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Transcript of Statistics – OR 155, Section 2 J. S. Marron, Professor Department of Statistics and Operations...
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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/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)
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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”1. Working together:
• Great, if the relationship is equal• But don’t be the “yes, I get it” person…
2. 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…
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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:
Don’t 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 Today’s 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 text’s definition)
Definition 2:
The Science of Managing Uncertainty
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Key Themes
I. Uncertainty
II. 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 Types1. “Categorical” - puts into set of “slots”
e.g. Male / Femalee.g. Fr, So, Jr, Sr
2. “Quantitative” - an actual numbere.g. HW score, height, age
HW: 1.1(Note: not in order,
please turn in in order assigned)
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Exploratory Data AnalysisEDA 1:
Numerical Summaries for Categorical Data
a. Frequencies = Counts
b. Relative Freq. = Counts / Total
(puts on scale of [0,1])
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EDA 1
HW C2: For the data of 1.37:
a. What is the frequency of Males?
b. What is the relative freq. of Males?
c. 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
a. Pie Charts - not recommended
b. Bar Graphs - heights are frequencies
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Class Example 1Text 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”