Christmas Cream

Here’s the recipe for the “Christmas Cream” adult beverage that I whipped up for my wife-  seems to be a big hit with her lady friends.


Single Serving:

1/4 c ‘natural’ coffee creamer

1/4 c milk

1 T Creme de Cacao

2 t Whipped Cream Vodka

2 t Peppermint Schnapps

Mix ingredients and serve over ice.


Party Sized Recipe

1 quart heavy cream

1/2 gallon skim milk

(makes 1 1/2 gallon half-and-half; use that if it’s less expensive)

2 1/4 cup sugar

2 T vanilla extract

1.5 c Creme De Cacao

1 c Whipped Cream Vodka

3/4 c Peppermint Schnapps

1.  Heat milk and cream to 180 degrees (bubbles around the edge).

2.  Add sugar and mix to dissolve.

3.  Add vanilla, mix in.

4.  Mix in alcohols.

5.  Bottle if making ahead and refrigerate before serving over ice.



Concealed Carry Control vs. Violent Crime Rates by State

Question: Does the control of concealed carrying (of handguns) have an impact on violent crime?

Hypothesis: Increased control of concealed carrying will cause an increase in violent crime.

Method: Values were assigned to the level of control in a State as follows: 0 for no control, 1 for shall-issue control; 2 for may-issue control; 3 for no-issue control.  Data was plotted on two axes: one for all violent crime, one for homicide.  Sources: control, homicide, violent crime.  Control level was coded by statute, to avoid bias in subjective interpretation of practice.  Linear regressions were run for both data series to determine the coefficient of correlation.  LibreOffice 3.5.7 was used for charting and calculations.

Results: Both data sets show a weak correlation between increased control and increased rates.  R2 for violent crime is .17, for homicide it’s .14.  In no case did the least violent State in a group have a lower crime rate in either series than the lowest State in the less restrictive groups.

Conclusion: Restrictions on concealed carry may cause an increase in crime, but other cultural factors are probably more significant in determining the overall difference in crime between States.  In no State has increased control lowered crime rates below that of all States with less control – adding control on concealed carry cannot be justified as a valid approach for improving crime rates.

On GMO Labeling

I don’t have a strong opinion as to whether GMO foods are dangerous or not.  In fact, I think the question is wrong – it seems most likely that some modifications could be harmful while others could be harmless.  I’m fairly certain that BT sprayed on an apple tree in the spring is not harmful to humans, but I’m not certain that BT-toxin expressed by the apple and present in the eaten food is harmless to humans.  For some modifications it might be that both ‘conventional’ pesticides and GMO-expressed pesticides are both harmful, one may be more harmful than the other, or that organic is the only safe way to go.  But not eating vegetables because of the price of organic may be worse.  Science should inform this, but it seems to be incomplete at this time.

The separate issue of labelling has important consequences.  In the US, a Natural Rights Republic, the issue of Free Speech is a very important one.  It’s incredibly dangerous to tread on it for some perceived short-term benefit.  For that reason I’m glad the California proposition to mandate labelling failed (whether it really did or not is a separate issue). Compelled speech is one of the worst kinds of free speech infringements.

But the root of the problem lies not in compelled speech, but restrictions on free speech imposed by the FDA.  It forbids companies from putting “GMO Free” on their products, so voluntary labelling can’t happen. They told Polaner (All Fruit maker) that they couldn’t put “GMO Free” on their strawberry spread because a strawberry is produce, “not an organism”. They told Spectrum (oils refiner) that their No-GMO label would imply that there is something wrong with GMO’s so they couldn’t use it.

I’d like to have more information on the foods I buy at the store.  It’s clear that ‘the market’ wants to provide it.  Freedom of speech isn’t just a good idea, it’s the Supreme Law.  It’s time the FDA stopped breaking it.

I Didn’t Vote

I didn’t vote because of the new NH VoterID law.  If voting is a right you don’t need to show an ID.  I don’t need to show a bureaucrat an ID to go into church, I don’t need to show an ID to speak on the street corner, and Part 1st , Article 11 says that elections are to be “Free”, as in free speech, free exercise of religion, a free press, etc.  Any extant voter fraud was smaller than the margin of error by counting, so there was no problem actually being solved  – it was pure authoritarian thuggery by the Republican majority (an attempt to disenfranchise student voters, mostly).

Oh, and the affidavit? – if I showed up to vote constitutionally and signed their paper (say it was offered by the poll workers or Town Moderator who all know me by name) then they’ll be sending follow-up letters, starting investigations, and charging me with a crime for not playing their repression game.  As if that’s a ‘free’ option.

If voting is merely a privilege now, then I want no part of it, as the system of government we supposedly have is based on a right of representation.  Perhaps it always was a privilege, but now the charade is up.  “Papers, please.”

Fructose-free Chocolate Milk Recovery Drink

yield: 1 gallon (2 1/4c dry mix)


1 1/3 c dextrose

2/3 c dark cocoa (Dutch process preferred, e.g. Special Dark or better)

2 T stevia/erythritol blend (to make blend: 1/2 t pure stevia extract per cup erythritol)

2 t xanthan gum

2000 IU Vitamin E succinate

1 gallon skim milk

2/3 c hot water

method: mix powders in blender to break up clumps. Add hot water. Mix to form paste. Add milk to target full blender (n.b. expands by about 1.5x with air) and mix until fully blended. Pour contents of blender back into gallon of milk and shake.

usage: 1oz drink per 7.5lbs of ideal body weight as soon as possible after lifting.

Can Facebook Predict the NH Primary?

Can we predict the NH Gubernatorial primary outcomes by watching campaign page statistics on Facebook?

Here we compare the ‘Likes’ and activity ‘people talking about this’ for each page – one month before the primary (8/8 and one day before the primary (9/10):

First the Republican race: Ovide had and has tremendously more likes than Smith. Smith has a better slope on Like count (1.3 vs. 1.1) but the difference is so large as to not matter. Similarly, the activity slopes favor Smith (2.4 vs. 1.7) but the magnitude is again so much different that it won’t matter. Smith is very popular among some, but not enough.

The Democratic race gets less attention on Facebook in general and is a bit closer (Hassan has roughly half the lead over Cilley as Ovide does over Smith). Hassan has a better slope (1.4 vs. 1.2) on Likes but Cilley has a better slope on activity (2.1 vs. 1.6). Cilley will pose a stronger challenge to Hassan than Smith will to Ovide, but it will not be enough to win it for her.

Using the perhaps-bogus method of multiplying the current likes by the activity slope, and doing percentages (should probably use a standard normal table instead) the outcomes will be:

Republican: Ovide: 73%, Smith 27%

Democrat: Hassan: 67%, Cilley 33%

We’ll find out tomorrow to what degree Facebook pages reflect the voting population at large and just how bad that calculation is. New Hampshire has such a small percentage of party voters that it’s probably not useful to extend this to the general election at this time.

Update: actual results:

Ovide: 67.7%, Smith: 29.8%

Hassan: 53.1%, Cilley 38.9%

Not bad considering how “WILDLY WRONG” some prominent politicos declared the predictions of this method!

Crime Rates and Police Officers by State (Data Visualization)

The previous post looked at the police to population ratio, which raises the question: how do we measure the appropriateness of those ratios? One way to look at that is to see how much crime is in a State, see how many officers there are for the population, and compare that with other States’ outcomes. If we follow the “laboratory of Democracy” model, with each State coming to independent conclusions, a common optimal solution ought to be found over time, perhaps with some States getting it wrong.

Again, using the FBI numbers, we can see such a pattern emerging in a data visualization of the numbers. Indeed, forty-five of the Fifty States are clustered in a tight cloud, and therefore seem to have arrived at very similar outcomes, if not solutions. Five states plus DC are outliers. Errors could be found in three directions – too much crime, too little staffing, or too much staffing (few would argue for ‘too little crime’). These errors can occur independently on both axes. The numbers are only as good as the reporting methodology, but the best we have.

Oregon and South Dakota deserve special praise for having the most efficient policing solutions. Oregon for the lowest crime rate at the lowest-cost end of the group, and South Dakota for achieving the lowest crime rate and being below the median staffing levels.

To figure out which of these errors is occurring in a State, if any, we can draw a bounding box on the tight cluster of States (shown here in yellow). The attributes of outliers in either of the North, South, East, or West directions can be considered normal within those bounds. Those in the NW, NE, SE, SW directions can be considered errors. Note: we’ve already declared the South direction (too little crime) to not be an error, and none of the States make the error in the West direction (too little staffing as an outlier).

Where States are making multiple errors, to discern the primary error we can draw a linear regression trendline through the data set. Those above the trend line have an error primarily in the Y-axis measure; those below the trendline have primarily an X-axis error.

Given those criteria, we can draw the following conclusions about the outliers:

South Carolina: properly staffed, crime is too high. Primary problem: crime rate.
New York, New Jersey: over staffed, respectable crime rates. Primary problem: overstaffing.
Vermont: overstaffed, too high a crime rate. Primary problem: crime rate.
Louisiana: overstaffed, much too high a crime rate. Primary problem: crime rate.
D.C.: tremendously over-staffed, too high a crime rate. Primary problem: overstaffing.


The States with a primary problem as their crime rate need to look at the factors that are causing their crime rates. Are the police operating effectively? Is there corruption within the police force? Are the police over-policing and thus creating a reporting anomaly? Are there poverty problems caused by a lack of education or a high tax rate? Vermont and Louisiana should reduce their staffing levels to at least 2.9. South Carolina may want to increase its staffing levels towards 2.9 until the crime problem is solved.

The States (New York and New Jersey) and D.C., with staffing levels as their primary problem, should work to immediately reduce the cost to taxpayers of superfluous police forces. Returning that money to the productive economy will do more to decrease poverty than it’s doing with an inordinately high level of police staffing, which should have a positive impact (decrease) on crime rates. Target 2.9 as a first step, verify the crime rates remain consistent or decrease, then move towards the median level of 2.2 per thousand.

D.C. deserves special mention because it’s such a mess on both axes.  It may face special challenges as being a city-district (future work should compare it with other cities specifically) but placing D.C’s crime rate on a straight line between South Carolina and Louisiana (which it lies between in terms of crime rate) excuses a staffing level of no more than 3.2.

Further work should look at factors such as population density and tax rates to see what impact they have on the crime rates and see if the outliers would be brought back into the ‘normal cloud’ if their numbers were adjusted for the Fifty-States trend for those two factors.