My occupation is a statistician. I tell people it is like "CSI without dead bodies" because analyzing a set of data that has been collected is like doing an autopsy on a deceased person in the sense that I'm trying to learn what I can from what statistics and information are available. Except in this case the information does not involve gross things. For me the research process can be humorous, scary, but always captivating.
Wednesday, July 15, 2020
A Look at Positive COVID-19 Testing Rates in Cambria, PA, and the US
Friday, April 10, 2020
The Number of Corona Virus Cases in Cambria County has Grown Exponentially While Health Behaviors Predict Cases in PA
Number of Corona Cases |
Corona Deaths |
|
Length of Life Z-Score |
0.046 |
0.067 |
Quality of Life Z-Score |
0.286 |
0.284 |
Health Behavior Z-Score |
-0.038 |
0.065 |
Clinical Care Z-Score |
-0.059 |
0.114 |
Social Economic Z-Score |
0.301 |
0.412 |
Physical Environment Z-Score |
0.062 |
-0.449 |
Number
of Corona Cases |
1.000 |
0.957 |
Corona
Deaths |
0.957 |
1.000 |
population |
0.841 |
0.792 |
Friday, December 19, 2014
My 15 Minutes (5 really) of Fame at the Warhol
I did a screen test at the Warhol Museum. He said "everyone has their 15 minutes of Fame." The screen test was five minutes with no sound. There is no point to the video. Just like the countless others who have posted. He did make a point with this image of Nixon which got him audited by the IRS but that is as close to offering a solution in his work as he got.
Warhol's art pokes fun at our culture and it's obsession with stuff. He offered no solutions which is why I present the video below which does.
**Related Posts**
Podcamp Session Feedback Part 2-The Video of My Session Has Been Posted
PodCamp 6 Interview
A Kinder, Gentler Looney Tunes
What is Sanity?
Sunday, September 28, 2014
The Fourth Year of CSI wo DB
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Sep 29, 2013 - Sep 28, 2014
Sep 29, 2012 - Sep 28, 2013
|
10. First Time I Heard Multivariate Analysis and Multicollinearity on Mentioned on TV
A humorous look at statistical issues thanks to the Daily Show.
9. Two Years Ago in Stanton Heights
This post from 2011 still gets some traffic on the shooting of three police officers in my neighborhood by a right wing radical.
8. Income and Life Expectancy. What does it Tell Us About US?
My all time most read post on life expectancy and income thanks to a link on the BBC website for the program The Joy of Stats. The link is not there anymore but it still gets some traffic.
7. Global Warming, Wikileaks, and Statistics: What Barry Sanders Can Teach Us
6. Hitler, Napoleon, and Stalin: Outsider Despots
This post is from this year on the history of three outsiders who exploited power vacuums to become absolute rulers of their countries. Their similarities and differences are described.
5. The World Wars and Today's Wars
This post is related to the number 6 post on this list as it is the 100th anniversary of the First World War and many of today's problems in the Middle East are tied to what happened 100 years ago.
4. Bullying & Society
A post from 2010 where I argued that bullying is a reflection of society's greater ills.
3. A Geographical Represenation of the Mode and Ethnicity
A post from last November on ethnicity in the United States and how it corresponds to other regional differences.
2. Correlation with the Number of Hate Groups per Million, Poor Health Suggests More Hate
A look at the concentration of hate groups in each state and health outcomes.
1. A Wave of Hate Groups in California? No in Washington, DC
This post managed to make the all time most viewed list. The number of hate groups in the US in each state is standardized by the size of each state's population. The results are surprising.
**Related Posts**
Three Years of CSI Without Dead Bodies
The Second Year of CSI without Dead Bodies
One Year of CSI Without Dead Bodies
My (Quarter Year of) Blogging in Review
CSI senza cadavere (my first post)
Friday, March 21, 2014
Correlation with the Number of Hate Groups per Million, Poor Health Suggests More Hate
The state with the highest previous rate of 23.72 groups per million was the District of Columbia. One possible criticism is that they have a large African American population and that they are not technically a state. If the four black separatist groups in DC are excluded from their total of 15, it still has a rate of 17.40 groups per million which is well above the national rate. I decided to look at which other state level variables are correlated with the rate of hate groups in each state.
I combined this data set with a state level health and income data set and several of them are significantly correlated with the health measures. The strongest of these effects was the one between infant mortality and hate groups per million accounting for 40.9% of the variability. In the chart on the left, DC is an outlier on both variables.
The correlation was rerun with DC excluded. The relationship was still significant but with 12.3% of the variability accounted. This indicates that the relationship is weaker with DC excluded but still present.
The relationship between hate groups and state level life expectancy was also significant with 29.4% of the variability accounted in a negative relationship where as the number of hate groups increases, the state's life expectancy decreases. Like the previous graph, DC is an outlier on hate groups per million. When DC is removed from the graph, 30.2% of the variability is accounted for in a relationship that is still negative. This suggests that DC has high influence but is not poorly fit to the data.

There is a more advanced method that can identify clusters of highly correlated variables. It is called factor analysis. There were two factors extracted which account for 68.8 % of the variability. They are presented in the table below.
Rotated
Factor Matrixa
|
||
Factor
|
||
Health
(46% of
var explained)
|
Income
(22% of
var explained)
|
|
Infant Mortality 2007 Deaths/1000
|
.909
|
|
Life Expectancy
|
-.817
|
-.462
|
% Low Birthweight Babies
|
.735
|
.245
|
Hate Groups per million
|
.709
|
|
Percent under age 65 in 200% of Poverty
|
.411
|
.862
|
Income
|
.140
|
-.727
|
Percent Uninsured in Demographic Group for All Income Levels
|
.140
|
.644
|
Expanding medicaid
|
-.314
|
|
Extraction Method: Principal Axis Factoring.
Rotation Method: Varimax
with Kaiser Normalization.a
|
||
a. Rotation converged in 3 iterations.
|
The first factor extracted has the health related variables loading on it and accounts for 46% of the total variance. Infant mortality, life expectancy, % low birth weight babies, and the rate of hate groups load most strongly on this factor. Percent within 200% of poverty, income, and % uninsured load most strongly on the second extracted factor (called an income factor) while accounting for 22% of the variability.
The hate group rate does not load on the income factor but it does on the health suggesting an association with health related outcomes. One must always be careful about inferring a cause and effect relationship based on correlational data. When DC was removed, the factor analysis did not run.
**Update**
Mark Potok of the Southern Poverty Law Center discusses the rise in hate groups and the prominence of Overland, Kansas shooter Frazier Glenn Miller. Missouri, where Miller was living, had a rate of 3.82 hate groups per million and has life expectancy of 76.8 years with a ranking of 38th . Kansas had a rate of 1.73 hate groups per million with a life expectancy rating of and a ranking of 27th.