What is the probability the two estimates of residual variance for the two models are equal? I fitted two models using linear regression in r:
Analysis of Variance Table
Response: price
             Df     Sum Sq    Mean Sq F value    Pr(>F)    
sqft_living   1 1.5130e+13 1.5130e+13 393.784 < 2.2e-16 ***
grade         1 1.2981e+12 1.2981e+12  33.786 1.585e-08 ***
Residuals   297 1.1411e+13 3.8421e+10                      
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Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Analysis of Variance Table

Response: price
             Df     Sum Sq    Mean Sq F value    Pr(>F)    
sqft_living   1 1.5130e+13 1.5130e+13  354.75 < 2.2e-16 ***
Residuals   298 1.2709e+13 4.2649e+10                      
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Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

What is the probability the two estimates of residual variance for the two models are equal? 
I guess if the second variable have no effect on the regression, then I may have similar results. But how could I have calculate such a probability.
 A: Since the estimates of residual variance are constants, the probability of them being equal is $0$ (if they're not equal), or $1$ (if they're equal).
But you could ask whether the new variable in a linear model has a predictive force, and you could accomplish that using $\bar R^2$ (adjusted coefficient of determination) that measures the amount of variance explained by the model, but unlike $R^2$ (coefficient of determination), it only increases if residual variance goes down more than one would expect by random chance.
You can calculate $\bar R^2$ as
$\bar R^2 = 1-(1-R^2)\frac{n-1}{n-p-1}$, where $p$ is the number of independent variables (for your first model, $p = 2$ and for your second model $p = 1$) and $n$ is the sample size (the number of data points).
You calculate $R^2$ as
$R^2 = 1-\frac{\displaystyle\sum_{i=1}^n(y_i-f_i)^2}{\displaystyle\sum_{i=1}^n(y_i-\bar y)^2}$, where $y_i$ is the value of the dependent variable in the i-th data point (in your models, that's i-th price) and $f_i$ is the the estimate of the dependent variable from the i-th data point (in your models, that's whatever the model predicts the i-th price would be).
