update

doc/pub/week9/html/._week9-bs017.html

@@ -661,7 +661,7 @@ <h2 id="blocking-transformations" class="anchor">Blocking transformations </h2>
 \( \boldsymbol{X}_i \) is a stationary time series for all \( 0 \leq i \leq d-1 \)
 </p>
 
-<p>We can then compute the autocovariance, the variance, sample mean, and
+<p>We can then compute the autocovariance (or just covariance), the variance, sample mean, and
 number of observations for each \( i \). 
 Let \( \gamma_i, \sigma_i^2,
 \overline{X}_i \) denote the covariance, variance and average of the

doc/pub/week9/html/week9-reveal.html

@@ -564,7 +564,7 @@ <h2 id="blocking-transformations">Blocking transformations </h2>
 \( \boldsymbol{X}_i \) is a stationary time series for all \( 0 \leq i \leq d-1 \)
 </p>
 
-<p>We can then compute the autocovariance, the variance, sample mean, and
+<p>We can then compute the autocovariance (or just covariance), the variance, sample mean, and
 number of observations for each \( i \). 
 Let \( \gamma_i, \sigma_i^2,
 \overline{X}_i \) denote the covariance, variance and average of the

doc/pub/week9/html/week9-solarized.html

@@ -865,7 +865,7 @@ <h2 id="blocking-transformations">Blocking transformations </h2>
 \( \boldsymbol{X}_i \) is a stationary time series for all \( 0 \leq i \leq d-1 \)
 </p>
 
-<p>We can then compute the autocovariance, the variance, sample mean, and
+<p>We can then compute the autocovariance (or just covariance), the variance, sample mean, and
 number of observations for each \( i \). 
 Let \( \gamma_i, \sigma_i^2,
 \overline{X}_i \) denote the covariance, variance and average of the

doc/pub/week9/html/week9.html

@@ -942,7 +942,7 @@ <h2 id="blocking-transformations">Blocking transformations </h2>
 \( \boldsymbol{X}_i \) is a stationary time series for all \( 0 \leq i \leq d-1 \)
 </p>
 
-<p>We can then compute the autocovariance, the variance, sample mean, and
+<p>We can then compute the autocovariance (or just covariance), the variance, sample mean, and
 number of observations for each \( i \). 
 Let \( \gamma_i, \sigma_i^2,
 \overline{X}_i \) denote the covariance, variance and average of the