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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4) Lot of old and outdated data quoted selectively.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 |
* 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% |
* 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
|
Definition Source  Printable version |
|||||||||||||
|
||||||||||||||
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 |
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
| 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 |
|
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Related Articles
- More Infringements on Our Gun Rights (lewrockwell.com)
- Rep. Giffords Joined 276 Americans that Day Killed or Wounded by Guns (grantlawrence.blogspot.com)
- Myth About Gun Violence (socyberty.com)
- Tell the Arizona Shooting Victims That Guns Don’t Kill (thedailybeast.com)
- Jacob M. Appel: Want a Gun? Get a Prescription! (huffingtonpost.com)
- America and Guns, Once More (nytimes.com)
- Gun Control Timeline: 7 Big Events In The Federal Gun Control Debate (huffingtonpost.com)
- After Tucson: Why Are the Mentally Ill Still Bearing Arms? (time.com)
- Guns (fishofgold.wordpress.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
Related Articles
- A workflow for R (revolutionanalytics.com)
- Statistical analysis as journalism – Benford’s law (onlinejournalismblog.com)
- Using Box-and-Whiskers Plots (brighthub.com)
- Managing a statistical analysis project – guidelines and best practices (r-statistics.com)
Business Intelligence and Stat Computing: The White Man’s Last Stand
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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
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.
Related Articles
- Black Male Students Doing Worse Than Bad [Education] (gawker.com)
- SAP Still Winning Business Intelligence Trench War Against Oracle, IBM (blogs.forbes.com)
- “New Report on Black Male Achievement Reveals Jaw-Dropping Data” and related posts (d-edreckoning.blogspot.com)
- Getting the Word Out: Improving Access to Data Throughout Your Company (informationweek.com)
- Business Intelligence and the Kung Fu Dragons of Wudang (oracleprophet.wordpress.com)
Business Intelligence and Stat Computing: The White Man's Last Stand
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
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.
Related Articles
- Black Male Students Doing Worse Than Bad [Education] (gawker.com)
- SAP Still Winning Business Intelligence Trench War Against Oracle, IBM (blogs.forbes.com)
- “New Report on Black Male Achievement Reveals Jaw-Dropping Data” and related posts (d-edreckoning.blogspot.com)
- Getting the Word Out: Improving Access to Data Throughout Your Company (informationweek.com)
- Business Intelligence and the Kung Fu Dragons of Wudang (oracleprophet.wordpress.com)
