Added another fix and ackowledgment to jcornickm.

index.qmd

@@ -25,8 +25,8 @@ A special thanks to my tidyverse guru David Robinson and Amy Gill for dozens of
 This book was conceived during the teaching of several applied statistics courses, starting over fifteen years ago. The teaching assistants working with me throughout the years made important indirect contributions to this book. The latest iteration of this course is a HarvardX series coordinated by Heather Sternshein and Zofia Gajdos. We thank them for their contributions. We are also grateful to all the students whose questions and comments helped us improve the book. The courses were partially funded by NIH grant R25GM114818. We are very grateful to the National Institutes of Health for its support.
 
 A special thanks goes to all those who edited the book via GitHub pull requests or made suggestions by creating an _issue_ or sending an email:  `nickyfoto` (Huang Qiang), `desautm` (Marc-André Désautels), `michaschwab` (Michail Schwab), `alvarolarreategui` (Alvaro Larreategui), `jakevc` (Jake VanCampen), `omerta` (Guillermo Lengemann), `espinielli` (Enrico Spinielli), `asimumba`(Aaron Simumba), `braunschweig` (Maldewar), `gwierzchowski` (Grzegorz Wierzchowski), `technocrat` (Richard Careaga), `atzakas`, `defeit` (David Emerson Feit), `shiraamitchell` (Shira Mitchell),  `Nathalie-S`, `andreashandel` (Andreas Handel), `berkowitze` (Elias Berkowitz), `Dean-Webb` (Dean Webber), `mohayusuf`, `jimrothstein`, `mPloenzke` (Matthew Ploenzke), `NicholasDowand` (Nicholas Dow), `kant` (Darío Hereñú), `debbieyuster` (Debbie Yuster), `tuanchauict` (Tuan Chau), `phzeller`, `BTJ01` (BradJ), `glsnow` (Greg Snow), `mberlanda` (Mauro Berlanda), `wfan9`, `larswestvang` (Lars Westvang), `jj999` (Jan Andrejkovic), `Kriegslustig` (Luca Nils Schmid), `odahhani`, `aidanhorn` (Aidan Horn), `atraxler` (Adrienne Traxler), `alvegorova`,`wycheong` (Won Young Cheong), 
-`med-hat` (Medhat Khalil), `biscotty666` (Brian Carey), `kengustafson`, `Yowza63`, `ryan-heslin` (Ryan Heslin), `raffaem`, `tim8west`,
-David D. Kane, El Mustapha El Abbassi, Vadim Zipunnikov, Anna Quaglieri, Chris Dong, Rick Schoenberg, Isabella Grabski, and Doug Snyder.
+`med-hat` (Medhat Khalil), `biscotty666` (Brian Carey), `kengustafson`, `Yowza63`, `ryan-heslin` (Ryan Heslin), `raffaem`, `tim8west`, `jcornickm`, 
+David D. Kane, El Mustapha El Abbassi, Vadim Zipunnikov, Anna Quaglieri, Chris Dong, Rick Schoenberg, Isabella Grabski, Doug Snyder, and JT Harton.
 
 
 

inference/models.qmd

@@ -357,7 +357,7 @@ b.  It is a random variable with expected value $\mu$ and standard error $\sigma
 c.  It is a random variable with expected value $\mu$ and standard error $\sigma$.
 d.  Contains no information.
 
-4\. So, how is this useful? We are going to use an oversimplified yet illustrative example. Suppose we want to know the average height of our male students, but we can only measure 50 of the 708. We will use $\bar{X}$ as our estimate. We know from the answer to exercise 3 that the standard estimate of our error $\bar{X}-\mu$ is $\sigma/\sqrt{N}$. We want to compute this, but we don't know $\sigma$. Based on what is described in this section, show your estimate of $\sigma$.
+4\. So, how is this useful? We are going to use an oversimplified yet illustrative example. Suppose we want to know the average height of our male students, but we can only measure 50 of the 708. We will use $\bar{X}$ as our estimate. We know from the answer to exercise 3 that the standard estimate of our estimate $\bar{X}-\mu$ is $\sigma/\sqrt{N}$. We want to compute this, but we don't know $\sigma$. Based on what is described in this section, show your estimate of $\sigma$.
 
 5\. Now that we have an estimate of $\sigma$, let's call our estimate $s$. Construct a 95% confidence interval for $\mu$.