Directions: Use the real estate data you used for your Week 2 learning team assignment. Analyze the data and explain your answers.
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?