--- In [email protected], akasha_108 <[EMAIL PROTECTED]> wrote: > > I worked a bit to develop a regression model for annual violent crime > data in DC. I sought to create a core model that estimated and > accounted for the basic variations in annual violent crime. Once this > was done, I added ME variables for the intervention year, 1993, plus > lagged ME variables to test if there is a continuing effect of the ME > in subsequent years. > > For the core model I tested if the non-violent aka personal crimes > (PC) were significant in explaining variations in violent crimes. > Both in the current year, and also lagged variables, to see if > personal crimes affected violent crimes in subsequent years. > > I hypothesized that these PCs could be correlated to strong control > variables such as weather, police on the street, LE funding -- but > which I have not yet aquired. This correlation hypothesis makes sense > in that PC variables should also go up in hot weather and down with > increased police and LE $. (And in regression, a variable that is > correlated to a "good" control (independent) variable can be an > effective replacement for the actual control variable.) > > And I pulled down 30 or so national economic variables -- not ideal, > but the best I have at the moment -- no DC specific control data yet. > This national economic variables should correlate, to a degree, with > specific DC economic data, so it is a reasonable first step. > > I found a five variable model with a fairly good fit (1964-2003) with > an adjusted R^2 of .89. That means the model explains 89% of the > variation in violent crime. And each variable was statistically > significant -- the t-values for each var was >2, meaning the is less > than a 5% chance that the variable's contribution is just a fluke, a > random chance effect. > > Several other diagnostics were run: the durbin-watson stat was good, > showing that the model did not have undue levels of autocorrelation > (variables were not correlated to their previous values -- t-1, t- 2, > etc.) And there was low correlation between independent variables, > called non-collinearity -- an important characteristic for models to > have. Another diagnostic, hetroscadasticity was mild. > > So generally, for a quick model, it was fairly strong. > > In the below table, for years 1990-2003 you can see the actual annual > changes in violent crime in DC in the first column, and the estimated > or predicted series from the model in the second column. As you can > see, it generally rises and falls in synch with the actual crime data. > > Having a good core model as a baseline, I then added some ME > variables. First 3 variables -- one for the intervention year, and two > for subsequent years, to see if there were continuing effects. And > then another model with 5 ME variables -- for the test year and 4 > subsequent years. > > The ME effects were interesting. Their effect in the model was to show > about a 4% increase in violent crime from the ME in the test year, and > then a continuing decreasing crime impact of about 5% in the next 2-4 > years. > > The hypothesis could be that in the intervention ME year, things are > stirred up in the "collective consciousness" -- social unstressing so > to speak, and then good effects emerge in the subsequent years. > > However, the significance of the ME variables was weak. There is a > 20-50% chance that they are having no more impact than random chance. > It could jsut be the abortion effect we have discussed (per Levitt) or > other untested factors. Better DC specific data, more economic and > demographic and weather data, and the acquistion of monthly data > should shed light on this and determine better if there is a > significant ME effect that can be demonstrated by this type of analysis. >
You should go outside and see more daylight and sunshine. > For now, its an interesting and thought-provoking result. Haiglin and > all may have been looking at the wrong thing -- current ME effects, > instead of where the action may really be --- future effects. That is, > ME may have its effect via long-term structural changes in collective > consciousness, not immediate ones -- which actually may be negative > (washing machine effect, perhaps). > You mean like when Maharishi said a big Rakshasha hanging over DC had been defeated by that course? OffWorld > > Actual ----- Predicted ---- > No ME ME 3 ME 5 > 1990 14.8% 12.3% 12.0% 7.8% > 1991 -0.2% 1.6% 1.4% 4.4% > 1992 15.5% 4.5% 4.5% 9.6% > 1993 3.1% -2.0% 3.1% 4.9% > 1994 -8.9% -6.2% -8.9% -10.6% > 1995 0.0% 6.3% 0.0% 0.0% > 1996 -7.2% -2.6% -2.6% -0.7% > 1997 -18.0% -15.7% -16.0% -11.1% > 1998 -15.1% -12.6% -12.6% -7.4% > 1999 -5.3% -8.2% -8.1% -6.6% > 2000 -7.4% -5.6% -5.6% -6.1% > 2001 15.2% 8.2% 8.1% 8.0% > 2002 -5.7% -0.5% -0.6% 8.2% > 2003 -1.8% 0.0% -0.1% -7.5% > > > ------ > Estimated Independent Variables > > I Vars Beta Std. Error t-value Sig > LAR 0.146493072 0.086854908 1.68664126 0.101712298 > ROB 0.540438633 0.054472023 9.92139825 3.86814E-11 > MT-2 0.118127037 0.05135057 2.300403597 0.028316925 > UNEI 0.029748611 0.020410475 1.457516846 0.155030356 > ME 0.041842476 0.049411284 0.846820246 0.40358555 > ME-1 -0.035634855 0.050277557 -0.708762667 0.483768091 > ME-2 -0.067917481 0.050595442 -1.342363634 0.189226664 > ME3 -0.0532541 0.049249815 -1.081305573 0.287901536 > ME4 -0.038371741 0.053936408 -0.711425589 0.48213999 > ------------------------ Yahoo! Groups Sponsor --------------------~--> Get fast access to your favorite Yahoo! Groups. Make Yahoo! your home page http://us.click.yahoo.com/dpRU5A/wUILAA/yQLSAA/JjtolB/TM --------------------------------------------------------------------~-> To subscribe, send a message to: [EMAIL PROTECTED] Or go to: http://groups.yahoo.com/group/FairfieldLife/ and click 'Join This Group!' Yahoo! Groups Links <*> To visit your group on the web, go to: http://groups.yahoo.com/group/FairfieldLife/ <*> To unsubscribe from this group, send an email to: [EMAIL PROTECTED] <*> Your use of Yahoo! Groups is subject to: http://docs.yahoo.com/info/terms/
