tweak

diff --git a/problem_sets/week_06/ps6-worked-solution.Rmd b/problem_sets/week_06/ps6-worked-solution.Rmd
index 986a9a0147f27890d9b6c2925e9898f9204666e2..23d9a7726b4915ca70c9abd533e43532da52a1bb 100644 (file)
--- 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
 author: "Jeremy Foote"
 date: "April 11, 2019"
 output: html_document


@@ -11,15 +12,18 @@ knitr::opts_chunk$set(echo = TRUE, messages = F)
 
 ## Programming Questions
 
 
 ## 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)
 ```
 
 
 ```{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)
 
 ```{r}
 library(tidyverse)


@@ -40,7 +44,9 @@ df$dose <- as.factor(df$dose)
 df <- df[complete.cases(df),]
 ```
 
 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}
 
 
 ```{r}
 


@@ -103,9 +109,9 @@ The global mean is
 mean(df$weeks_alive)
 ```
 
 mean(df$weeks_alive)
 ```
 

+### PC3  

 
 

-PC3. Anova
-
+Anova! 

 ```{r}
 summary(aov(weeks_alive ~ dose, data = df))
 
 ```{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.
 
 
 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}
 
 
 ```{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.
 
 
 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. 
 ## Statistical Questions
 
 Q1.