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.

1.   Which variable is the independent variable (x)
and which is the dependent variable (y)? 

2. 
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?

3. 
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”. 

(a)  What
is the Coefficient of Correlation between square footage and listing
price? 

(b)  Does
your Coefficient of Correlation seem consistent with your answer to #2
above?  Why or why not? 

(c)  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?

(d)  Check
the coefficients in your summary output. What is the regression equation
relating square footage to listing price?

(e)  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?

(f)  Using
the regression equation that you designated in #3(d) above, what is the
predicted sales price for a house of 2100 square feet?