diff --git a/ch3/applied.Rmd b/ch3/applied.Rmd
index e5e0554..df905c8 100644
--- a/ch3/applied.Rmd
+++ b/ch3/applied.Rmd
@@ -408,7 +408,7 @@ suggests that there isn't a relationship between y and $x^2$.
 ----
 ```{r}
 set.seed(1)
-eps1 = rnorm(100, 0, 0.125)
+eps1 = rnorm(100, 0, sqrt(0.125))
 x1 = rnorm(100)
 y1 = -1 + 0.5*x1 + eps1
 plot(x1, y1)
@@ -424,7 +424,7 @@ As expected, the error observed in $R^2$ and $RSE$ decreases considerably.
 ----
 ```{r}
 set.seed(1)
-eps2 = rnorm(100, 0, 0.5)
+eps2 = rnorm(100, 0, sqrt(0.5))
 x2 = rnorm(100)
 y2 = -1 + 0.5*x2 + eps2
 plot(x2, y2)
diff --git a/ch3/applied.html b/ch3/applied.html
index e43bad0..b4a2ff4 100644
--- a/ch3/applied.html
+++ b/ch3/applied.html
@@ -743,7 +743,7 @@ <h2>13g.</h2>
 <div id="h.-1" class="section level2">
 <h2>13h.</h2>
 <pre class="r"><code>set.seed(1)
-eps1 = rnorm(100, 0, 0.125)
+eps1 = rnorm(100, 0, sqrt(0.125))
 x1 = rnorm(100)
 y1 = -1 + 0.5*x1 + eps1
 plot(x1, y1)
@@ -754,19 +754,19 @@ <h2>13h.</h2>
 ## lm(formula = y1 ~ x1)
 ## 
 ## Residuals:
-##      Min       1Q   Median       3Q      Max 
-## -0.29052 -0.07545  0.00067  0.07288  0.28665 
+##     Min      1Q  Median      3Q     Max 
+## -0.8217 -0.2134  0.0019  0.2061  0.8108 
 ## 
 ## Coefficients:
 ##             Estimate Std. Error t value Pr(&gt;|t|)    
-## (Intercept)  -0.9864     0.0113   -87.3   &lt;2e-16 ***
-## x1            0.4999     0.0118    42.2   &lt;2e-16 ***
+## (Intercept)  -0.9615     0.0319   -30.1   &lt;2e-16 ***
+## x1            0.4997     0.0335    14.9   &lt;2e-16 ***
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
-## Residual standard error: 0.113 on 98 degrees of freedom
-## Multiple R-squared:  0.948,  Adjusted R-squared:  0.947 
-## F-statistic: 1.78e+03 on 1 and 98 DF,  p-value: &lt;2e-16</code></pre>
+## Residual standard error: 0.319 on 98 degrees of freedom
+## Multiple R-squared:  0.694,	Adjusted R-squared:  0.691 
+## F-statistic:  223 on 1 and 98 DF,  p-value: &lt;2e-16</code></pre>
 <pre class="r"><code>abline(lm.fit1, lwd=3, col=2)
 abline(-1, 0.5, lwd=3, col=3)
 legend(-1, legend = c(&quot;model fit&quot;, &quot;pop. regression&quot;), col=2:3, lwd=3)</code></pre>
@@ -775,7 +775,7 @@ <h2>13h.</h2>
 <div id="i.-2" class="section level2">
 <h2>13i.</h2>
 <pre class="r"><code>set.seed(1)
-eps2 = rnorm(100, 0, 0.5)
+eps2 = rnorm(100, 0, sqrt(0.5))
 x2 = rnorm(100)
 y2 = -1 + 0.5*x2 + eps2
 plot(x2, y2)
@@ -787,18 +787,21 @@ <h2>13i.</h2>
 ## 
 ## Residuals:
 ##     Min      1Q  Median      3Q     Max 
-## -1.1621 -0.3018  0.0027  0.2915  1.1466 
+## -1.6434 -0.4268  0.0038  0.4123  1.6215 
 ## 
 ## Coefficients:
 ##             Estimate Std. Error t value Pr(&gt;|t|)    
-## (Intercept)  -0.9456     0.0452   -20.9   &lt;2e-16 ***
-## x2            0.4995     0.0474    10.6   &lt;2e-16 ***
+## (Intercept)  -0.9230     0.0639  -14.45  &lt; 2e-16 ***
+## x2            0.4993     0.0670    7.46  3.6e-11 ***
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 ## 
-## Residual standard error: 0.451 on 98 degrees of freedom
-## Multiple R-squared:  0.532,  Adjusted R-squared:  0.527 
-## F-statistic:  111 on 1 and 98 DF,  p-value: &lt;2e-16</code></pre>
+## Residual standard error: 0.638 on 98 degrees of freedom
+## Multiple R-squared:  0.362,	Adjusted R-squared:  0.355 
+## F-statistic: 55.6 on 1 and 98 DF,  p-value: 3.62e-11</code></pre>
+
+
+
 <pre class="r"><code>abline(lm.fit2, lwd=3, col=2)
 abline(-1, 0.5, lwd=3, col=3)
 legend(-1, legend = c(&quot;model fit&quot;, &quot;pop. regression&quot;), col=2:3, lwd=3)</code></pre>
@@ -812,12 +815,12 @@ <h2>13j.</h2>
 ## x            0.3926  0.6064</code></pre>
 <pre class="r"><code>confint(lm.fit1)</code></pre>
 <pre><code>##               2.5 %  97.5 %
-## (Intercept) -1.0088 -0.9640
-## x1           0.4764  0.5234</code></pre>
+## (Intercept) -1.0249 -0.8981
+## x1           0.4332  0.5661</code></pre>
 <pre class="r"><code>confint(lm.fit2)</code></pre>
 <pre><code>##               2.5 %  97.5 %
-## (Intercept) -1.0352 -0.8559
-## x2           0.4055  0.5935</code></pre>
+## (Intercept) -1.0498 -0.7963
+## x2           0.3664  0.6323</code></pre>
 <p>All intervals seem to be centered on approximately 0.5, with the second fit’s interval being narrower than the first fit’s interval and the last fit’s interval being wider than the first fit’s interval.</p>
 </div>
 <div id="a.-6" class="section level2">