*Regression Equation: Provide the regression equation for the line of best fit using the scatterplot from the Module Two assignment
*Determine r: Determine r and 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 is see as the direction of the correlation?
Examine the Slope and Intercepts: Examine the slope b1
and intercept b0.
Draw conclusions from 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 when x=0, which is the y-intercept.
Determine the R-squared Coefficient: Determine the R-squared value.
*Discuss what R-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)?
*Use the regression equation to estimate how much you would list your house for if it was 1,200 square feet.
*What square footage range would the graph be best used for?
*Also use the MAT-240 module three assignment
-attached is an example of how the assignment is suppose to look and detailed.
