00-preface.Rmd

# Preface {.unnumbered}

```{r, include = FALSE}
source("_common.R")
```

We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods.

1. Statistics is an applied field with a wide range of practical applications.
2. You don't have to be a math guru to learn from interesting, real data.
3. Data are messy, and statistical tools are imperfect. However, when you understand the strengths and weaknesses of these tools, you can use them to learn interesting things about the world.

## Textbook overview {-}

- **Part 1: Introduction to data.** Data structures, variables, and basic data collection techniques.
- **Part 2: Exploratory data analysis.** Data visualization and summarization for one variable, the relationship between two variables, and exploring relationships among many variables.
- **Part 3: Foundations of inference.** An introduction to the ideas of statistical inference with randomization tests, bootstrap intervals, and mathematical models.
- **Part 4: Inference for categorical data.** Inference for one or two proportions using simulation and randomization techniques as well as the normal distribution.
- **Part 5: Inference for quantitative data.** Inference for one or two means using simulation and randomization techniques as well as the $t$-distribution.
- **Part 6: Inference for regression.** Inference for a regression slope or correlation using simulation and randomization techniques as well as the $t$-distribution.
- **Part 7: Probability.** A taste of probability theory through hypothetical two-way tables and tree diagrams.

Each part contains multiple chapters and ends with a chapter demonstrating how to apply the methods using the RStudio software.

Each chapter ends with a review section which contains a chapter summary as well as a list of key terms and key ideas 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.
However, you should be able to easily spot them as **bolded text**.

### Examples and exercises {.unnumbered}

Examples are provided to establish an understanding of how to apply methods.

::: {.workedexample data-latex=""}
This is an example.
When a question is asked here, where can the answer be found?

------------------------------------------------------------------------

The answer can be found here, in the solution section of the example!
:::

When we think the reader is ready to try determining a solution on their own, we frame it as Guided Practice.

::: {.guidedpractice data-latex=""}
The reader may check or learn the answer to any Guided Practice problem by reviewing the full solution in a footnote.[^preface-1]
:::

[^preface-1]: Guided Practice problems are intended to stretch your thinking, and you can check yourself by reviewing the footnote solution for any Guided Practice.


### Data sets and their sources {.unnumbered}

A large majority of the datasets used in the book can be found in various R packages.
Each time a new dataset is introduced in the narrative, a reference to the package like the one below is provided.
Many of these datasets are in the [**openintro**](http://openintrostat.github.io/openintro) R package that contains datasets used in [OpenIntro](https://www.openintro.org/)'s open-source textbooks.[^preface-2]

[^preface-2]: Mine Çetinkaya-Rundel, David Diez, Andrew Bray, Albert Kim, Ben Baumer, Chester Ismay and Christopher Barr (2020).
    openintro: Data Sets and Supplemental Functions from 'OpenIntro' Textbooks and Labs.
    R package version 2.0.0.
    <https://github.com/OpenIntroStat/openintro>.

::: {.data data-latex=""}
The [`textbooks`](http://openintrostat.github.io/openintro/reference/textbooks.html) data can be found in the [**openintro**](http://openintrostat.github.io/openintro) R package.
:::

The datasets used throughout the book come from real sources like opinion polls and scientific articles, except for a handful of cases where we use toy data to highlight a particular feature or explain a particular concept.
References for the sources of the real data are provided at the end of the book.

## STAT 216 Coursepack {-}

Each week, you will work through in-class activities with your team mates and the guidance of your instructor. These activities, as well as reading guides to guide you in taking notes on the required readings and videos, are included in the _STAT 216 Coursepack_. This course requires you to purchase a printed copy of the _STAT 216 Coursepack_ and bring it with you to class each day. 

The coursepack is available for purchase through the [MSU Bookstore](https://www.msubookstore.org/). You may purchase the coursepack in person, or you may purchase online and have the coursepack shipped to you. The coursepack will be available in the MSU Bookstore on the first day of classes. Chapter 1 of the coursepack is provided here if you do not have the coursepack by your first day of class.

* [STAT 216 Coursepack: Chapter 1](Coursepack-Ch1.pdf)

## Acknowledgements {-}

This project would not have been possible without the talented authors
of the [*OpenIntro*](https://www.openintro.org/) open resource textbooks and all those who volunteer with OpenIntro. 
The authors would
also like to thank the Montana State University Library,
who generously funded this project.