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Statistical Analyses

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Y = ?0 +?1X1 +?2X2 +?

Price = 35591.507 + (4.001 * Block Size) + (3088.933 * Floor Area) + 177444.648

The dependent variable Price can be predicted from a linear combination of the independent variables:

P

Block Size

Floor Area

All independent variables appear to contribute to predicting Price (P < 0.05).

Y =?0 +?1X1 +?2X2 +?3X3 +?4X4 + (2)

Price = -52451.132 + (4.108 * Block Size) + (2354.550 * Floor Area) + (0.990 * Land Value) + (2940.910 * Rooms) + 159418.866

The dependent variable Price can be predicted from a linear combination of the independent variables:

P

Block Size

Floor Area

Land Value

Rooms

Not all of the independent variables appear necessary (or the multiple linear model may be underspecified).

The following appear to account for the ability to predict Price (P < 0.05): Block Size, Floor Area, Land Value

Y = ?0 + ?1X1 + ?2X2 + ?3X3 + ?4X4 +? A +?1 D1 + ?2D2 +?

(3)

Price = -50248.225 + (4.619 * Block Size) + (2221.843 * Floor Area) + (0.950 * Land Value) + (3855.113 * Rooms) + (51888.205 * Central Air) + (31167.050 * D1) - (1380.805 * D2) + 157689.673

The dependent variable Price can be predicted from a linear combination of the independent variables:

P

Block Size 0.002

Floor Area

Land Value

Rooms 0.111

Central Air

D1 0.173

D2 0.883

Not all of the independent variables appear necessary (or the multiple linear model may be underspecified).

The following appear to account for the ability to predict Price (P < 0.05): Block Size, Floor Area, Land Value, Central Air

c) The dummy variables are useful for representing qualitative variables in order to have meaningful interpretations of their effects on the dependent variable in regression analysis. They also give a prediction of the dependent variable with minimal error and bias.

d) The coefficient of D2 indicates that, if the property is between 1km and 5km of the coast, price decreases by $1,380.805.

e) Partial effect of D2

Ho: 2 = 0; H1: 2-0 (To test determine if D2 has non-zero regression coefficient. If it does not, (i.e 2 = 0), then D2 is dropped from the regression analysis). From the excel sheet for "Y = ?0 + ?1X1 + ?2X2 + ?3X3 + ?4X4 +? A +?1 D1 + ?2D2 +?

(3)," since the p-value for D2 (0.888) is greater than the significance level (0.05), we fail to reject the null hypothesis and hence D2 should not be dropped from the regression analysis.

i) Using the price equation in 3) block size=900m2, floor area = 180m2, land value = $140,000, rooms = 7, D1= 0 and D2 = 1, The price of the property is:

Sheet4

SUMMARY OUTPUT

Regression Statistics

Multiple R. 0.7765801252

R Square 0.6030766909