating system.
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The plot between Asking Price and Heating system did not show curvature and will be considered in developing a forecasting model for the Asking Price.
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Evaluation: Asking Price and City .
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This graph depicted the price differences between houses located in two different cities. The average asking price for a house in City Washington D.C. (x=0) was $ 163,259 while the average asking price of a house in City Chicago (x=1) was $ 127,864.