1.            Develop a simple linear regression model to predict the price of ahouse based upon the living area (square feet) using a 95% level ofconfidence.

1.            Write the reqression equation
2.            Discuss the statistical significance of the model as a whole usingthe appropriate regression statistic at a 95% level ofconfidence.
3.             Discuss the statistical significance of the coefficient for theindependent variable using the appropriate regression statistic ata 95% level of confidence.
4.            Interpret the coefficient for the independent variable.
5.            What percentage of the observed variation in housing prices isexplained by the model?
6.             Predict the value of a house with 3,000 square feet of livingarea.

2.            Develop a simple linear regression model to predict the price of ahouse based upon the number of bedrooms using a 95% level ofconfidence.

1.            Write the reqression equation
2.            Discuss the statistical significance of the model as a whole usingthe appropriate regression statistic at a 95% level ofconfidence.
3.             Discuss the statistical significance of the coefficient for theindependent variable using the appropriate regression statistic ata 95% level of confidence.
4.            Interpret the coefficient for the independent variable.
5.            What percentage of the observed variation in housing prices isexplained by the model?
6.             Predict the value of a house with 3 bedrooms.

3.            Develop a simple linear regression model to predict the price of ahouse based upon the number of bathrooms using a 95% level ofconfidence.

1.            Write the reqression equation
2.            Discuss the statistical significance of the model as a whole usingthe appropriate regression statistic at a 95% level ofconfidence.
3.             Discuss the statistical significance of the coefficient for theindependent variable using the appropriate regression statistic ata 95% level of confidence.
4.            Interpret the coefficient for the independent variable.
5.            What percentage of the observed variation in housing prices isexplained by the model?
6.             Predict the value of a house with 2.5 bathrooms.

4.            Develop a simple linear regression model to predict the price of ahouse based upon its age using a 95% level of confidence.

1.            Write the reqression equation
2.            Discuss the statistical significance of the model as a whole usingthe appropriate regression statistic at a 95% level ofconfidence.
3.             Discuss the statistical significance of the coefficient for theindependent variable using the appropriate regression statistic ata 95% level of confidence.
4.            Interpret the coefficient for the independent variable.
5.            What percentage of the observed variation in housing prices isexplained by the model?
6.             Predict the value of a house that is 50 years old.

5.            Compare the preceding four simple linear regression models todetermine which model is the preferred model. Use the SignificanceF values, p-values for independent variable coefficients, R-squaredor Adjusted R-squared values (as appropriate), and standard errorsto explain your selection.
6.            Calculate the predicted sale price of a 50 year old house with3,000 square feet of living area, 3 bedrooms, and 2.5 bathroomsusing your preferred regression model from part 5.

Prepare a single Microsoft Excel file, using a separateworksheet for each regression model, to document your regressionanalyses. Prepare a single Microsoft Word document that outlinesyour responses for each portions of the case study.

Selling Price        Age(Years)         Living Area(Sq Feet)       No. Bathrooms NoBedrooms

$92,000                18          1,527    2             4

$211,002             0             2,195    2.5          4

$115,000             14          1,480    1.5          3

$113,000             53          1,452    2             3

$216,300             0             2,360    2.5          4

$145,000             32          1,440    1             3

$114,000             14          1,480    2.5          2

$139,050             125        1,879    2.5          3

$104,000             14          1,480    1.5          3

$169,900             11          1,792    2.5          3

$177,900             2             1,386    2.5          3

$133,000             14          1,676    2             2

$185,000             0             768        2             4

$115,000             16          1,560    1.5          3

$100,000             91          1,000    1             3

$117,000             15          1,676    1.5          4

$150,000             11          1,656    1.5          3

$187,500             11          2,300    1.5          3

$107,000             25          1,712    1             3

$126,900             26          1,350    1.5          3

$147,000             15          1,676    2.5          3

$62,000                103        1,317    1.5          3

$101,000             30          1,056    2             3

$143,500             13          912        1             3

$113,400             18          1,232    2             2

$112,000             36          1,280    1             3

$112,500             43          1,232    1             3

$97,000                45          1,406    1.5          3

$121,000             6             1,164    2             3

$65,720                123        1,198    1             3

$225,000             10          2,206    2.5          4