Univariate data analysis

Learning outcomes

• The study of variability important to help answer: "what happened?"
• Univariate tools such as the histogram, median, MAD, standard deviation, quartiles will be reviewed from prior courses (as a refresher)
• The normal and t-distribution will be important in our work: what are they, how to interpret them, and use tables of these distributions
• The central limit theorem will be explained conceptually: you cannot finish a course on stats without knowing the key result from this theorem.
• Using and interpreting confidence intervals will be crucial in all the modules that follow.

Extended readings

• New Boeing planes will generate 0.5 TB of data per flight. Read about this, and other sources of data: "every piece of that plane has an internet connection, from the engines to the flaps to the landing gear".
• All students, but especially the 600-level students should read the article by Peter J. Rousseeuw, Tutorial to Robust Statistics it is easy to read, and contains so much useful content.

Resources

• Class notes 2015
• Class notes 2014
• Textbook, chapter 2
• Quizzes (with solutions): attempt these after you have watched the videos

Tasks to do first	Quiz	Solution
Complete steps 10, 11, 12 and 13 of the software tutorial (also steps 1 through 9)	Quiz	Solution
Watch videos 1, 2, 3 and 4	Quiz	Solution
Watch videos 7, 8 and 9	Quiz	Solution
Watch videos 10, 11, and 12	Quiz	Solution
Watch videos 13, 14	Quiz	Solution
Watch videos 15, 16	Quiz	Solution

• A demonstration of R
• Using tables of the normal distribution

Class videos from prior years

Videos from 2015

1. Introduction [05:59]
2. Histograms [04:50]
3. Basic terminology [06:41]
4. Outliers, medians and MAD [04:42]
5. The central limit theorem [06:56]
6. The normal distribution, and standardizing variables [05:54]
7. Normal distribution notation and using tables and R [05:48]
8. Checking if data are normally distributed [05:57]
9. Introducing the idea of a confidence interval [covered in class]
10. Confidence intervals when we don't know the variance [07:59]
11. Interpreting the confidence interval [07:52]
12. A worked example: calculating and interpreting the CI [03:37]
13. A motivating example to see why tests for differences are important [08:29]
14. The mathematical derivation for a confidence interval for differences [covered in class]
15. Using the confidence interval to test for differences to solve the motivating example [covered in class]
16. Confidence intervals for paired tests: theory and an example [11:59]

Covered in class | No video | Script

Audio only | No video | Script

Audio only | No video | Script

Videos from 2014

See the webpage from 2014

Videos from 2013

See the webpage from 2013