How to find residual variance of a linear regression model in R?

The residual variance is the variance of the values that are calculated by finding the distance between regression line and the actual points, this distance is actually called the residual. Suppose we have a linear regression model named as Model then finding the residual variance can be done as (summary(Model)$sigma)**2.
x1<-rnorm(500,5,1)
y1<-rnorm(500,5,2)
Model1<-lm(y1~x1)
summary(Model1)
lm(formula = y1 ~ x1)
Min 1Q Median 3Q Max
-5.6621 -1.2257 -0.0272 1.4151 6.6421
Estimate Std. Error t value Pr(>|t|) (Intercept) 5.12511 0.46364 11.054 <2e-16 *** x1 -0.01077 0.09120 -0.118 0.906 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error − 1.966 on 498 degrees of freedom
Multiple R-squared − 2.798e-05, Adjusted R-squared: -0.00198
F-statistic − 0.01393 on 1 and 498 DF, p-value: 0.9061
Finding the residual variance of the model −
(summary(Model1)$sigma)**2 [1] 3.863416
x2<-rpois(5000,5)
y2<-rpois(5000,2)
Model2<-lm(y2~x2)
summary(Model2)
lm(formula = y2 ~ x2)
  Min 1Q Median 3Q Max
-2.0474 -0.9898 0.0030 1.0102 6.0318
Estimate Std. Error t value Pr(>|t|) (Intercept) 1.953861 0.049840 39.203 <2e-16 *** x2 0.007192 0.009125 0.788 0.431 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error − 1.423 on 4998 degrees of freedom
Multiple R-squared − 0.0001243, Adjusted R-squared: -7.578e-05
F-statistic − 0.6212 on 1 and 4998 DF, p-value: 0.4306
(summary(Model2)$sigma)**2 [1] 2.024254
x3<-runif(5000,2,5)
y3<-runif(5000,2,10)
Model3<-lm(y3~x3)
summary(Model3)
lm(formula = y3 ~ x3)
   Min 1Q    Median 3Q   Max
-3.9879 -2.0642 0.0001 2.0438 4.0109
Estimate Std. Error t value Pr(>|t|) (Intercept) 6.004373 0.136367 44.031 <2e-16 *** x3 -0.004376 0.037914 -0.115 0.908 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error − 2.334 on 4998 degrees of freedom
Multiple R-squared − 2.666e-06, Adjusted R-squared: -0.0001974
F-statistic − 0.01332 on 1 and 4998 DF, p-value: 0.9081
(summary(Model3)$sigma)**2 [1] 5.445925
x4<-rexp(100000,5.5)
y4<-rexp(100000,7.5)
Model4<-lm(y4~x4)
summary(Model4)
lm(formula = y4 ~ x4)
  Min 1Q Median 3Q Max
-0.13359 -0.09515 -0.04089 0.05144 1.39856
Estimate Std. Error t value Pr(>|t|) (Intercept) 0.1335960 0.0005972 223.697 <2e-16 *** x4 -0.0010954 0.0023153 -0.473 0.636 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error − 0.1335 on 99998 degrees of freedom
Multiple R-squared − 2.239e-06, Adjusted R-squared : -7.762e-06
F-statistic − 0.2239 on 1 and 99998 DF, p-value: 0.6361
(summary(Model4)$sigma)**2 [1] 0.01781908
x5<-sample(0:9,25000,replace=TRUE)
y5<-sample(91:99,25000,replace=TRUE) Model5<-lm(y5~x5)
summary(Model5)
lm(formula = y5 ~ x5)
  Min 1Q Median 3Q Max
-3.9949 -1.9937 0.0075 2.0093 4.0105
Estimate Std. Error t value Pr(>|t|) (Intercept) 94.989520 0.030168 3148.693 <2e-16 *** x5 0.000595 0.005641 0.105 0.916 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error − 2.57 on 24998 degrees of freedom
Multiple R-squared − 4.45e-07, Adjusted R-squared : -3.956e-05
F-statistic − 0.01112 on 1 and 24998 DF, p-value − 0.916
(summary(Model5)$sigma)**2 
[1] 6.604745