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

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

Happy TeamHappy TeamWee lee LohReetu KumraYves ChretienLinjuan QianPaul EdlefsenPaul EdlefsenKari LockCassandra Wolos Pattanayak

l iPaul BainesSimeng HanJonathan HennesseyyBo JiangVictoria Liublinska

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

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

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

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

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

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

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?

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)

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

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!