Fix typo

06_StatisticalInference/homework/hw4.Rmd

@@ -250,7 +250,7 @@ The answer is <span class="answer">`r round(power, 3)`</span>
 --- &multitext
 Researchers would like to conduct a study of healthy adults to detect a four year mean brain volume loss of .01 mm3. Assume that the standard deviation of four year volume loss in this population is .04 mm3. 
 
-1. What is necessary sample size for the study for a 5% one sided test versus a null hypothesis of no volume loss to acheive 80% power? (Always round up)
+1. What is necessary sample size for the study for a 5% one sided test versus a null hypothesis of no volume loss to achieve 80% power? (Always round up)
 
 
 

06_StatisticalInference/homework/hw4.html

@@ -399,7 +399,7 @@ <h2>About these slides</h2>
   <p>Researchers would like to conduct a study of healthy adults to detect a four year mean brain volume loss of .01 mm3. Assume that the standard deviation of four year volume loss in this population is .04 mm3. </p>
 
 <ol>
-<li>What is necessary sample size for the study for a 5% one sided test versus a null hypothesis of no volume loss to acheive 80% power? (Always round up)</li>
+<li>What is necessary sample size for the study for a 5% one sided test versus a null hypothesis of no volume loss to achieve 80% power? (Always round up)</li>
 </ol>
 
   <button class="quiz-submit btn btn-primary">Submit</button>

06_StatisticalInference/homework/hw4.md

@@ -254,7 +254,7 @@ The answer is <span class="answer">0.804</span>
 --- &multitext
 Researchers would like to conduct a study of healthy adults to detect a four year mean brain volume loss of .01 mm3. Assume that the standard deviation of four year volume loss in this population is .04 mm3. 
 
-1. What is necessary sample size for the study for a 5% one sided test versus a null hypothesis of no volume loss to acheive 80% power? (Always round up)
+1. What is necessary sample size for the study for a 5% one sided test versus a null hypothesis of no volume loss to achieve 80% power? (Always round up)
 
 
 

07_RegressionModels/03_01_glms/index.Rmd

@@ -103,7 +103,7 @@ $$\sum_{i=1}^n y_i \eta_i =
 \sum_{k=1}^p \beta_k\sum_{i=1}^n X_{ik} y_i
 $$
 Thus if we don't need the full data, only $\sum_{i=1}^n X_{ik} y_i$. This simplification is a consequence of chosing so-called 'canonical' link functions.
-* (This has to be derived). All models acheive their maximum at the root of the so called normal equations
+* (This has to be derived). All models achieve their maximum at the root of the so called normal equations
 $$
 0=\sum_{i=1}^n \frac{(Y_i - \mu_i)}{Var(Y_i)}W_i
 $$

07_RegressionModels/03_01_glms/index.html

@@ -173,7 +173,7 @@ <h2>Some things to note</h2>
 \sum_{k=1}^p \beta_k\sum_{i=1}^n X_{ik} y_i
 \]
 Thus if we don&#39;t need the full data, only \(\sum_{i=1}^n X_{ik} y_i\). This simplification is a consequence of chosing so-called &#39;canonical&#39; link functions.</li>
-<li>(This has to be derived). All models acheive their maximum at the root of the so called normal equations
+<li>(This has to be derived). All models achieve their maximum at the root of the so called normal equations
 \[
 0=\sum_{i=1}^n \frac{(Y_i - \mu_i)}{Var(Y_i)}W_i
 \]

07_RegressionModels/03_01_glms/index.md

@@ -89,7 +89,7 @@ $$\sum_{i=1}^n y_i \eta_i =
 \sum_{k=1}^p \beta_k\sum_{i=1}^n X_{ik} y_i
 $$
 Thus if we don't need the full data, only $\sum_{i=1}^n X_{ik} y_i$. This simplification is a consequence of chosing so-called 'canonical' link functions.
-* (This has to be derived). All models acheive their maximum at the root of the so called normal equations
+* (This has to be derived). All models achieve their maximum at the root of the so called normal equations
 $$
 0=\sum_{i=1}^n \frac{(Y_i - \mu_i)}{Var(Y_i)}W_i
 $$