Statistics

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Statistics OR 155, Section 2

• J. S. Marron, Professor
• Department of Statistics
• and Operations Research

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Class Web Site

• http//www.stat-or.unc.edu/postscript/marron/teach
  ing/stor155-2007/Stor155-07Home.html
• (dont need to write down, it is on handout)
• Fundamental to all parts of course
• (so figure it out immediately)

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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)

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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

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HW ideas Concepts

• Two HW Traps
• Working together
• Great, if the relationship is equal
• But dont be the yes, I get it person
• The HW Consortium
• You do HW 1, and Ill do HW 2
• Easy with electronic HW
• Trap HW is about learning
• You dont learn on your off weeks

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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)

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Get up to Speed on EXCEL

• HW C1 Class Problem 1
• (Microsoft Word File)
• On Part C1.2
• Dont type in data, upload instead
• (Recall instructions on Class Web Page)
• Also load Excel's "Data Analysis Toolpak

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Next time

• Show more intro to Excel screen shots
• Use menus as on 07-03-01, pgs 29 30

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Reading In Textbook

• Approximate Reading for Todays Material
• Pages 1-10
• Approximate Reading for Next Class
• Pages 14-23

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What is Statistics?

• Definition 1
• Gaining Insight from Numbers
• (similar to texts definition)
• Definition 2
• The Science of Managing Uncertainty

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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

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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,

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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

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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

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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)

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Exploratory Data Analysis

• EDA 1
• Numerical Summaries for Categorical Data
• Frequencies Counts
• Relative Freq. Counts / Total
• (puts on scale of 0,1)

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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

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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

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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)

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Bar Graph HW

• HW 1.14 (bars in both original order, and
  sorted), using Excel
• (Recall no need to type in data)

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Material Deliberately Skipped

• Pie Charts
• Statistical Graphics Folklore
• All meaningful information is
• better conveyed with bar chart

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Material Deliberately Skipped

• Stem Leaf Plots
• Statistical Graphics Folklore
• A restrictive and arbitrary histogram,
• only pencil and paper artifact