QM_670_Regression_Problems

# QM_670_Regression_Problems - Discuss the output 3 Repeat...

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QM 670 Regression Problems We are using the following data to build a model to predict house prices. Price Sq.Feet #Rooms #Bedrooms Lot Size 87 1400 6 3 0.5 155 2100 8 4 0.5 148 2400 9 3 3 290 2900 11 5 1 455 3900 14 6 0.8 122 2300 8 3 2 75 1300 6 3 1.5 98 1700 5 2 0.7 100 1650 6 2 0.5 136 2250 8 3 1 149 2140 7 3 1 165 1800 7 3 0.7 210 2170 8 3 2 225 2080 8 4 0.9 140 2600 11 4 1 115 1800 7 3 5 105 1900 8 4 1 138 2260 9 4 8 100 1900 7 3 1 93 1920 6 3 0.8 1. Plot price vs. square footage. Describe the relationship present in the model. 2. Obtain regression output for a model using only square footage to predict price.
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Unformatted text preview: Discuss the output. 3. Repeat steps 1 and 2 for the other three explanatory variables. 4. Obtain a correlation matrix for the five variables. Discuss the matrix. 5. Obtain regression output for the overall model to predict price. Discuss. 6. Is multicollinearity present? Explain. 7. Which variable(s) should be considered for omission? Explain....
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## This note was uploaded on 12/02/2011 for the course QM 670 taught by Professor Dr.keeney during the Fall '11 term at Jefferson College.

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