Real Estate Regression Exercise statistics homework help

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Directions: Use the real estate data you used for your Week 2 learning team assignment. Analyze the data and explain your answers. (WILL PROVIDE DATA)

You are consulting for a large real estate firm.You have been asked to construct a model that can predict listing prices based on square footages for homes in the city you’ve been researching.You have data on square footages and listing prices for 100 homes.

  • Which variable is the independent variable (x) and which is the dependent variable (y)?
  • Click on any cell.Click on Insert→Scatter→Scatter with markers (upper left).

To add a trendline, click Tools→Layout→Trendline→Linear Trendline

Does the scatterplot indicate observable correlation?If so, does it seem to be strong or weak?

In what direction?

  • Click on Data→Data Analysis→Regression→OK.Highlight your data (including your two headings) and input the correct columns into Input Y Range and Input X Range, respectively.Make sure to check the box entitled “Labels”.
  • What is the Coefficient of Correlation between square footage and listing price?
  • Does your Coefficient of Correlation seem consistent with your answer to #2 above?Why or why not?
  • What proportion of the variation in listing price is determined by variation in the square footage?What proportion of the variation in listing price is due to other factors?
  • Check the coefficients in your summary output. What is the regression equation relating square footage to listing price?
  • Test the significance of the slope. What is your t-value for the slope?Do you conclude that there is no significant relationship between the two variables or do you conclude that there is a significant relationship between the variables?
  • Using the regression equation that you designated in #3(d) above, what is the predicted sales price for a house of 2100 square feet?
 
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