Sta 101 - Data Analysis and Statistical Inference (Spring 2019)

Links to most frequently used resources for this course:

• Sakai
• Piazza: To post and answer questions related to course materials (to be posted)
• Videos: For viewing videos on the Duke Coursera page (to be posted)
• Learning objectives: For viewing the learning objectives on the Duke Coursera page (to be posted)
  • Answers to Test Yourself questions: Crowd-sourced answers to Test Yourself questions from the learning objectives. Feel free to add / edit.
• OpenIntro Statistics: Textbook
• Probability calculations:
  • Probability tables
  • Distribution calculator applet

---

Interactive web applets:

You will see these in the course videos, and you’re encouraged to play with / use them for learning and doing.

• Distribution calculator: For calculating probabilities for the binomial, normal, t, T, and $\\chi^2$ distributions.
• Central Limit Theorem (for means): For exploring and visualizing the central limit theorem (for means). Especially illustrative if you’re trying to grasp what we mean by “sampling many times from the population” or by the term “sampling distribution”. Also great for illustrating shapes of sampling distributions given various population characteristics and sample sizes.
• Central Limit Theorem (for proportions): For exploring and visualizing the central limit theorem (for proportions).
• Diagnostics for simple linear regression: For illustrating how diagnostics plots for simple (one-predictor) linear regression look for various forms of relationship between the explanatory and response variables.

---

Supplementary resources from OpenIntro:

You might find the following resources useful as you navigate through this course.

• Additional videos accompanying OpenIntro Statistics: These can be used as supplementary videos, they are basically book-on-tape versions of the textbook.
• Textbook errata: If you read something that doesn’t make sense first check to see if it’s been corrected already. If not, submit typos at http://www.openintro.org/stat/textbook.php. The OpenIntro team very much appreciates your feedback!
• Probability tables

---

Supplementary resource for bootstrapping:

• Book chapter: Bootstrap methods and permutation tests

---

Supplementary R resources:

• CodeSchoolR Tutorial: Another brief R tutorial, in case you would like to have another avenue by which to get introduced to R.
• twotorials: how to do stuff in r. two minutes or less.

---

Stats and data visualization blogs:

Find out what others are doing with data, or what other statisticians/data analysts/data scientists/data visualizers are up to.

• FiveThirtyEight: Nate Silver and friends
• FlowingData: data visualization
• Junk Charts: the name says it, examples of not-so-good visualizations and discussion on how to improve them
• The Guardian Data Blog: interesting data visualizations for news stories as well as links to the actual data
• Statistical Modeling, Causal Inference, and Social Science: blog of Andrew Gelman, professor of statistics and political science at Columbia University, often higher level statistics but interesting commentary
• Simply Statistics: blog of three biostatistics professors (Jeff Leek, Roger Peng, and Rafa Irizarry), posts discussing science/popular writing, linking to inspiring articles, and advice for up-and-coming statisticians