Introduction to Modern Statistics

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.

• 
  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.

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.

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.
However, you should be able to easily spot them as bolded text.