erratas: 3.15, 4.2

ch3/applied.Rmd

@@ -609,7 +609,7 @@ x = c(coefficients(lm.zn)[2],
 y = coefficients(lm.all)[2:14]
 plot(x, y)
 ```
-Coefficient for nox is approximately -10 in univariate model and 31 in
+Coefficient for nox is approximately 31 in univariate model and -10 in
 multiple regression model.
 
 15d.

ch4/2.Rmd

@@ -33,7 +33,7 @@ $$
 \geq
 \pi_i \exp (x \frac {\mu_i} {\sigma^2} - \frac {\mu_i^2} {2 \sigma^2})
 $$
-Multipy this inequality by the positive constant
+Multiply this inequality by the positive constant
 $$
 c = \frac {
                 \frac {1} {\sqrt{2 \pi} \sigma}
@@ -47,7 +47,6 @@ c = \frac {
 $$
 and we have that
 $$
-
 \frac {\pi_k
                 \frac {1} {\sqrt{2 \pi} \sigma}
                 \exp(- \frac {1} {2 \sigma^2} (x - \mu_k)^2)
@@ -57,9 +56,7 @@ $$
                 \frac {1} {\sqrt{2 \pi} \sigma}
                 \exp(- \frac {1} {2 \sigma^2} (x - \mu_l)^2)
                }}
-
 \geq
-
 \frac {\pi_i
                 \frac {1} {\sqrt{2 \pi} \sigma}
                 \exp(- \frac {1} {2 \sigma^2} (x - \mu_i)^2)
@@ -70,4 +67,4 @@ $$
                 \exp(- \frac {1} {2 \sigma^2} (x - \mu_l)^2)
                }}
 $$
-or equivalently, $f_k(x) \geq f_i(x)$. Reversing these steps also holds, so we have that maximizing $\delta_k$ is equivalent to maximizing $p_k$.
+or equivalently, $f_k(x) \geq f_i(x)$. Reversing these steps also holds, so we have that maximizing $\delta_k$ is equivalent to maximizing $p_k$.

ch4/2.md

@@ -33,7 +33,7 @@ $$
 \geq
 \pi_i \exp (x \frac {\mu_i} {\sigma^2} - \frac {\mu_i^2} {2 \sigma^2})
 $$
-Multipy this inequality by the positive constant
+Multiply this inequality by the positive constant
 $$
 c = \frac {
                 \frac {1} {\sqrt{2 \pi} \sigma}
@@ -57,9 +57,7 @@ $$
                 \frac {1} {\sqrt{2 \pi} \sigma}
                 \exp(- \frac {1} {2 \sigma^2} (x - \mu_l)^2)
                }}
-
 \geq
-
 \frac {\pi_i
                 \frac {1} {\sqrt{2 \pi} \sigma}
                 \exp(- \frac {1} {2 \sigma^2} (x - \mu_i)^2)