real life module statistics: a happy hdh arvard ei te xperimentkfl5/lock_icots_2010.pdf · 2010....
TRANSCRIPT
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RealReal‐‐Life Module Statistics:Life Module Statistics:A H H d E i tA H H d E i tA Happy Harvard ExperimentA Happy Harvard Experiment
Kari Lock and Xiao‐Li MengHarvard University Statistics Department
International Conference on Teaching StatisticsJuly 12th 2010July 12th, 2010
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RealReal‐‐Life Statistics: Your Chance Life Statistics: Your Chance
GOAL: Develop a course that will
for Happiness (or Misery)for Happiness (or Misery)GOAL: Develop a course that will
• make learning statistics a happy experience• make students appreciate the power andmake students appreciate the power and applicability of statistics• provide students with a deep understanding of the fundamental concepts of statistics
Thi t d i t tThis turned into two courses…STAT 105: Second level course, prerequisite of one course in statisticscourse in statisticsEM 16: General education course
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Happy TeamHappy TeamWee lee LohReetu KumraYves ChretienLinjuan QianPaul EdlefsenPaul EdlefsenKari LockCassandra Wolos Pattanayak
l iPaul BainesSimeng HanJonathan HennesseyyBo JiangVictoria Liublinska
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Happy TeamHappy Team
• Creating a course becomes much more manageable and enjoyable as part of a teamenjoyable as part of a team
• Faculty benefit from having a happy teamy g ppy• Assistance of students; student perspective; new ideas
• Grad students benefit from being on a happy team• Experience course creation early on; get to work closely with experienced faculty members on pedagogyexperienced faculty members on pedagogy
• The course benefits from many minds working together y g g
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RealReal‐‐Life ModulesLife Modules• Course is comprised of 5 modules, each dedicated to a real‐life topicp
• Statistical concepts are introduced as needed asStatistical concepts are introduced as needed, as tools to address these real‐life issues
• Note: this is NOT the same as using real‐life examples to illustrate the statistical concept being taught
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TopicTopic MatrixMatrix
Statistical Topics Finance Romance Medical Legal Wine/ChocolateProbability Random variables/Probability Distributions
Rules of probabilityBayes's RuleDescriptive Statistics
Statistical Inference Hypothesis TestingPosterior probabilities and p-valuesDecision Theory
Advanced Statistics Linear RegressionANOVAANOVATime Series ModelsLogistic RegressionStatistical InteractionModel/Variable SelectionSimpson's ParadoxMultiple Hypothesis Testing
Study Design Survey MethodsExperimental DesignObservational StudiesSelection BiasResponse BiasPublication BiasPublication Bias
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Speed DatingROMANCESpeed Dating
0 1 2 3 4 5log · · · · ·1p sincerity intelligenattrac ce fun ambitioe nt
pivβ β β β β β
⎛ ⎞= + + +⎜ ⎟−⎝ ⎠
+ +
MALES Estimate Std.Error z value Pr(>|z|) (Intercept) -4.68 1.45 -3.22 0.001 ** tt ti 0 61 0 16 3 88 0 0001 ***attractive 0.61 0.16 3.88 0.0001 ***sincere -0.05 0.16 -0.32 0.75 intelligence -0.09 0.20 -0.44 0.66 fun 0.26 0.15 1.65 0.10 . ambitious 0.01 0.15 0.05 0.96
FEMALES Estimate Std.Error z value Pr(>|z|) ( ) 7(Intercept) -4.13 0.82 -5.02 10-7 *** attractive 0.66 0.10 6.89 10-12 ***sincere -0.10 0.10 -1.01 0.31 intelligence -0.03 0.12 0.21 0.83
7
gfun 0.20 0.09 2.20 0.03 * ambitious -0.12 0.09 -1.37 0.17
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FINANCE
User Cash No. Stocks Stock Value ($'s) Total Worth ($'s)
fanSTATStic4 28.23 4 13,695.00 13,723.23
ERNYng$ 3,691.44 4 9,865.88 13,557.32
teamemerald 36.84 2 11,105.07 11,141.91
SteelMagnolias 19.02 5 11,017.09 11,036.11
Enron 3,005.75 6 7,368.19 10,373.94
sharpspoons 0.16 5 10,253.78 10,253.94
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ELECTIONELECTION
A poll of 500 people was taken in Oklahoma (population ≈ 3.6 million) to predict Oklahoma in the 2008
id i l l i l ll ld d ipresidential election. How large a poll would you need in California (population ≈ 36 million ) to get a prediction as precise?as precise?
1. 500
√2. 500*√103. 5000
4. > 5000
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MEDICAL
Positive ResultC
C
F F F F F F F F F F F FF F F F F F F F F F FF F F F F F F F F FF F F F F F F F F
Negative ResultCC
C
F F F F F F F FF F F F F F FF F F F F FF F F F F
Cancer Cancer-free
f d l k ll f h ’ Everyone
d l k ll f h E If we randomly pick a ball from the Cancer bin, it’s more likely to be red/positive. If we randomly pick a ball the Cancer-free bin, it’s more
We randomly pick a ball from the Everyone bin.
If the ball is red/positive, is it more likely to be from the
10
y plikely to be green/negative.
p yCancer or Cancer-free bin?
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WINE AND CHOCOLATE
Data on Average Ratings
(1 7)
Wine Style
Kabinett Spätlese Auslese(1-7) Kabinett Spätlese Auslese
1st 5.25 (n=8) 4.00 (n=7) 5.63 (n=8)Order tasted 2
nd 4.38 (n=8) 4.25 (n=8) 4.86 (n=7)
3rd 4 86 ( 7) 3 25 ( 8) 4 75 ( 8)3rd 4.86 (n=7) 3.25 (n=8) 4.75 (n=8)
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Additional Keys to SuccessAdditional Keys to Success• Clickers (Personal Response System)
• Gets the students to come to class and stay engaged
• Peer discussion• Students are given time to discuss amongst themselves• Students are given time to discuss amongst themselves
• Emphasis on projectsp p j• One project per module; helps to make statistics “real‐life”
• Guest Speakers• Guest Speakers• Experts in the module topic who use statistics
• i‐movies
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ChallengesChallenges
• Material is taught out of order
• Modules give less flexibility to the professor
• Students of varying backgrounds
F i d i• Forming groups, group dynamics
• Creating a new course is a lot of workCreating a new course is a lot of work• SOLUTIONS:
1) STAT 3052) Build a happy team!