The sales team at D.M. Pan Real Estate Company. I selected the Midwest region for this analysis and generated a simple random sample of 30 listings from the dataset.

• Regression Equation: provide the regression equation for the line of best fit.
• Determiner: Determinerand what it means. (What is the relationship between the variables?)
  • Determine the strength of the correlation (weak, moderate, or strong).
  • Discuss how you determine the direction of the association between the two variables.
    • Is there a positive or negative association?
    • What do you see as the direction of the correlation?
• Examine the Slope and Intercepts: Examine the slope 1 and intercept 0.
• Conclude the slope and intercept in the context of this problem. Does the intercept make sense based on your observation of the line of best fit?
  • Determine the value of the land only. Note: You can assume, when the square footage of the house is zero, that the price is the value of just the land. This happens whenx=0, which is they-intercept. Does this value make sense in context?
• Determine theR-squared Coefficient: Determine theR-squared value.
  • Discuss whatR-squared means in the context of this analysis.
• Conclusions: Reflect on the Relationship: Reflect on the relationship between square feet and sales price by answering the following questions:
  • Is the square footage for homes in your selected region different than for homes overall in the United States?
  • For every 100 square feet, how much does the price go up (i.e., can you use slope to help identify price changes)?
  • What square footage range would the graph be best used for?