From: aaronshaw <aaron.d.shaw@gmail.com>
Date: Mon, 6 May 2019 02:51:05 +0000 (-0500)
Subject: tweak
X-Git-Url: https://code.communitydata.science/stats_class_2019.git/commitdiff_plain/50161569aabb14f5eda4e9a0643ee27f6843dac7?hp=d77163d23d116e6069bb89ce9be8c37d20d6d022

tweak
---

diff --git a/problem_sets/week_06/ps6-worked-solution.Rmd b/problem_sets/week_06/ps6-worked-solution.Rmd
index 986a9a0..23d9a77 100644
--- a/problem_sets/week_06/ps6-worked-solution.Rmd
+++ b/problem_sets/week_06/ps6-worked-solution.Rmd
@@ -1,5 +1,6 @@
 ---
-title: "Week 6 Worked Examples"
+title: 'Week 6 problem set: Worked solutions'
+subtitle: "Statistics and statistical programming  \nNorthwestern University  \nMTS 525"
 author: "Jeremy Foote"
 date: "April 11, 2019"
 output: html_document
@@ -11,15 +12,18 @@ knitr::opts_chunk$set(echo = TRUE, messages = F)
 
 ## Programming Questions
 
-PC0. First we import the data.
+### PC0  
+
+First we import the data.
 
 ```{r}
 raw_df = read.csv("~/Desktop/DeleteMe/Teaching/owan03.csv") # Note that I saved the file as a CSV for importing to R
 head(raw_df)
 ```
 
-PC1. Let's reshape the data
+### PC1
 
+Let's reshape the data
 
 ```{r}
 library(tidyverse)
@@ -40,7 +44,9 @@ df$dose <- as.factor(df$dose)
 df <- df[complete.cases(df),]
 ```
 
-PC2: Now we're goint to get statistics and create some visualizations
+### PC2  
+
+Now we're going to calculate summary statistics and create some visualizations
 
 ```{r}
 
@@ -103,9 +109,9 @@ The global mean is
 mean(df$weeks_alive)
 ```
 
+### PC3  
 
-PC3. Anova
-
+Anova! 
 ```{r}
 summary(aov(weeks_alive ~ dose, data = df))
 
@@ -113,7 +119,9 @@ summary(aov(weeks_alive ~ dose, data = df))
 
 This provides evidence that the group means are different.
 
-PC4. T-test between None and Any, and between None and High.
+### PC4
+
+T-test between None and Any, and between None and High.
 
 ```{r}
 
@@ -143,7 +151,6 @@ These t-tests both support the idea that receiving a dose of RD40 reduces lifesp
 
 The Bonferroni correction is more conservative than it needs to be, ane there are other approaches; for example,  the `TukeyHSD` function takes in an anova result and does post-hoc comparisons with corrections for all of the groups.
 
-
 ## Statistical Questions
 
 Q1.