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Real estate regression exercise qnt 351 version 5 complete solutions correct answers key
1. University of Phoenix Material
Real Estate Regression Exercise QNT/351 Version 5 complete solutions correct
answers key
Find the answer at
http://www.coursemerit.com/solution-details/14929/Real-Estate-Regression-ExerciseQNT351-Version-5-complete-
solutions-correct-answers-key
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)?
The independent variable (x) is the square footage and the dependent variable
(y) is the listing price.
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?
2. The scatterplot does not indicate the presence of observable correlation as all
the plotted points are not very close together and not in the form of a straight
line.
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?
3. (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?