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Assumptions on Guns

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While sitting in Delhi, India- I sometimes notice that there is one big new worthy gun related incident in the United States every six months (latest incident Gabrielle giffords incident) and the mythical NRA (which seems just as powerful as equally mythical Jewish American or Cuban American lobby ) . As someone who once trained to fire guns (.22 and SLR -rifles actually), comes from a gun friendly culture (namely Punjabi-North Indian), my dad carried a gun sometimes as a police officer during his 30 plus years of service, I dont really like guns (except when they are in a movie). My 3 yr old son likes guns a lot (for some peculiar genetic reason even though we are careful not to show him any violent TV or movie at all).

So to settle the whole guns are good- guns are bad thing I turned to the one resource -Internet

Here are some findings-

1) A lot of hard statistical data on guns is biased by the perspective of the writer- it reminds me of the old saying Lies, True lies and Statistics.

2) There is not a lot of hard data in terms of a universal research which can be quoted- unlike say lung cancer is caused by cigarettes- no broad research which can be definitive in this regards.

3) American , European and Asian attitudes on guns actually seem a function of historical availability , historic crime rates and cultural propensity for guns.

Switzerland and United States are two extreme outlier examples on gun causing violence causal statistics.

4) Lot of old and outdated data quoted selectively.

It seems you can fudge data about guns in the following ways-

1) Use relative per capita numbers vis a vis aggregate numbers

2) Compare and contrast gun numbers with crime numbers selectively

3) Remove drill down of type of firearm- like hand guns, rifles, automatic, semi automatic

Maybe I am being simplistic-but I found it easier to list credible data sources on guns than to summarize all assumptions on guns. Are guns good or bad- i dont know -it depends? Any research you can quote is welcome.

Data Sources on Guns and Firearms and Crime-

1) http://www.justfacts.com/guncontrol.asp

Ownership

* As of 2009, the United States has a population of 307 million people.[5]

* Based on production data from firearm manufacturers,[6] there are roughly 300 million firearms owned by civilians in the United States as of 2010. Of these, about 100 million are handguns.[7]

* Based upon surveys, the following are estimates of private firearm ownership in the U.S. as of 2010:

Households With a Gun Adults Owning a Gun Adults Owning a Handgun
Percentage 40-45% 30-34% 17-19%
Number 47-53 million 70-80 million 40-45 million

[8]

* A 2005 nationwide Gallup poll of 1,012 adults found the following levels of firearm ownership:

Category Percentage Owning 

a Firearm

Households 42%
Individuals 30%
Male 47%
Female 13%
White 33%
Nonwhite 18%
Republican 41%
Independent 27%
Democrat 23%

[9]

* In the same poll, gun owners stated they own firearms for the following reasons:

Protection Against Crime 67%
Target Shooting 66%
Hunting 41%

2) NationMaster.com

http://www.nationmaster.com/graph/cri_mur_wit_fir-crime-murders-with-firearms

VIEW DATA: Totals Per capita
Definition Source Printable version
Bar Graph Pie Chart Map

Showing latest available data.

Rank Countries Amount
# 1 South Africa: 31,918
# 2 Colombia: 21,898
# 3 Thailand: 20,032
# 4 United States: 9,369
# 5 Philippines: 7,708
# 6 Mexico: 2,606
# 7 Slovakia: 2,356
# 8 El Salvador: 1,441
# 9 Zimbabwe: 598
# 10 Peru: 442
# 11 Germany: 269
# 12 Czech Republic: 181
# 13 Ukraine: 173
# 14 Canada: 144
# 15 Albania: 135
# 16 Costa Rica: 131
# 17 Azerbaijan: 120
# 18 Poland: 111
# 19 Uruguay: 109
# 20 Spain: 97
# 21 Portugal: 90
# 22 Croatia: 76
# 23 Switzerland: 68
# 24 Bulgaria: 63
# 25 Australia: 59
# 26 Sweden: 58
# 27 Bolivia: 52
# 28 Japan: 47
# 29 Slovenia: 39
= 30 Hungary: 38
= 30 Belarus: 38
# 32 Latvia: 28
# 33 Burma: 27
# 34 Macedonia, The Former Yugoslav Republic of: 26
# 35 Austria: 25
# 36 Estonia: 21
# 37 Moldova: 20
# 38 Lithuania: 16
= 39 United Kingdom: 14
= 39 Denmark: 14
# 41 Ireland: 12
# 42 New Zealand: 10
# 43 Chile: 9
# 44 Cyprus: 4
# 45 Morocco: 1
= 46 Iceland: 0
= 46 Luxembourg: 0
= 46 Oman: 0
Total: 100,693
Weighted average: 2,097.8

DEFINITION: Total recorded intentional homicides committed with a firearm. Crime statistics are often better indicators of prevalence of law enforcement and willingness to report crime, than actual prevalence.

SOURCE: The Eighth United Nations Survey on Crime Trends and the Operations of Criminal Justice Systems (2002) (United Nations Office on Drugs and Crime, Centre for International Crime Prevention)

3)

Bureau of Justice Statistics

see

http://bjs.ojp.usdoj.gov/dataonline/Search/Homicide/State/RunHomTrendsInOneVar.cfm

or the brand new website (till 2009) on which I CANNOT get gun crime but can get total

http://www.ucrdatatool.gov/

Estimated  murder rate *
Year United States-Total

1960 5.1
1961 4.8
1962 4.6
1963 4.6
1964 4.9
1965 5.1
1966 5.6
1967 6.2
1968 6.9
1969 7.3
1970 7.9
1971 8.6
1972 9.0
1973 9.4
1974 9.8
1975 9.6
1976 8.7
1977 8.8
1978 9.0
1979 9.8
1980 10.2
1981 9.8
1982 9.1
1983 8.3
1984 7.9
1985 8.0
1986 8.6
1987 8.3
1988 8.5
1989 8.7
1990 9.4
1991 9.8
1992 9.3
1993 9.5
1994 9.0
1995 8.2
1996 7.4
1997 6.8
1998 6.3
1999 5.7
2000 5.5
2001 5.6
2002 5.6
2003 5.7
2004 5.5
2005 5.6
2006 5.7
2007 5.6
2008 5.4
2009 5.0
Notes: National or state offense totals are based on data from all reporting agencies and estimates for unreported areas.
* Rates are the number of reported offenses per 100,000 population
  • United States-Total –
    • The 168 murder and nonnegligent homicides that occurred as a result of the bombing of the Alfred P. Murrah Federal Building in Oklahoma City in 1995 are included in the national estimate.
    • The 2,823 murder and nonnegligent homicides that occurred as a result of the events of September 11, 2001, are not included in the national estimates.

     

  • Sources: 


    FBI, Uniform Crime Reports as prepared by the National Archive of Criminal Justice Data


    4) united nation statistics of 2002  were too old in my opinion.
    wikipedia seems too broad based to qualify as a research article but is easily accessible http://en.wikipedia.org/wiki/Gun_violence_in_the_United_States
    to actually buy a gun or see guns available for purchase in United States see
    http://www.usautoweapons.com/

    Statistical Analysis with R- by John M Quick

    I was asked to be a techie reviewe for John M Quick’s new R book “Statistical Analysis with R” from Packt Publishing some months ago-(very much to my surprise I confess)-

    I agreed- and technical reviewer work does take time- its like being a mid wife and there is whole team trying to get the book to birth.

    Statistical Analysis with R- is a Beginner’s Guide so has nice screenshots, simple case studies, and quizzes to check recall of student/ reader. I remember struggling with the official “beginner’s guide to R” so this one is different in that it presents a story of a Chinese Army and how to use R to plan resources to fight the battle. It’s recommended especially for undergraduate courses- R need not be an elitist language- and given my experience with Asian programming acumen – I am sure it is a matter of time before high schools in India teach basic R in final years ( I learnt quite a shit load of quantum physics as compulsory topics in Indian high schools- but I guess we didnt have Jersey Shore things to do)

    Congrats to author Mr John M Quick- he is doing his educational Phd from ASU- and I am sure both he and his approach to making education simple informative and fun will go places.

    Only bad thing- The Name Statistical Analysis with R has atleast three other books , but I guess Google will catch up to it.

    This book is here-https://www.packtpub.com/statistical-analysis-with-r-beginners-guide/book

    Business Intelligence and Stat Computing: The White Man's Last Stand

    Unknown White Male

    Image via Wikipedia

    Name an industry in which top level executives are mostly white males, new recruits are mostly male (white or Indian/Chinese), women are primarily shunted into publicity relationships, social media or marketing.

    Statistical Computing And Business Intelligence are the white man’s last stand to preserve an exclusive club of hail fellow well met and lets catch up after drinks culture. Newer startups are the exception in the business intelligence world , but  a whiter face helps (so do an Indian or Chinese male) to attract a mostly male white venture capital industry.

    I have earlier talked about technology being totally dominated by Asian males at grad student level and ASA membership almost not representing minorities like blacks and yes women- but this is about corporate culture in the traditional BI world.

    If you are connected to the BI or Stat Computing world, who would you rather hire AND who have you actually hired- with identical resumes

    White Male or White Female or Brown Indian Male/Female or Yellow Male/Female or Black Male or Black Female

    How many Black Grad Assistants do you see in tech corridors- (Nah- it is easier to get a  hard working Chinese /Indian- who smiles and does a great job at $12/hour)

    How many non- Asian non white Authors do you see in technology and does that compare to pie chart below


    racist image Pictures, Images and Photos

    Note_ 2010 Census numbers arent available for STEM, and I was unable to find ethnic background for various technology companies, because though these numbers are collected for legal purposes, they are not publicly shared.

    Any technology company which has more than 40% women , or more than 10% blacks would be fairly representative to the US population. Anecdotal evidence suggests European employment for minorities is worse (especially for Asians) but better for women.

    Any data sources to support/ refute these hypothesis are welcome for purposes of scientific inquiry.

    racist math image Pictures, Images and Photos

    Business Intelligence and Stat Computing: The White Man’s Last Stand

    Unknown White Male

    Image via Wikipedia

    Name an industry in which top level executives are mostly white males, new recruits are mostly male (white or Indian/Chinese), women are primarily shunted into publicity relationships, social media or marketing.

    Statistical Computing And Business Intelligence are the white man’s last stand to preserve an exclusive club of hail fellow well met and lets catch up after drinks culture. Newer startups are the exception in the business intelligence world , but  a whiter face helps (so do an Indian or Chinese male) to attract a mostly male white venture capital industry.

    I have earlier talked about technology being totally dominated by Asian males at grad student level and ASA membership almost not representing minorities like blacks and yes women- but this is about corporate culture in the traditional BI world.

    If you are connected to the BI or Stat Computing world, who would you rather hire AND who have you actually hired- with identical resumes

    White Male or White Female or Brown Indian Male/Female or Yellow Male/Female or Black Male or Black Female

    How many Black Grad Assistants do you see in tech corridors- (Nah- it is easier to get a  hard working Chinese /Indian- who smiles and does a great job at $12/hour)

    How many non- Asian non white Authors do you see in technology and does that compare to pie chart below


    racist image Pictures, Images and Photos

    Note_ 2010 Census numbers arent available for STEM, and I was unable to find ethnic background for various technology companies, because though these numbers are collected for legal purposes, they are not publicly shared.

    Any technology company which has more than 40% women , or more than 10% blacks would be fairly representative to the US population. Anecdotal evidence suggests European employment for minorities is worse (especially for Asians) but better for women.

    Any data sources to support/ refute these hypothesis are welcome for purposes of scientific inquiry.

    racist math image Pictures, Images and Photos

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