preface: added some "paths" for getting through the book

changes for V2: Mine to read / edit / expand (I wasn't sure if I should say "and we also fixed all the typos")

changes_v2.qmd

@@ -0,0 +1,15 @@
+# Changes for 2nd edition
+
+Welcome to the second edition of "Introduction to Modern Statistics"!
+While the second edition does not represent a major change from the first edition, we have worked hard to improve content, to add exercises, and to update text and code to reflect changes in best practices (e.g., the book is now written in [Quarto](https://quarto.org/)).
+
+A brief summary of the biggest changes follows:
+
+* Twenty-five completely new exercises were added.  Most of the new exercises are concatenated onto existing exercises so as to retain similar numbering across editions.  However, a few exercises have been shuffled so as to produce both odd exercises (with solutions) and even exercises (without solutions) on the same topic.
+
+* Multiple datasets were added or updated.  For example, 
+the [`pm25_2022_durham`](http://openintrostat.github.io/openintro/reference/pm25_2022_durham.html) data on air quality in Durham, NC in 2022 can be found in the [**openintro**](https://openintrostat.github.io/openintro/) R package.
+
+* @sec-data-applications was re-written with an updated context and data example.  Additionally, in @sec-data-applications, we explore Simpson's Paradox.
+
+* Throughout the text and the exercises, "statistically significant" has been changed to "statistically discernible" so as to distance ourselves from the more colloquial use of the word "significant."

preface.qmd

@@ -8,16 +8,28 @@ We hope readers will take away three ideas from this book in addition to forming
 
 #### Textbook overview {.unnumbered}
 
--   **Part 1: Introduction to data.** Data structures, variables, summaries, graphics, and basic data collection and study design techniques.
--   **Part 2: Exploratory data analysis.** Data visualization and summarization, with particular emphasis on multivariable relationships.
--   **Part 3: Regression modeling.** Modeling numerical and categorical outcomes with linear and logistic regression and using model results to describe relationships and make predictions.
--   **Part 4: Foundations for inference.** Case studies are used to introduce the ideas of statistical inference with randomization tests, bootstrap intervals, and mathematical models.
--   **Part 5: Statistical inference.** Further details of statistical inference using randomization tests, bootstrap intervals, and mathematical models for numerical and categorical data.
--   **Part 6: Inferential modeling.** Extending inference techniques presented thus-far to linear and logistic regression settings and evaluating model performance.
+-   [**Part 1: Introduction to data.**](https://openintro-ims2.netlify.app/introduction-to-data) Data structures, variables, summaries, graphics, and basic data collection and study design techniques.
+-   [**Part 2: Exploratory data analysis.**](https://openintro-ims2.netlify.app/exploratory-data-analysis) Data visualization and summarization, with particular emphasis on multivariable relationships.
+-   [**Part 3: Regression modeling.**](https://openintro-ims2.netlify.app/regression-modeling) Modeling numerical and categorical outcomes with linear and logistic regression and using model results to describe relationships and make predictions.
+-   [**Part 4: Foundations for inference.**](https://openintro-ims2.netlify.app/foundations-of-inference) Case studies are used to introduce the ideas of statistical inference with randomization tests, bootstrap intervals, and mathematical models.
+-   [**Part 5: Statistical inference.**](https://openintro-ims2.netlify.app/statistical-inference) Further details of statistical inference using randomization tests, bootstrap intervals, and mathematical models for numerical and categorical data.
+-   [**Part 6: Inferential modeling.**](https://openintro-ims2.netlify.app/inferential-modeling) Extending inference techniques presented thus-far to linear and logistic regression settings and evaluating model performance.
 
 Each part contains multiple chapters and ends with a case study.
 Building on the content covered in the part, the case study uses the tools and techniques to present a high-level overview.
 
+In the chapters that cover statistical inference, we have presented a parallel structure that walks the student through both computational and mathematical approaches to every inferential topic.
+Trying to cover every approach for every topic is likely too much material for a one semester class.
+We suggest that you make deliberate choices for navigating the book with your students.
+A few potential paths through the book (with chapter numbers in parentheses) are given as follows:
+
+* Focus on **parallel structure of computational and mathematical methods**: [Intro](https://openintro-ims2.netlify.app/introduction-to-data) (1, 2), [EDA](https://openintro-ims2.netlify.app/exploratory-data-analysis) (4, 5), [Regression](https://openintro-ims2.netlify.app/regression-modeling) (7), [Foundations](https://openintro-ims2.netlify.app/foundations-of-inference) (11, 12, 13, 14), [Inference](https://openintro-ims2.netlify.app/statistical-inference) (a subset of: 16, 17, 18, 19, 20, 21, 22; potentially: 16, 17, 19, 20)
+* Focus on **computational methods**: [Intro](https://openintro-ims2.netlify.app/introduction-to-data) (1, 2), [EDA](https://openintro-ims2.netlify.app/exploratory-data-analysis) (4, 5), [Regression](https://openintro-ims2.netlify.app/regression-modeling) (7), [Foundations](https://openintro-ims2.netlify.app/foundations-of-inference) (11, 12, 14), [Inference](https://openintro-ims2.netlify.app/statistical-inference) (computational methods only for some subset of: 16, 17, 18, 19, 20, 21, 22)
+* Focus on **mathematical methods**: [Intro](https://openintro-ims2.netlify.app/introduction-to-data) (1, 2), [EDA](https://openintro-ims2.netlify.app/exploratory-data-analysis) (4, 5), [Regression](https://openintro-ims2.netlify.app/regression-modeling) (7), [Foundations](https://openintro-ims2.netlify.app/foundations-of-inference) (11, 12, 13, 14), [Inference](https://openintro-ims2.netlify.app/statistical-inference) (mathematical methods only for some subset of: 16, 17, 18, 19, 20, 21, 22)
+* Focus on **modeling**:  [Intro](https://openintro-ims2.netlify.app/introduction-to-data) (1, 2), [EDA](https://openintro-ims2.netlify.app/exploratory-data-analysis) (4, 5), [Regression](https://openintro-ims2.netlify.app/regression-modeling) (7, 8, 9), [Foundations](https://openintro-ims2.netlify.app/foundations-of-inference) (11, 12, 13, 14), [Inference](https://openintro-ims2.netlify.app/statistical-inference) (19), [Inferential modeling](https://openintro-ims2.netlify.app/inferential-modeling) (24, 25, 26)
+
+We expect that most courses following a classical syllabus will not have time to cover the chapters in the last part [Inferential modeling](https://openintro-ims2.netlify.app/inferential-modeling) (24, 25, 26).
+
 Each chapter ends with a review section which contains a chapter summary as well as a list of key terms introduced in the chapter.
 If you're not sure what some of these terms mean, we recommend you go back in the text and review their definitions.
 We purposefully present them in alphabetical order, instead of in order of appearance, so they will be a little more challenging to locate.