Statistics – OR 155, Section 2

1. Statistics – OR 155, Section 2 J. S. Marron, Professor Department of Statistics and Operations Research

2. Class Web Site http://www.stat-or.unc.edu/postscript/marron/teaching/stor155-2007/Stor155-07Home.html (don’t need to write down, it is on handout) Fundamental to all parts of course (so figure it out immediately)

3. Class Web Site Alternate Approach: • Goggle: marron • Choose “access to course material” • Choose “Stor 155” Fundamental to all parts of course (so figure it out immediately)

4. Suggested Use Of These Notes • Save the Power Point to your computer • Make a print before class • File • Print • Print What: Handouts • Bring to class and write notes on it • Will try to get these up the night before

5. HW ideas & Concepts Two HW “Traps” • Working together: • Great, if the relationship is equal • But don’t be the “yes, I get it” person… • The HW “Consortium”: • You do HW 1, and I’ll do HW 2… • Easy with electronic HW • Trap: HW is about learning • You don’t learn on your off weeks…

6. Get up to Speed on EXCEL HW C1: Class Problem 1 (Microsoft Word File) Recall: only turn in one printed page (per problem part) (recall instructions on course web page) Note: you can also write on that sheet (e.g. your name & highlight answer)

7. Get up to Speed on EXCEL HW C1: Class Problem 1 (Microsoft Word File) On Part C1.2: Don’t type in data, upload instead (Recall instructions on Class Web Page) Also: load Excel's "Data Analysis Toolpak”

8. Next time Show more “intro to Excel” screen shots Use menus as on 07-03-01, pgs 29 & 30

9. Reading In Textbook Approximate Reading for Today’s Material: Pages 1-10 Approximate Reading for Next Class: Pages 14-23

10. What is Statistics? Definition 1: Gaining Insight from Numbers (similar to text’s definition) Definition 2: The Science of Managing Uncertainty

11. Key Themes • Uncertainty • Variability (will get quantitative about these) Favorite Quote: “I was never good at math, but statistics is easy, since it is just common sense”

12. Fundamental Concepts “Populations” of “Individuals” e.g. each of you in class Each individual is associated with numbers Called “variables” E.g. scores on HW1, HW2, …

13. Common “Data Structure” I.e. Data organization method: A “matrix” (mathematical object) i. e. 2-d Array, i. e. spreadsheet Where: Individuals  Rows Variables  Columns

14. Common “Data Structure” HW: 1.2 (answer questions in text), 1.37a Appears on pages 22-23, 38-39 of text. {Note: odd answers in back, Sec. S}

15. 2 Important Variable Types • “Categorical” - puts into set of “slots” e.g. Male / Female e.g. Fr, So, Jr, Sr • “Quantitative” - an actual number e.g. HW score, height, age HW: 1.1 (Note: not in order, please turn in in order assigned)

16. Exploratory Data Analysis EDA 1: Numerical Summaries for Categorical Data • Frequencies = Counts • Relative Freq. = Counts / Total (puts on scale of [0,1])

17. EDA 1 HW C2: For the data of 1.37: • What is the frequency of Males? • What is the relative freq. of Males? • Explain in 15 words or less why (b) is the “better summary”. {Ask for answer by email on Wednesday}

18. Exploratory Data Analysis EDA 2: Visual Displays of Categorical Data Idea: Picture allows quick understanding of frequencies • Pie Charts - not recommended • Bar Graphs - heights are frequencies

19. Class Example 1 Text Problem 1.13 (bar graphs, same) (file) (get data from CD, to avoid retyping) • Show stretching of fields • Zoom to 200% • Chart Wizard (note it makes guesses) • Add titles, etc. • Twiddle size, location etc. (recall need to turn in only 1 page)

20. Bar Graph HW HW: 1.14 (bars in both original order, and sorted), using Excel (Recall no need to type in data)

21. Material Deliberately Skipped Pie Charts • Statistical Graphics Folklore: “All meaningful information is better conveyed with bar chart”

22. Material Deliberately Skipped Stem & Leaf Plots • Statistical Graphics Folklore: “A restrictive and arbitrary histogram, only pencil and paper artifact”