• This presentation analyzes the relationship between square footage (2000 - 3000) and home prices in ZIP code 85249.
• A total of 28 observations were collected.
• The data were collected from Zillow.

The relationship between home price and square footage can be analyzed using the linear regression equation:

\[ y = \beta_0 + \beta_1 x \]

Where:

• \(y\) = price
• \(\beta_0\) = intercept (price when square footage is 0)
  
• \(\beta_1\) = slope (change in price for each additional square foot)
  
• \(x\) = square footage

The estimated regression equation is:

\[ \hat{y} = \beta_0 + \beta_1 x \]

Where:

• \(\hat{y}\) = predicted home price
  
• \(\beta_0\) = estimated intercept
  
• \(\beta_1\) = estimated slope
• \(x\) = square footage

plot_ly(homes,
        x = homes$sqft,
        y = homes$price,
        type = "scatter",
        mode = "markers" )%>%
  layout(xaxis = list(title = "Square Footage"),
         yaxis = list(title = "Home Price ($)"))

• There appears to be a positive relationship between square footage and home price.
• Homes with larger square footage have higher prices.