>  mdata <-read.table("housing.csv", header=T, sep=",")
> y <-mdata[,1]
> dt <-mdata[,2]
> d2 <-mdata[,3]
> dtd2 <-mdata[,4]
> x1 <-mdata[,5]
> x2 <-mdata[,6]
> x3 <-mdata[,7]
> x4 <-mdata[,8]
> x5 <-mdata[,9]
> x6 <-mdata[,10]
> ols <-lm(y~dt+d2+dtd2+x1+x2+x3+x4+x5+x6)
> summary(ols)

Call:
lm(formula = y ~ dt + d2 + dtd2 + x1 + x2 + x3 + x4 + x5 + x6)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.11271 -0.10441  0.02652  0.12032  0.67874 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  6.626e+00  6.147e-01  10.780  < 2e-16 ***
dt           7.124e-02  6.746e-02   1.056 0.292651    
d2           4.491e-01  5.360e-02   8.379 3.54e-14 ***
dtd2        -1.637e-01  7.863e-02  -2.082 0.039046 *  
x1          -1.010e-02  2.055e-03  -4.915 2.30e-06 ***
x2           4.904e-05  1.441e-05   3.404 0.000852 ***
x3          -2.444e-02  5.611e-02  -0.436 0.663787    
x4           3.869e-02  2.957e-02   1.308 0.192782    
x5           4.491e-01  7.020e-02   6.397 1.91e-09 ***
x6           1.199e-01  3.829e-02   3.132 0.002088 ** 
---
Signif. codes:  0 �e***�f 0.001 �e**�f 0.01 �e*�f 0.05 �e.�f 0.1 �e �f 1

Residual standard error: 0.2373 on 150 degrees of freedom
Multiple R-squared:  0.7564,    Adjusted R-squared:  0.7418 
F-statistic: 51.76 on 9 and 150 DF,  p-value: < 2.2e-16

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