Linn She took a ionship between selling sgesf bouse is de City. variable variabl

Question

Linn She took a ionship between selling sgesf bouse is de City. variable variable mple of price and de tize and reoneded the prices On 2 houses from those sold in the last month, and Below (in square feet de noted by wwiable r) nd ages (in years. sample data and output of de revessen analysis Age House Price size Age 2 3548 2,600 16 2365 3 396.8 3.630 12 y450 2.12 298.4 2020 9 3293 860 62 2 1,830 39 10 280 1.480 45 263.3 1,420 310.8 1,908 32 318.0 2.000 34 12 310 soo 36 SUMMARY OUTPUT ssion Statistics Multiple R R Square Adjusted R square Standard Error ANOVA icance F MS Regression 2 19706.9627 9853.4814 431.1481 1.15703E-09 205.6865 22.8541 Residual 19912.6492 Coefficients Standard Error Stat P-value Lower 95% Upper 95% 329,987 17.5750 18.7759 1,58368E-08 290 2297 3697446 t 0.0240 0.0050 4.8138 0.0009 0,0128 0 0.2326 0832 2.03772E-05 .2.4062

Explanation / Answer

Answers to each part in detail:
a.

Estimated regression equation is :
Price = 329.9871 +.0240Size +1.8801Age

b.

THat interprestation is :
If size increases by 1 unit . price increases by .0240 units
If Age increases by 1 years, price increases by 1.8801 units

c.

Or R2 square can be intreprested as "the amountof varinace of Price (depdedent variable) that can be explained by the varinales size and Age"
Here, its a near perfect R2, with as much as .99% ( .9897) explained by these 2 variables)

d. THis is price = 329.9871 +.0240*1600 +1.8801*15 = $396.589

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