© 2006 Hewlett-Packard Development Company, L.P.The information contained herein is subject to change without notice

Oleg Janke25-June-2012

Tip of the hat to Kevin Kacmarynski

Scatterplots and Cautions of Correlation

Topics for Today’s Discussion• Scatterplots:

−Why bother?−Creating & Analyzing−Good usage

• Correlation and Association• Potential Missteps • Summary• Real Personal Application

Presumption• Basic graphing skills• Familiar with charting (for example, as

Excel)• YES NO

Scatterplots: Why bother?• Scatterplot (Scatter diagram)

−Converts two columns of numbers (ordered pairs) into picture

−Explores relationship between two quantitative variables

• What value does it have?−Determine possible cause and

effect links (control)−Predict results of variable that is

difficult to measure if it is strongly related to another variable that is easier to measure (proxy)

Creating a scatterplotWhat is the relationship between height & weight?

Creating a scatterplotPlot each characteristic of interest on a standard XY plot. Katie James

Analyzing a scatterplotIs there a relationship? OR Is it just randomness? (N=40)

Analyzing a scatterplotIs there a relationship? OR Is it just randomness? (N=40) How confident are you

that there is a linear relationship between height and weight in this data set? (Choose one)

• 100%

• 99-100%

• 95-99%

• 90-95%

• 80-90%

• insufficient data to say

• no relationship

Analyzing a scatterplotIs there a relationship or is it just randomness? (N=40)• Add Median lines and count quadrant totals+

Median X

146

614

Median Y

+ Olmstead-Tukey 1947

Analyzing a scatterplotIs there a relationship or is it just randomness? (N=40)• Add Median lines and count quadrant totals+

Median X

146

614

Median Y

+ Olmstead-Tukey 1947

NO relationship

• shotgun effect

• appx equal number in each quadrant

IS a relationship

• one diagonal will dominate

Analyzing a scatterplotIs there a relationship or is it just randomness? (N=40)• Add Median lines and count quadrant totalsMedian X

146

614

Median Y

•Less than 5% chance data could align this way simply from randomness• Therefore fairly confident X & Y are related

SIGN TEST TABLE *N 1% 5%10 0 120 3 530 7 940 11 1350 15 1760 19 21

* Ishikawa “Guide to Quality Control”, 1976

Good usage of Scatterplots• In this plot, we

observe a clear relationship between height and weight

• As height of individuals increase, their weight tends to increase as well

• In the ideal case this relationship is called Body Mass Index (BMI)

Good usage of scatterplots

Scenario 1: We are building

parts on one line in one location.

What does the plot tell us about part length and part diameter?

Good usage of scatterplots Scenario 2:

We build the same part on two different Lines.

Now what does plot tell us about part length & part diameter?

Good usage of scatterplots

• IS a Line effect here • Relationship between

Diam & Length differs by Line

• Diam1 twice Daim2− Tighter process control?

• Length1 < Length2

Now what does plot tell us about part length & part diameter?• Always be alert to possible strata in the data• Plotting your data is crucial for discovery

Correlation Coefficient• Correlation is defined as measure of strength of

linear relationship between two quantitative variables−Correlation coefficient is a mathematically calculated value:

−Correlation values are always between -1 and +1• 0 indicates no correlation (perfect shotgun pattern)• -1 and +1 indicates perfect correlation (all points fall on line)• Sign indicates direction

− Positive: up and to right− Negative: down and to left

Correlation and Association• Re-visiting our first

example, we saw strong, positive relationship between height and weight

• Supported by correlation coefficient value of 0.709

• Relationship exists • Does NOT prove causalityCorrelation = 0.709

Calculation provided by JMP statistical software

Correlation and Association

Medical Trial• Dosage 490-510 mg• Recorded therapeutic

response from 20 to 100

Is there a correlation between Dosage and Response?

• Yes

• No

• Insufficient data

• Don’t know

Correlation and Association• Since calculated

correlation value is zero, there is no association between dosage and desired response! Right?

− No LINEAR relationship

• Correlation coefficient, by itself, does not tell the entire story

• Always look at your graphs to see what the data sayCorrelation coefficient

= 0Calculation provided by JMP statistical software

2 2

2 2

Questions?How to create/analyze

ScatterplotsCorrelation and Associations

Missteps with scatterplots & correlation

1. Bimodal distributions2. Stratified data3. Lurking variables4. Extrapolation5. Too narrow range of X (independent variable)6. Weak/sloppy measurement 7. Chicken and Egg Syndrome

Misstep #1with scatterplots & correlation• What is my house

worth?−Sale price data & house

size were collected on 21 houses in the same town

−Another house (mine) in same town is 2300 square feet in size, so it should be worth a little over $200K

−Correlation coefficient = 0.943 (very high)

What is the problem in this analysis and the resulting conclusion?

Misstep #1with scatterplots & correlation

What is the problem?• Relationship/correlation dependent solely on one data point• Why might this one point not be appropriate?

• Location (school district, suburban)• Features (pool, lot, barn, view)• Timing (peak of housing bubble)

• Example of Bimodal data

Need both appropriate data & proper analysis techniques

47

64

Is there a linear relationship?• What do median lines say about the relationship?

Sign Test TableN 1% 5%10 0 120 3 530 7 940 11 1350 15 17

Misstep #2 with scatterplots & correlation

• Based on this data set, with high Correlation Coefficient (0.780) what’s the relationship between shoe size and knowledge?

• What’s missing?Correlation = 0.780Calculation provided by JMP statistical software

Misstep #2 with scatterplots & correlation• Does this help solve mystery?

• Be sure to look for hidden variables that might have an impact on relationship

• Stratified Data– sub population with different relationships – can give erroneous conclusions

For example: CSAT data• Those who respond to survey• Those who do NOT respond to surveyDo both groups have similar opinions?

Misstep #3 with scatterplots & correlation Beware of Lurking Variables

• Related thru common 3rd variable− Ice cream sales correlates with water usage (temperature)− Height–weight example (age)− Call vol at hp Call Center A correlated w/call vol at hp CC--B

(business)• Related thru independent growth (decay) rates

− Population in Indonesia correlates with price of tea in NYC (growth)− My car’s value correlates to grams of Cobalt-60 isotope (decay)

• Both have half-lives of about 5-6 years• Related through measuring same characteristic differently

− Weight in pounds is correlated to weight in kilos− Attendance at an event is correlated to empty seats at same event− Area of a US state is correlated with population of that state

• Some notable exceptions (AK, MT)

Does School Spending Educate Students?

States spending more per student have lower SAT* scores!

Expediture/pupil by State vs SAT

900

950

1000

1050

1100

1150

1200

$4,000 $5,000 $6,000 $7,000 $8,000 $9,000 $10,000 $11,000 $12,000

Expenditure per pupil for public school K-12 (2002-03)

SAT

scor

es fo

r 199

8 Obvious Negative

Correlation

* SAT test is a standardized test used by many colleges across US to determine level of student preparedness for college

Does School Spending Educate Students?

States spending more per student have lower SAT scores!

Expediture/pupil by State vs SAT

900

950

1000

1050

1100

1150

1200

$4,000 $5,000 $6,000 $7,000 $8,000 $9,000 $10,000 $11,000 $12,000

Expenditure per pupil for public school K-12 (2002-03)

SAT

scor

es fo

r 199

8

Negative Correlation

Do we have a measurement issue here?

What does SAT scores actually measure?

• Test performance – at a minimum

• Education – Not always correlated with Knowledge

• Knowledge – Our belief that this leads to Life Success

• Life Success -- This is what we would like to be the case

Be careful of proxies that stand in for other measures

Does School Spending Educate Students?

States spending more per student have lower SAT scores!

Expediture/pupil by State vs SAT

900

950

1000

1050

1100

1150

1200

$4,000 $5,000 $6,000 $7,000 $8,000 $9,000 $10,000 $11,000 $12,000

Expenditure per pupil for public school K-12 (2002-03)

SAT

scor

es fo

r 199

8

Negative Correlation

Do we have a measurement issue here?

No! SAT scores are predictive of Life Success-- financial

• Life Success – college grad, good job

• Not with certainty, but on average

• Should we move to states with lower student spending?

Does School Spending Educate Students?

Does Percent of students taking SAT impact SAT scores?

Correlation Coefficient = .92

Beware the Lurking Variable!

1998 SAT by State

y = 1278x-0.0575

R2 = 0.8461

900

1000

1100

1200

0 10 20 30 40 50 60 70 80 90

% Taking SAT

Com

posi

te S

AT OR

SC

WV

DC

NH

GA

WAAK

COIL

MN

TX

MS

OH

KS

WI

USA

UT

MA

NY

NJ

CT

INHI NC

NV

MT

VT

VT PARI

MSMSMS

Misstep #4 with scatterplots & correlationBack to Height & Weight data• How much would an 100 inch (~2.5meters) person weigh?

80 90 100

300

280

260

240

220

200

•From scatterplot he would weight ~300# (136 kg)

•Can we make this prediction?

Misstep #4 with scatterplots & correlation

• “Predicted” 300 pounds!•Robert Wadlow was 8 ft 11 (2.72 m) and weighed 439# (199 kg)• Interpolating within range of independent variable set -- acceptable•Extrapolating beyond range of independent variable is dangerous

• Relationship may not be stable

Misstep #5 with scatterplots & correlationBack to Height & Weight data• What would conclusion be if height ranged from 1600.0 mm

to 1700.0 mm (64-66 inches)?

• Easily conclude no relationship between height and weight

• Make sure range of independent variable (X) sufficiently large relative to dependent variable (Y)

Misstep #6 with scatterplots & correlationPoor Measurement System• Inappropriate tool or gage to measure

−Pixel width with standard yard/meter stick−Monitor response time with second hand on watch

• Weak tech repeatability−Tech visual determination of damage of NB set in for repair−Typo-graphical errors on a written page

Ensure Measurement System Analysis performed before data are collected

Misstep #7 with scatterplots & correlation

Chicken and Egg Syndrome• Which came first?• What is the cause and what is the effect?

−Do children from poor families do poorly academically because they are poor OR are they poor because of poor academic performance?

−Do consumers buy good product out of loyalty OR are consumers loyal because of good product?

• Vicious/Virtuous Cycles – hard to break through• Relationship is there; Causality is not easily determined

Misstep SummaryWatch out for ….• Bimodal distribution House Size and Price• Stratified data Shoe Size and Knowledge • Lurking variable SAT Scores and Participation Rate

− Underlying third variable− Common but unrelated growth/decay curves− Same variable measured differently

• Extrapolation Height and Weight for tallest man− Generalizing from a sampled subset to a broader, larger population

• Narrow range of X Height and Weight• Sloppy measurement

− Can hide a real relationship− May create one when none exists

• Chicken and Egg Syndrome− Variables are related, but which is cause & which is effect?

Questions?Seven Missteps

Summary• Scatterplots

−Simple, but powerful tool to explore relationships between two quantitative variables

• Be sure data are representative of question −“What are we trying to accomplish?”

• Plot data to look for anomalies or associations• Correlation has special meaning

−Correlation does not imply causation −Nor does lack of correlation deny causation

• Recall Missteps that may impact scatterplot/correlation analysis including lurking variables

Personal Example

Memory Loss Boosts Risk of Death

x

Memory Loss Boosts Risk of Death

Cognitive Impairment

Qty Lifespan median month

Mortality

None 3157 138 57%Mild 533 106 68%

Moderate267 63 79%Severe

Were there any missteps in the analysis?

X

Key Points in Article• About 4000 men & women• Aged 60 to 102 • Indianapolis, Indiana. USA• Started in early 1990’s; ended 2006• Lower socio-economic background

• 10 questions to assess mental status

• Primary care Dr appt• No intervening follow-up of mental assessment

Memory Loss Boosts Risk of DeathPotential missteps in analysis1) Applies equally for men and women?

Stratified data?2) Indy only? OR for all USA? OR World Wide?3) Only applies to those that go to Doctor?4) Socio-economic Background– What role does it

play? Three possible Extrapolations

5) How repeatable was 10 question assessment? Measurement system

6) Why combine Moderate & Severe?7) Depends on fewer points at extreme

Bimodal?8) And the Big Misstep

Lurking variable!!!

Key Points in Article• About 4000 men & women• Aged 60 to 102 • Indianapolis, Indiana. USA• Started in early 1990’s; ended 2006• Lower socio-economic background

• 10 questions to assess mental status

• Primary care Dr appt• No intervening follow-up of mental assessmentCognitive Impairment

Qty Lifespan median month

Mortality

None 3157 138 57%Mild 533 106 68%

Moderate267 63 79%Severe

Cognitive Impairment

Qty Lifespan median month

Mortality

None 3157 138 57%Mild 533 106 68%

Moderate267 63 79%Severe

Memory Loss Boosts Risk of DeathPotential missteps in analysis1) Applies equally for men and women?

Stratified data?2) Indy only? OR for all USA? OR WW?3) Only applies to those that go to Doctor?4) Socio-economic Background– What role does it

play? Three possible Extrapolations

5) How repeatable was 10 question assessment? Measurement system

6) Why combine Moderate & Severe?7) Depends on fewer points at extreme

Bimodal?8) And the Biggie ….

A Lurking variable!!!

Key Points in Article• About 4000 men & women• Aged 60 to 102 • Indianapolis, Indiana. USA• Started in early 1990’s; ended 2006• Lower socio-economic background

• 10 questions to assess mental status

• Primary care Dr appt• No intervening follow-up of mental assessment

Cognitive Impairment

Qty Lifespan median month

Mortality

None 3157 138 57%Mild 533 106 68%

Moderate267 63 79%Severe

Cognitive Impairment

Qty Lifespan median month

Mortality

None 3157 138 57%Mild 533 106 68%

Moderate267 63 79%Severe

Does Cognitive Impairment hasten death? OR

Does Age Boost the Risk of Death?

Questions?