Introduction to Data Analysis and R: Wrap Up

I hope you now feel at least a little more comfortable working with and analyzing datasets, as well as working with R/RStudio. Don't forget that there is a vast pool of information related to these topics freely available and easily findable online, so if you get stuck or if you want to learn more, starting with a general internet search is not a bad idea.

The entire playlist of R intro videos in this module is available. if you would like to bookmark it for future reference.

There are lots of other ways you can analyze data, but they are all outside the scope of this module. If your data doesn't fit any of the analyses we've learned, you may need to consult a statistician to find out the best way to analyze and interpret your data.

References & Acknowledgements

The following materials were cited or otherwise used in this module:

• Huff, D. (1954). How to lie with statistics. W.W. Norton & Company.
• McDonald, J.H. (2014). Handbook of biological statistics, (3rd ed.). Sparky House Publishing.
• Navarro, D. (2019). Learning Statistics with R, (Version 0.6.1). https://learningstatisticswithr.com/book/index.html.
• Van Emden, H. F. (2008). Statistics for terrified biologists. Blackwell Pub.
• Vickers, A. (2010). What is a p-value anyway? Pearson Education.
• Wallace, D. P., & Van Fleet, C. J. (2012). Knowledge into action: research and evaluation in library and information science. Libraries Unlimited.

Feedback

This module is part of a recent initiative to help increase overall data literacy at Miami. We would love for feedback on this module! If you would like to provide feedback, please fill out some or all of this feedback form. Thank you!