A sample consists of 321 observations, where the unit of observation is a house.

Question

A sample consists of 321 observations, where the unit of observation is a house. price is the price of the house (in dollars), and dist is the distance from the house to the incinerator in miles. Given the information found in the attached STATA output

1. A sample consists of 321 observations, where the unit of observation is a house. price

is the price of the house (in dollars), and dist is the distance from the house to the

incinerator in miles. Given the information found in the attached STATA output,

(a) The population model implied by the regression output is

(b) The (population) parameter of interest is

(c) The estimator of the parameter of interest is

(d) The estimate of the parameter of interest is

(e) Interpret the estimate

(f) In the space below, provide a variable that could possibly affect your dependent variable, but was not accounted for in the simple linear regression model (i.e. is in the u term). Make sure to clearly define your variable and briefly explain why you believe it affects your dependent variable.

2 · sum price distance Variable Obs Mean Std. Dev Min Max price distance 321 321 96100.66 3.923405 43223.73 1.611398 300000 9469697 7.575758 26000 3gen lprice-1n (price) 4 reg lprice distance 321 Number of obs PI 1, 319)28.08 Prob > F R-squared Adj R-squared0.0780 Source df MS Model Residual 1 4.97031174 .97031174 56.4686736 319177017786 -0.0000 a 0.0809 Total 61.4389853 320.191996029 Root MSE 42073 price Coet. Std. Err. P 95 Cont. Interval] dietance cons 0773417 11.07468 0145959 5.30 0.000 0618935 178.93 0.000 0486254 10.9529 1060581 11.19645

Explanation / Answer

a) The regression model is :

Multiple Regression Model is : Y = a0 + a1*X1+ a2*X2 + ............+ an*Xn + ei

Where Y = Response variable (Dependent Variable ) = Price of house

X = Predictor Variable ( Independent Variable ) = Distance

ei = it is error term follows normal distribution with mean 0 and constant variable.

Our Model : Price of House = 11.07468 + (0.0773417) * Distance.

b) The (population) parameter of interest is Price of House.

c) The estimator of the parameter of interest is Distance.

d) Estimates: Intercept = 11.07468

Regression coefficient of Distance = 0.0773417

e) When distance of house is increases from city or main centre then price of house decreases in multiple of 0.0773417

f) Our Dependent variable is Y = Price of house. and independent variable would be,

X1 = Distance from city

X2 = Age of House.

X3 = Size of house (in squre feet )

X4 = type of House.

The above factor would be important variable in this model because when age of house is high and size is small then price of house is go dowen.

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