DataScienceSpecialization · drakulic · Aug 11, 2014 · Aug 11, 2014 · Aug 15, 2014
diff --git a/06_StatisticalInference/.nojekyll b/06_StatisticalInference/.nojekyll
diff --git a/06_StatisticalInference/01_01_Introduction/index.Rmd b/06_StatisticalInference/01_01_Introduction/index.Rmd
@@ -155,4 +155,4 @@ addressing missing data.
   1. The epidemiological literature is very focused on using study design to investigate public health.
   2. The classical development of study design in agriculture broadly covers design and design principles.
   3. The industrial quality control literature covers design thoroughly.
- 
+
diff --git a/06_StatisticalInference/01_01_Introduction/index.html b/06_StatisticalInference/01_01_Introduction/index.html
diff --git a/06_StatisticalInference/01_01_Introduction/index.md b/06_StatisticalInference/01_01_Introduction/index.md
@@ -155,4 +155,4 @@ addressing missing data.
   1. The epidemiological literature is very focused on using study design to investigate public health.
   2. The classical development of study design in agriculture broadly covers design and design principles.
   3. The industrial quality control literature covers design thoroughly.
- 
+
diff --git a/06_StatisticalInference/01_02_Probability/assets/fig/unnamed-chunk-1.png b/06_StatisticalInference/01_02_Probability/assets/fig/unnamed-chunk-1.png
diff --git a/06_StatisticalInference/01_02_Probability/assets/fig/unnamed-chunk-2.png b/06_StatisticalInference/01_02_Probability/assets/fig/unnamed-chunk-2.png
diff --git a/06_StatisticalInference/01_02_Probability/figure/unnamed-chunk-1.png b/06_StatisticalInference/01_02_Probability/figure/unnamed-chunk-1.png
diff --git a/06_StatisticalInference/01_02_Probability/figure/unnamed-chunk-2.png b/06_StatisticalInference/01_02_Probability/figure/unnamed-chunk-2.png
diff --git a/06_StatisticalInference/01_02_Probability/figure/unnamed-chunk-4.png b/06_StatisticalInference/01_02_Probability/figure/unnamed-chunk-4.png
diff --git a/06_StatisticalInference/01_02_Probability/figure/unnamed-chunk-6.png b/06_StatisticalInference/01_02_Probability/figure/unnamed-chunk-6.png
diff --git a/06_StatisticalInference/01_02_Probability/index.Rmd b/06_StatisticalInference/01_02_Probability/index.Rmd
@@ -274,4 +274,3 @@ qbeta(0.5, 2, 1)
   discussed is the **population median**.
 - A probability model connects the data to the population using assumptions.
 - Therefore the median we're discussing is the **estimand**, the sample median will be the **estimator**
-
diff --git a/06_StatisticalInference/01_02_Probability/index.html b/06_StatisticalInference/01_02_Probability/index.html
@@ -309,7 +309,7 @@ <h2>Example</h2>
 plot(x, y, lwd = 3, frame = FALSE, type = &quot;l&quot;)
 </code></pre>
 
-<p><img src="assets/fig/unnamed-chunk-1.png" alt="plot of chunk unnamed-chunk-1"> </p>
+<p><img src="assets/fig/unnamed-chunk-1.png" title="plot of chunk unnamed-chunk-1" alt="plot of chunk unnamed-chunk-1" style="display: block; margin: auto;" /></p>
 
   </article>
   <!-- Presenter Notes -->
@@ -322,7 +322,7 @@ <h2>Example continued</h2>
   <article data-timings="">
     <p>What is the probability that 75% or fewer of calls get addressed?</p>
 
-<p><img src="assets/fig/unnamed-chunk-2.png" alt="plot of chunk unnamed-chunk-2"> </p>
+<p><img src="assets/fig/unnamed-chunk-2.png" title="plot of chunk unnamed-chunk-2" alt="plot of chunk unnamed-chunk-2" style="display: block; margin: auto;" /></p>
 
   </article>
   <!-- Presenter Notes -->

diff --git a/06_StatisticalInference/01_02_Probability/index.md b/06_StatisticalInference/01_02_Probability/index.md
@@ -191,7 +191,7 @@ y <- c(0, 0, 2, 0, 0)
 plot(x, y, lwd = 3, frame = FALSE, type = "l")
 ```
 
-![plot of chunk unnamed-chunk-1](assets/fig/unnamed-chunk-1.png) 
+<img src="assets/fig/unnamed-chunk-1.png" title="plot of chunk unnamed-chunk-1" alt="plot of chunk unnamed-chunk-1" style="display: block; margin: auto;" />
 
 
 ---
@@ -200,7 +200,7 @@ plot(x, y, lwd = 3, frame = FALSE, type = "l")
 
 What is the probability that 75% or fewer of calls get addressed?
 
-![plot of chunk unnamed-chunk-2](assets/fig/unnamed-chunk-2.png) 
+<img src="assets/fig/unnamed-chunk-2.png" title="plot of chunk unnamed-chunk-2" alt="plot of chunk unnamed-chunk-2" style="display: block; margin: auto;" />
 
 
 ---
@@ -308,4 +308,3 @@ qbeta(0.5, 2, 1)
   discussed is the **population median**.
 - A probability model connects the data to the population using assumptions.
 - Therefore the median we're discussing is the **estimand**, the sample median will be the **estimator**
-
diff --git a/06_StatisticalInference/01_03_Expectations/assets/fig/lsm.png b/06_StatisticalInference/01_03_Expectations/assets/fig/lsm.png
diff --git a/06_StatisticalInference/01_03_Expectations/assets/fig/unnamed-chunk-1.png b/06_StatisticalInference/01_03_Expectations/assets/fig/unnamed-chunk-1.png
diff --git a/06_StatisticalInference/01_03_Expectations/assets/fig/unnamed-chunk-2.png b/06_StatisticalInference/01_03_Expectations/assets/fig/unnamed-chunk-2.png
diff --git a/06_StatisticalInference/01_03_Expectations/assets/fig/unnamed-chunk-3.png b/06_StatisticalInference/01_03_Expectations/assets/fig/unnamed-chunk-3.png
diff --git a/06_StatisticalInference/01_03_Expectations/figure/galton.png b/06_StatisticalInference/01_03_Expectations/figure/galton.png
diff --git a/06_StatisticalInference/01_03_Expectations/figure/lsm.png b/06_StatisticalInference/01_03_Expectations/figure/lsm.png
diff --git a/06_StatisticalInference/01_03_Expectations/figure/unnamed-chunk-1.png b/06_StatisticalInference/01_03_Expectations/figure/unnamed-chunk-1.png
diff --git a/06_StatisticalInference/01_03_Expectations/figure/unnamed-chunk-2.png b/06_StatisticalInference/01_03_Expectations/figure/unnamed-chunk-2.png
diff --git a/06_StatisticalInference/01_03_Expectations/figure/unnamed-chunk-3.png b/06_StatisticalInference/01_03_Expectations/figure/unnamed-chunk-3.png
diff --git a/06_StatisticalInference/01_03_Expectations/figure/unnamed-chunk-31.png b/06_StatisticalInference/01_03_Expectations/figure/unnamed-chunk-31.png
diff --git a/06_StatisticalInference/01_03_Expectations/figure/unnamed-chunk-32.png b/06_StatisticalInference/01_03_Expectations/figure/unnamed-chunk-32.png
diff --git a/06_StatisticalInference/01_03_Expectations/index.Rmd b/06_StatisticalInference/01_03_Expectations/index.Rmd
diff --git a/06_StatisticalInference/01_03_Expectations/index.html b/06_StatisticalInference/01_03_Expectations/index.html
@@ -69,7 +69,10 @@ <h2>Example</h2>
   <article data-timings="">
     <h3>Find the center of mass of the bars</h3>
 
-<p><img src="assets/fig/unnamed-chunk-1.png" alt="plot of chunk unnamed-chunk-1"> </p>
+<pre><code>## Loading required package: MASS
+</code></pre>
+
+<p><img src="assets/fig/unnamed-chunk-1.png" title="plot of chunk unnamed-chunk-1" alt="plot of chunk unnamed-chunk-1" style="display: block; margin: auto;" /></p>
 
   </article>
   <!-- Presenter Notes -->
@@ -105,7 +108,7 @@ <h2>The center of mass is the empirical mean</h2>
 lines(rep(meanChild, 100), seq(0, 150, length = 100), col = &quot;red&quot;, lwd = 5)
 </code></pre>
 
-<p><img src="assets/fig/lsm.png" alt="plot of chunk lsm"> </p>
+<p><img src="assets/fig/lsm.png" title="plot of chunk lsm" alt="plot of chunk lsm" style="display: block; margin: auto;" /></p>
 
   </article>
   <!-- Presenter Notes -->
@@ -125,7 +128,7 @@ <h2>Example</h2>
 <li>Note, if thought about geometrically, this answer is obvious; if two equal weights are spaced at 0 and 1, the center of mass will be \(.5\)</li>
 </ul>
 
-<p><img src="assets/fig/unnamed-chunk-2.png" alt="plot of chunk unnamed-chunk-2"> </p>
+<p><img src="assets/fig/unnamed-chunk-2.png" title="plot of chunk unnamed-chunk-2" alt="plot of chunk unnamed-chunk-2" style="display: block; margin: auto;" /></p>
 
   </article>
   <!-- Presenter Notes -->

diff --git a/06_StatisticalInference/01_03_Expectations/index.md b/06_StatisticalInference/01_03_Expectations/index.md
@@ -27,7 +27,12 @@ mode        : selfcontained # {standalone, draft}
 
 ## Example
 ### Find the center of mass of the bars
-![plot of chunk unnamed-chunk-1](assets/fig/unnamed-chunk-1.png) 
+
+```
+## Loading required package: MASS
+```
+
+<img src="assets/fig/unnamed-chunk-1.png" title="plot of chunk unnamed-chunk-1" alt="plot of chunk unnamed-chunk-1" style="display: block; margin: auto;" />
 
 
 ---
@@ -53,7 +58,7 @@ meanChild <- mean(galton$child)
 lines(rep(meanChild, 100), seq(0, 150, length = 100), col = "red", lwd = 5)
 ```
 
-![plot of chunk lsm](assets/fig/lsm.png) 
+<img src="assets/fig/lsm.png" title="plot of chunk lsm" alt="plot of chunk lsm" style="display: block; margin: auto;" />
 
 
 ---
@@ -66,7 +71,7 @@ lines(rep(meanChild, 100), seq(0, 150, length = 100), col = "red", lwd = 5)
     $$
 - Note, if thought about geometrically, this answer is obvious; if two equal weights are spaced at 0 and 1, the center of mass will be $.5$
 
-![plot of chunk unnamed-chunk-2](assets/fig/unnamed-chunk-2.png) 
+<img src="assets/fig/unnamed-chunk-2.png" title="plot of chunk unnamed-chunk-2" alt="plot of chunk unnamed-chunk-2" style="display: block; margin: auto;" />
 
 ---
 

diff --git a/06_StatisticalInference/01_04_Independence/assets/fig/unnamed-chunk-2.png b/06_StatisticalInference/01_04_Independence/assets/fig/unnamed-chunk-2.png
diff --git a/06_StatisticalInference/01_04_Independence/figure/unnamed-chunk-1.png b/06_StatisticalInference/01_04_Independence/figure/unnamed-chunk-1.png
diff --git a/06_StatisticalInference/01_04_Independence/index.Rmd b/06_StatisticalInference/01_04_Independence/index.Rmd
diff --git a/06_StatisticalInference/01_04_Independence/index.html b/06_StatisticalInference/01_04_Independence/index.html
diff --git a/06_StatisticalInference/01_04_Independence/index.md b/06_StatisticalInference/01_04_Independence/index.md
diff --git a/06_StatisticalInference/01_05_ConditionalProbability/index.Rmd b/06_StatisticalInference/01_05_ConditionalProbability/index.Rmd
diff --git a/06_StatisticalInference/01_05_ConditionalProbability/index.html b/06_StatisticalInference/01_05_ConditionalProbability/index.html
diff --git a/06_StatisticalInference/01_05_ConditionalProbability/index.md b/06_StatisticalInference/01_05_ConditionalProbability/index.md
diff --git a/06_StatisticalInference/02_01_CommonDistributions/fig/unnamed-chunk-1.png b/06_StatisticalInference/02_01_CommonDistributions/fig/unnamed-chunk-1.png
diff --git a/06_StatisticalInference/02_01_CommonDistributions/fig/unnamed-chunk-3.png b/06_StatisticalInference/02_01_CommonDistributions/fig/unnamed-chunk-3.png
diff --git a/06_StatisticalInference/02_01_CommonDistributions/fig/unnamed-chunk-4.png b/06_StatisticalInference/02_01_CommonDistributions/fig/unnamed-chunk-4.png
diff --git a/06_StatisticalInference/02_01_CommonDistributions/figure/unnamed-chunk-1.png b/06_StatisticalInference/02_01_CommonDistributions/figure/unnamed-chunk-1.png
diff --git a/06_StatisticalInference/02_01_CommonDistributions/index.Rmd b/06_StatisticalInference/02_01_CommonDistributions/index.Rmd
@@ -13,22 +13,7 @@ url:
 widgets     : [mathjax]            # {mathjax, quiz, bootstrap}
 mode        : selfcontained # {standalone, draft}
 ---
-```{r setup, cache = F, echo = F, message = F, warning = F, tidy = F, results='hide'}
-# make this an external chunk that can be included in any file
-options(width = 100)
-opts_chunk$set(message = F, error = F, warning = F, comment = NA, fig.align = 'center', dpi = 100, tidy = F, cache.path = '.cache/', fig.path = 'fig/')
-
-options(xtable.type = 'html')
-knit_hooks$set(inline = function(x) {
-  if(is.numeric(x)) {
-    round(x, getOption('digits'))
-  } else {
-    paste(as.character(x), collapse = ', ')
-  }
-})
-knit_hooks$set(plot = knitr:::hook_plot_html)
-runif(1)
-```
+
 
 ## The Bernoulli distribution
 
@@ -189,7 +174,7 @@ plot(pvals, dbinom(7, 8, pvals) / dbinom(7, 8, 7/8) ,
 - Standard normal RVs are often labeled $Z$
 
 ---
-```{r, fig.height=4.5, fig.width=4.5}
+```{r, fig.height=5, fig.width=5, fig.align='center', results='hide'}
 zvals <- seq(-3, 3, length = 1000)
 plot(zvals, dnorm(zvals), 
      type = "l", lwd = 3, frame = FALSE, xlab = "z", ylab = "Density")

diff --git a/06_StatisticalInference/02_01_CommonDistributions/index.html b/06_StatisticalInference/02_01_CommonDistributions/index.html
@@ -105,7 +105,7 @@ <h2>Plotting all possible likelihoods for a small n</h2>
 
 <slide class="" id="slide-4" style="background:;">
   <article data-timings="">
-    <div class="rimage center"><img src="fig/unnamed-chunk-1.png" title="plot of chunk unnamed-chunk-1" alt="plot of chunk unnamed-chunk-1" class="plot" /></div>
+    <p><img src="assets/fig/unnamed-chunk-1.png" alt="plot of chunk unnamed-chunk-1"> </p>
 
   </article>
   <!-- Presenter Notes -->
@@ -215,16 +215,16 @@ <h2>Example</h2>
 \]</li>
 </ul>
 
-<pre><code class="r">choose(8, 7) * .5 ^ 8 + choose(8, 8) * .5 ^ 8 
+<pre><code class="r">choose(8, 7) * 0.5^8 + choose(8, 8) * 0.5^8
 </code></pre>
 
-<pre><code>[1] 0.03516
+<pre><code>## [1] 0.03516
 </code></pre>
 
-<pre><code class="r">pbinom(6, size = 8, prob = .5, lower.tail = FALSE)
+<pre><code class="r">pbinom(6, size = 8, prob = 0.5, lower.tail = FALSE)
 </code></pre>
 
-<pre><code>[1] 0.03516
+<pre><code>## [1] 0.03516
 </code></pre>
 
   </article>
@@ -233,11 +233,11 @@ <h2>Example</h2>
 
 <slide class="" id="slide-9" style="background:;">
   <article data-timings="">
-    <pre><code class="r">plot(pvals, dbinom(7, 8, pvals) / dbinom(7, 8, 7/8) , 
-     lwd = 3, frame = FALSE, type = &quot;l&quot;, xlab = &quot;p&quot;, ylab = &quot;likelihood&quot;)
+    <pre><code class="r">plot(pvals, dbinom(7, 8, pvals)/dbinom(7, 8, 7/8), lwd = 3, frame = FALSE, type = &quot;l&quot;, 
+    xlab = &quot;p&quot;, ylab = &quot;likelihood&quot;)
 </code></pre>
 
-<div class="rimage center"><img src="fig/unnamed-chunk-3.png" title="plot of chunk unnamed-chunk-3" alt="plot of chunk unnamed-chunk-3" class="plot" /></div>
+<p><img src="assets/fig/unnamed-chunk-3.png" alt="plot of chunk unnamed-chunk-3"> </p>
 
   </article>
   <!-- Presenter Notes -->
@@ -267,34 +267,11 @@ <h2>The normal distribution</h2>
 <slide class="" id="slide-11" style="background:;">
   <article data-timings="">
     <pre><code class="r">zvals &lt;- seq(-3, 3, length = 1000)
-plot(zvals, dnorm(zvals), 
-     type = &quot;l&quot;, lwd = 3, frame = FALSE, xlab = &quot;z&quot;, ylab = &quot;Density&quot;)
-sapply(-3 : 3, function(k) abline(v = k))
+plot(zvals, dnorm(zvals), type = &quot;l&quot;, lwd = 3, frame = FALSE, xlab = &quot;z&quot;, ylab = &quot;Density&quot;)
+sapply(-3:3, function(k) abline(v = k))
 </code></pre>
 
-<div class="rimage center"><img src="fig/unnamed-chunk-4.png" title="plot of chunk unnamed-chunk-4" alt="plot of chunk unnamed-chunk-4" class="plot" /></div>
-
-<pre><code>[[1]]
-NULL
-
-[[2]]
-NULL
-
-[[3]]
-NULL
-
-[[4]]
-NULL
-
-[[5]]
-NULL
-
-[[6]]
-NULL
-
-[[7]]
-NULL
-</code></pre>
+<p><img src="assets/fig/unnamed-chunk-4.png" title="plot of chunk unnamed-chunk-4" alt="plot of chunk unnamed-chunk-4" style="display: block; margin: auto;" /></p>
 
   </article>
   <!-- Presenter Notes -->
@@ -538,7 +515,7 @@ <h2>Example</h2>
 <pre><code class="r">ppois(3, lambda = 2.5 * 4)
 </code></pre>
 
-<pre><code>[1] 0.01034
+<pre><code>## [1] 0.01034
 </code></pre>
 
   </article>
@@ -554,16 +531,16 @@ <h2>Example, Poisson approximation to the binomial</h2>
 
 <p>What&#39;s the probability of 2 or fewer successes?</p>
 
-<pre><code class="r">pbinom(2, size = 500, prob = .01)
+<pre><code class="r">pbinom(2, size = 500, prob = 0.01)
 </code></pre>
 
-<pre><code>[1] 0.1234
+<pre><code>## [1] 0.1234
 </code></pre>
 
-<pre><code class="r">ppois(2, lambda=500 * .01)
+<pre><code class="r">ppois(2, lambda = 500 * 0.01)
 </code></pre>
 
-<pre><code>[1] 0.1247
+<pre><code>## [1] 0.1247
 </code></pre>
 
   </article>

diff --git a/06_StatisticalInference/02_01_CommonDistributions/index.md b/06_StatisticalInference/02_01_CommonDistributions/index.md
@@ -15,7 +15,6 @@ mode        : selfcontained # {standalone, draft}
 ---
 
 
-
 ## The Bernoulli distribution
 
 - The **Bernoulli distribution** arises as the result of a binary outcome
@@ -55,7 +54,7 @@ title(paste("Likelihoods for n = ", n))
 ```
 
 ---
-<div class="rimage center"><img src="fig/unnamed-chunk-1.png" title="plot of chunk unnamed-chunk-1" alt="plot of chunk unnamed-chunk-1" class="plot" /></div>
+![plot of chunk unnamed-chunk-1](assets/fig/unnamed-chunk-1.png) 
 
 
 ---
@@ -139,30 +138,30 @@ $$\left(
 $$
 
 ```r
-choose(8, 7) * .5 ^ 8 + choose(8, 8) * .5 ^ 8 
+choose(8, 7) * 0.5^8 + choose(8, 8) * 0.5^8
 ```
 
 ```
-[1] 0.03516
+## [1] 0.03516
 ```
 
 ```r
-pbinom(6, size = 8, prob = .5, lower.tail = FALSE)
+pbinom(6, size = 8, prob = 0.5, lower.tail = FALSE)
 ```
 
 ```
-[1] 0.03516
+## [1] 0.03516
 ```
 
 
 ---
 
 ```r
-plot(pvals, dbinom(7, 8, pvals) / dbinom(7, 8, 7/8) , 
-     lwd = 3, frame = FALSE, type = "l", xlab = "p", ylab = "likelihood")
+plot(pvals, dbinom(7, 8, pvals)/dbinom(7, 8, 7/8), lwd = 3, frame = FALSE, type = "l", 
+    xlab = "p", ylab = "likelihood")
 ```
 
-<div class="rimage center"><img src="fig/unnamed-chunk-3.png" title="plot of chunk unnamed-chunk-3" alt="plot of chunk unnamed-chunk-3" class="plot" /></div>
+![plot of chunk unnamed-chunk-3](assets/fig/unnamed-chunk-3.png) 
 
 
 ---
@@ -183,35 +182,11 @@ plot(pvals, dbinom(7, 8, pvals) / dbinom(7, 8, 7/8) ,
 
 ```r
 zvals <- seq(-3, 3, length = 1000)
-plot(zvals, dnorm(zvals), 
-     type = "l", lwd = 3, frame = FALSE, xlab = "z", ylab = "Density")
-sapply(-3 : 3, function(k) abline(v = k))
+plot(zvals, dnorm(zvals), type = "l", lwd = 3, frame = FALSE, xlab = "z", ylab = "Density")
+sapply(-3:3, function(k) abline(v = k))
 ```
 
-<div class="rimage center"><img src="fig/unnamed-chunk-4.png" title="plot of chunk unnamed-chunk-4" alt="plot of chunk unnamed-chunk-4" class="plot" /></div>
-
-```
-[[1]]
-NULL
-
-[[2]]
-NULL
-
-[[3]]
-NULL
-
-[[4]]
-NULL
-
-[[5]]
-NULL
-
-[[6]]
-NULL
-
-[[7]]
-NULL
-```
+<img src="assets/fig/unnamed-chunk-4.png" title="plot of chunk unnamed-chunk-4" alt="plot of chunk unnamed-chunk-4" style="display: block; margin: auto;" />
 
 
 ---
@@ -352,7 +327,7 @@ ppois(3, lambda = 2.5 * 4)
 ```
 
 ```
-[1] 0.01034
+## [1] 0.01034
 ```
 
 
@@ -365,19 +340,19 @@ What's the probability of 2 or fewer successes?
 
 
 ```r
-pbinom(2, size = 500, prob = .01)
+pbinom(2, size = 500, prob = 0.01)
 ```
 
 ```
-[1] 0.1234
+## [1] 0.1234
 ```
 
 ```r
-ppois(2, lambda=500 * .01)
+ppois(2, lambda = 500 * 0.01)
 ```
 
 ```
-[1] 0.1247
+## [1] 0.1247
 ```
 
 
diff --git a/06_StatisticalInference/02_02_Asymptopia/assets/fig/unnamed-chunk-1.png b/06_StatisticalInference/02_02_Asymptopia/assets/fig/unnamed-chunk-1.png
diff --git a/06_StatisticalInference/02_02_Asymptopia/assets/fig/unnamed-chunk-2.png b/06_StatisticalInference/02_02_Asymptopia/assets/fig/unnamed-chunk-2.png
diff --git a/06_StatisticalInference/02_02_Asymptopia/assets/fig/unnamed-chunk-3.png b/06_StatisticalInference/02_02_Asymptopia/assets/fig/unnamed-chunk-3.png
diff --git a/06_StatisticalInference/02_02_Asymptopia/fig/unnamed-chunk-1.png b/06_StatisticalInference/02_02_Asymptopia/fig/unnamed-chunk-1.png
diff --git a/06_StatisticalInference/02_02_Asymptopia/fig/unnamed-chunk-2.png b/06_StatisticalInference/02_02_Asymptopia/fig/unnamed-chunk-2.png
diff --git a/06_StatisticalInference/02_02_Asymptopia/fig/unnamed-chunk-3.png b/06_StatisticalInference/02_02_Asymptopia/fig/unnamed-chunk-3.png
Original file line number	Diff line number	Diff line change
Expand Up		@@ -155,4 +155,4 @@ addressing missing data.
		1. The epidemiological literature is very focused on using study design to investigate public health.
		2. The classical development of study design in agriculture broadly covers design and design principles.
		3. The industrial quality control literature covers design thoroughly.
Original file line number	Diff line number	Diff line change
Expand Up		@@ -274,4 +274,3 @@ qbeta(0.5, 2, 1)
		discussed is the population median.
		- A probability model connects the data to the population using assumptions.
		- Therefore the median we're discussing is the estimand, the sample median will be the estimator