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Turning data into knowledge to
solve real world problems
Christopher R. Bilder, Ph.D.
Department of Statistics
University of Nebraska-Lincoln
www.chrisbilder.com
www.chrisbilder.com
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15 years ago…
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The year is 1990
Music – U2
George Bush is president
TV – The Simpson’s
Millard South
– Senior year
– Big hair
– In the middle of winning state titles in
basketball for 3 out of 4 years (1988, 1989, 1991)
 What am I going to major in at college?
– Calculus I
– No AP Statistics!
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15 years ago…
 UNO (1990 – 1994)
– Math undergraduate major – What can you do with a degree?
– Planned to be an actuary
– Hypothesis testing in a statistics course (junior year)
• Use for decision making!
• Scientifically prove a hypothesis or statement
 Kansas State University for graduate school (1994 – 2000)
– Statistics graduate major in Department of Statistics
– Master of Science (MS) and Doctor of Philosophy (PhD)
 Oklahoma State University faculty (2000 – 2003)
– Department of Statistics
 UNL faculty (2003 – now)
– NEW Department of Statistics
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Purpose
 Tell you a little about the statistical science
 Turning data into knowledge to solve real world problems
– 3 actual examples
 AP statistics exam
 Website (www.chrisbilder.com/statistics) for more information
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Grocery store prices
 Undergraduate teaching example for a course like AP STATs
 How could you determine which grocery store, Super Wal-Mart
or Baker’s, has lower average prices?
– Paired or dependent two sample hypothesis test for Wal-Mart - Baker’s
– Sample the same items at each store
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Grocery store prices
 Undergraduate teaching example for a course like AP STATs
 How could you determine which grocery store, Dillon’s or
Food-4-Less in Manhattan, KS, has lower average prices?
– Paired or dependent two sample hypothesis test for Dillon’s - Food-4-Less
– Sample the same items at each store
 Only cereals from Fall 1998
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Grocery store prices
 Sample:
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Item
Malt-o-meal - Tootie Fruities, 15oz
Malt-o-meal - Golden Puffs, 18oz
Quaker Oats - Life Cereal: Original, 21oz
Cheerios, 20oz
Cheerios, 15oz
Wheaties, 18oz
Kellogg’s Funpack, 8 9/16oz
Kellogg’s Variety Pack 9 5/8oz.
Kellogg’s Frosted Mini-Wheats Bite Size
19oz
Kellogg’s Frosted Mini-Wheats, 16oz
Kellogg’s Frosted Flakes, 15oz
Our Family Frosted Flakes, 20oz.
Kellogg’s Crispix, 12oz.
Our Family - Raisin Bran, 20oz
Kellogg’s Smart Start, 13.3oz
Grape Nuts, 24oz
Frosted Alpha Bits, 15oz
Dillon's
$1.99
$1.99
$3.69
$4.59
$3.79
$3.89
$2.89
$3.49
$3.49
$2.50
$3.19
$2.50
$3.49
$2.50
$3.49
$3.00
$3.00
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Food-4-Less
$1.84
$1.84
$3.49
$4.24
$3.50
$3.60
$2.67
$3.14
$2.50
$2.73
$2.92
$1.90
$3.20
$1.92
$3.24
$2.85
$2.87
Difference
$0.15
$0.15
$0.20
$0.35
$0.29
$0.29
$0.22
$0.35
$0.99
-$0.23
$0.27
$0.60
$0.29
$0.58
$0.25
$0.15
$0.13
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Grocery store prices
 Do you think there are
mean differences?
Dillon's - Food 4 Less
$0.8
Dillon's
$1.99
$1.99
$3.69
$4.59
$0.6
$3.79
$3.89
$2.89
$3.49
$0.4
$3.49
$2.50
$3.19
$2.50
$0.2
$3.49
$2.50
$3.49
$3.00
$0.0
$3.00
$1.0
9
Food-4-Less Difference
$0.15
$1.84
$0.15
$1.84
$0.20
$3.49
$0.35
$4.24
12
14
$0.29
$3.50
$0.29
$3.60
$0.22
$2.67
$0.35
$3.14
$0.99
$2.508
4
-$0.23
$2.73
5
6
13
$0.27
$2.92
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15
$0.60
$1.90
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3
$0.29
$3.20
1
2
16
$0.58
$1.92
17
$0.25
$3.24
$0.15
$2.85
$0.13
$2.87
-$0.2
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10
$0.8
$0.6
$0.4
75%
50%
$0.2
25%
Dillon's - Food 4 Less
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Item
Malt-o-meal - Tootie Fruities, 15oz
Malt-o-meal - Golden Puffs, 18oz
Quaker Oats - Life Cereal: Original, 21oz
Cheerios, 20oz
Cheerios, 15oz
Wheaties, 18oz
Kellogg’s Funpack, 8 9/16oz
Kellogg’s Variety Pack 9 5/8oz.
Kellogg’s Frosted Mini-Wheats Bite Size
19oz
Kellogg’s
Frosted Mini-Wheats, 16oz
Kellogg’s Frosted Flakes, 15oz
Our Family Frosted Flakes, 20oz.
Kellogg’s Crispix, 12oz.
Our Family - Raisin Bran, 20oz
Kellogg’s Smart Start, 13.3oz
Grape Nuts, 24oz
Frosted Alpha Bits, 15oz
$1.0
$0.0
-$0.2
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Grocery store prices
 Paired two sample hypothesis test
– Ho:Dillon’s - Food-4-Less=0
Ha:Dillon’s - Food-4-Less0
– t = 4.77, p-value = 0.0002,
95% C.I.: 0.1644 < Dillon’s - Food-4-Less < 0.4274
– Reject equal mean prices
 If price was the only consideration, what store should one
shop at?
 Assumptions
– Normal populations
– The sample was taken in 1998; what about now?
– Finite populations
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Placekicking
 The use of the statistical science in sports
 Find a model to estimate the probability of success
for placekicks (field goals, PATs) in the NFL
 Video
– January 7, 1996
– Playoff game
– Indianapolis Colts 10
Kansas City Chiefs 7
– Lin Elliott of KC will attempt a 42 yard field goal to tie the game and
send it into overtime
– Field goal video
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Placekicking
 What factors affect the probability of success for NFL
placekicks?
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–
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–
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Distance
Pressure – How do you quantitatively measure?
Wind
Grass vs. artificial turf
Dome vs. outdoor stadium
 Collect sample of >1,700 placekicks during the 1995 NFL
season
 Find the best logistic regression model of the form
e0 1x1 2x2 k xk
p=
1  e0 1x1 2x2  k xk
where p is the probability of success
xi for i=1,…,k are independent variables
i measures the effect of xi on p for i=1,…,k
e  2.718; ln(e) = 1 www.chrisbilder.com
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Placekicking
 The i’s are parameters which are estimated using
“iteratively reweighted least squares”
 Estimated model
e4.49840.3306change 0.0807distance 1.2592PAT  2.8778wind0.0907distancewind
p̂=
1  e4.49840.3306change 0.0807distance 1.2592PAT  2.8778wind0.0907distancewind
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–
–
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Change: lead change = 1, non-lead change = 0
Distance: distance in yards
PAT: point after touchdown = 1, field goal = 0
Wind: windy (speed > 15 MPH) = 1, non-windy = 0
 What is the estimated probability of success for Elliott’s
field goal?
Change Distance PAT
Wind
– Conditions:
1
42
0
0
– Estimated probability of success: p̂  0.6850
– 90% confidence interval for probability of success:
0.6298 < p < 0.7402
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Estimated probability of success of a field goal (PAT=0)
0.4
0.6
0.685
0.2
Change=0, Wind=0
Change=1, Wind=0
Change=0, Wind=1
Change=1, Wind=1
0.0
Estimated Probability of Success
0.8
1.0
Estimated probability of success for a field goal (PAT=0)
20
30
40
42
Distance in Yards
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50
60
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HCV prevalence
 Hepatitis C (HCV)
– Viral infection that causes cirrhosis and cancer of the liver
 Questions:
– How can people be tested in a cost effective and timely manner?
• Blood bank setting
– What is the probability a person has HCV?
• What proportion of people is inflicted with HCV in a population?
• Prevalence in a population
 Individual testing
– Each blood sample is tested individually
– Problems:
• Costly
• Time
+ or - + or - + or - + or - + or -
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
+ or -
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HCV prevalence
 Group testing
– Pool the blood samples together to form n groups of size s
+ or -
Group 1

+ or -
Group 2
+ or -
Group n
– If the GROUP sample is negative, then all s people do not have the
disease
– If the GROUP sample is positive, then at least ONE of the s people
have the disease
• May want to determine who in the group has the disease
– Strategy works well when prevalence of a disease is small
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HCV prevalence
 Notation
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–
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–
–
p = probability an INDIVIDUAL is HCV positive (prevalence)
 = probability a GROUP is HCV positive
s = group size
n = number of groups
T be a random variable denoting the number of positive GROUPS
• T has a binomial distribution with “n trials” and “ as the probability
of success”
n
f(t)    t (1  )n t for t=0,1,2,...,n
t
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HCV prevalence
 How can we estimate p?
– We observe information about the groups, not individuals!
– Estimate  with ˆ  T / n = # positive / # of groups
–  = P(group is positive)
= P(at least one individual is positive)
= 1 – P(no individuals are positive)
using complement rule
= 1 – P(all individuals are negative)
= 1 – (1 – p)s
since p = P(individual is
positive) and s individuals
per group
– p = 1 – (1 – )1/s
1/ s
1/ s
– Then p̂  1  (1  ˆ )  1  (1  T / n)
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HCV prevalence
 Estimation of HCV prevalence in Xuzhou City, China
– Data from Liu et al. (Transfusion, 1997)
– 1,875 blood donors screened for HCV
• There were 42 positives
– In order to test the usefulness of group testing, blood samples were
also pooled
• n = 375 groups
• s = 5 individuals per group
• t = 37 positive groups
– Estimates of p, probability individual is positive
• Using individual data: 42/1875 = 0.0224
• Using group data: p̂  0.0206
– Which is easier and more cost effective?
• 1875 tests using individual testing
• 375 tests using group testing
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HCV prevalence
 New research – MS/PhD research
– What factors could affect p?
– Include independent variables to help model p
e0 1x1 2x2 k xk
p=
1  e0 1x1 2x2  k xk
– Problem: Do not have the individual outcomes
– After a group is tested positive, how can you find what individuals have
the disease?
• Use model to help decide who to retest if get a positive group
– Multiple diseases
• HCV
• HIV
• Other disease
• Simultaneously model
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HCV prevalence

Multiple vector transfer designs
–
–
Swallow (Phytopathology, 1985)
Want to estimate the probability a insect vector transfers a pathogen
(virus, bacteria, etc.) to a plant
Brown
planthopper
Whitebacked
planthopper
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HCV prevalence

Multiple vector transfer designs (continued)
y = 0 if plant is negative, 1 if plant is positive
y=0
y=1
y=0
Greenhouse
Does not
transmit
virus

Transmits
virus
Enclosed
test plant
y=0
y=1
Planthopper
y=0
T = number of plants with disease
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Why statistics?
 Statistics is used in many diverse areas!
– Statistics is the “science of science”
– Florence Nightingale quote:
the most important science in the whole world: for upon it depends the
practical application of every other science and of every art: the one
science essential to all political and social administration, all education,
all organization based on experience, for it only gives results of our
experience.
 Take statistics courses in college!
– Of course, I want you to consider coming to UNL!
– Statistics is mainly a graduate discipline, so there is no undergraduate
major at UNL
– Undergraduate minor in statistics can be useful for many majors
– Most statisticians have an undergraduate degree (Bachelor of Science)
in math
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Why statistics?
 Where do statisticians work?
–
–
–
–
–
–
Pharmaceutical and medical research – Pfizer, Merck, medical centers
Marketing – Target, Hallmark
Government research labs – INEEL, Los Alamos, Sandia, Argonne
Agriculture – Pioneer Hi-Bred
Consulting firms – Quintiles
In Nebraska – ConAgra, Gallup, First National Bank, MDS Pharma,
Experian, UNMC and Creighton medical center, various universities,
Pfizer, Acton International, Nebraska state agencies, Union Pacific
 Everyone that I have known has had a job offer before they
graduated!
 How many statisticians are there?
– 20,000
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Why statistics?
 Salaries
– Non-academic starting (2003 American Statistical Association survey)
Background needed
Survey response rate
Percentile
was 23.5%; see salary
th
th
th
Degree Sample size 25
50
75
surveys at the American
MS
102
45.5K 50K 59K
Statistical Association’s
PhD
99
60K 65K 75K
website
 Strong in mathematics and using computers
– Majority of statisticians have Bachelor’s degrees in mathematics
• Good with calculus
• Applied math courses
• Take at least one statistics course
• Comfortable with using software packages
– To actually be a “statistician”, usually need to go to graduate school to
get a MS or PhD in statistics
• Financial support
• Graduate Teaching Assistantship
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Why statistics?
 What courses to take next in college?
– AP statistics equivalent to a one semester introductory statistics course
without calculus
• UNL: STAT 218 (Introduction to Statistics)
• UNO: MATH/STAT 3000 (Statistical Methods I); Business
Administration 2130 (Principles of Business Statistics)
– Theory – 2 semester sequence using calculus I-III
• UNL: STAT 462 (Distribution Theory) and
STAT 463 (Statistical Inference)
• UNO: MATH 4740 and 4750
(Intro. to Probability and Statistics I and II)
– Applications
• UNL: STAT 450 (Introduction to Regression Analysis) or
STAT 412 (Introduction to Experimental Design)
• UNO: MATH /STAT 3010 (Statistical Methods II); Business
Administration 3140 (Business Statistical Applications)
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Why statistics?
 Other recommended UNL classes (undergraduate)
– MATH 340 Numerical Analysis
– MATH 314 Applied Linear Algebra
– MATH 325 Elementary Analysis and MATH 425 Mathematical Analysis
• Helpful if go on for a PhD
– Computer science programming courses
 Other recommended UNO classes (undergraduate)
–
–
–
–
MATH 3300 Numerical Methods
MATH 4050 Linear Algebra
MATH 4760 Topics in Modeling
MATH 4230 and 4240 Mathematical Analysis I and II
• Helpful if go on for a PhD
– Computer science programming courses
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AP Statistics
 Grading done in Lincoln!
– State fair grounds
– Grade the free response
section of about 66,000 student
exams (2004)
– 250 AP statistics high school
teachers and college
professors
– June 13 to June 19, 2005
– 8:30AM – 4:45PM EVERYDAY
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AP Statistics
 I graded in 2002
– About 900 problems graded!
– 16 graders in a room split into two groups
– Each group has a leader
• Answer questions
• CHECKS some of your grading!
– Paid $1,450
• Stay in dorms
• Free meals and snacks
 Grading is not fun
– Evening activities
– Discussions on how to teach introductory statistics better
 The grading rubric
– An outline of how to grade a problem that must be followed!
– These are put together before graders arrive through examining sample
set of tests
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AP Statistics
 Question #6 in 2002
– 4 parts – (a), (b), (c), (d)
– Each part is graded as
• E = Essentially correct
• P = Partially correct
• I = Incomplete
– Graders are given a “conversion” table to show how to convert the
scores into a numerical score
• 4 = Complete response
• 3 = Substantial response
• 2 = Developing response
• 1 = Minimal response
• 0 = No credit
– 1 point given to an E, 0.5 points given to a P, 0 points given to an I
• Round up if (a) or (c) has the correct interpretation
– Example given at end of PowerPoint file
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For more information…
 E-mail me at [email protected]
 Website: www.chrisbilder.com/statistics
– This PowerPoint presentation (including example question)
– Links to
• Introductory information about being a statistician
• Jobs (including internships)
• Salary information
• List of all Departments of Statistics
• Professional societies
• Course websites that myself and others teach
• Newspaper and magazine articles about statistical applications
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Turning data into knowledge to
solve real world problems
Christopher R. Bilder, Ph.D.
Department of Statistics
University of Nebraska-Lincoln
www.chrisbilder.com
www.chrisbilder.com
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33rd st.
Statistics at UNL
Department
of Statistics
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AP Statistics
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AP Statistics
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AP Statistics
May actually
be an E?
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AP Statistics
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AP Statistics
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Estimated probability of success for a field goal (PAT=0)
0.6
0.4
Lowest Number of Risk Factors
Estimated Probability
90% Confidence Interval
0.2
Highest Number of Risk Factors
Estimated Probability
90% Confidence Interval
0.0
Estimated Probability of Success
0.8
1.0
Estimated probability of success for a field goal (PAT=0)
20
30
40
Distance in Yards
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50
60
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