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About two years ago I decided to read the entire Bible. There are many ways to accomplish this task and I chose a Daily Reading plan served up to my RSS reader courtesy of The ESV Bible. When I started my second round of reading the Bible I increasingly used the audio version. This was much easier on those days when I was tired, unmotivated, and procrastination seemed to rule the day. About a month ago I started downloading podcasts to my phone. I play the reading on my way to work. This had been a very pleasant and fulfilling way to listen to the Bible and take advantage of the lost time during the morning commute. I enjoyed this endeavor so much I decided to listen to free audio books in the afternoon commute. In the morning I listen to the daily Bible reading and in the afternoon I listen to my daily reading of Pride and Prejudice courtesy of AudioOwl. It seems so appropriate that Pride and Prejudice is read by a woman with a British accent.

There are two things that are apparent from these graphs.

1. When you compare this recession to the other recessions in the study, the drop in GDP matches the largest drop in recent history. The job loss is the largest in recent history. This leads me to conclude that based on the economy’s performance so far, the monetary and fiscal policy decisions from 2007 to the present are probably the most ineffective policy decisions in the last sixty years.
2. The GDP and employment data says that the 2007 recession has a special relationship with jobs. When I plotted the GDP and employment data on the same graph, I was amazed that the jobs and GDP data were following almost identical paths. Conventional wisdom says jobs and the economy should be related but the 2007 recession has a remarkably close relationship. For kicks I ran a regression analysis on the recent recessions in the study. Surprisingly several recessions demonstrate almost no relationship between jobs and GDP while both the 2001 and 2007 recessions showed a close relationship. Is this a natural result as our economy transforms from a manufacturing economy to a service economy?  One of the more interesting economic questions for 2010 is what will the recovery to the 2007 recession look like in 2010 and 2011. Will it look like the 1981 recovery with strong growth in jobs as the the economy took off or will it look like the 2001 recovery in which jobs continued to decline while the economy gradually increased. Based on the close relationship of job and GDP in the last two recessions and the inability of the government to encourage permanent job growth in 2009, my bet is that the recovery in 2010 will look a lot like the 2001 recovery but with a GDP that never fully recovers the ground lost in the recession and a whole lot of fiddling with the employment data to avoid discussing the increasing amount of permanent job losses.

Below is a graph with both the jobs and GDP on the same graph for the 2001 and 2007 recessions. I used the data from the Federal Reserve of Minneapolis. (HT Ed Morrissey at Hot Air for the original article and the Bizzy Blog for pointing the article out to me.)

With all of the talk about the Climategate emails I devised a simple thought experiment for scientists. Let us imagine that you are teaching a college science class and you have assigned a science project to your students that will comprise the majority of their grade for the semester. The objective of the project is demonstrate the student’s knowledge of the material you are teaching, their ability to follow the scientific process like a scientist, and their ability to defend their conclusion in front of their fellow students. At the end of the semester you find one student who appears to have reasonable results but they lost the raw data and the method they transformed the raw data into the data that supports their conclusion. When you ask the student how they know the conclusion is correct, their primary defense is that it matches the results turned in by other students. During the defense of the project the other students do not seem to be bothered about the missing raw data and the method the data was transformed. So what grade do you give this student?

• You give the student an ‘A’ or a ‘B’ since they got the right result. Their defense of the conclusion was excellent despite the missing data and lack of questions by the other students.
• You give the student a ‘C’ or an ‘Incomplete’ since the conclusion was indefensible without the raw data and method used to transform the data. You are bothered that the other students were not more aggressive pursuing the questioning of this project. You have this uncomfortable sense of collusion amongst the students but you are unwilling to judge whether the student’s actions were deliberant or an unfortunate accident.
• You give the student a ‘F’ since you believe the student deliberately subverted the scientific process. You want to send a message to the students in the class and to those students planning on taking this course to forewarn them that this behavior will not be tolerated.

I am beginning to think that this global warming crisis is a sinister plot by statistics professors to get more students to  take statistics courses. Despite my misgivings about the true motives of global warming I will take the opportunity to give a big thanks to Luboš for encouraging me to refresh my knowledge of regression analysis. In his post, The Reference Frame: No statistically significant warming since 1995, he shows a simple example of regression analysis of temperature anomalies. Since I am Mathematica challenged I opted to use a method a high school student would probably be familiar with, I used Excel. Without much effort I was able to quickly plot add a trend line to the Excel chart with the following equation on the chart, y = 0.0095x + 0.12 with a R² = 0.0889. Luboš was kind enough to provide me with a link to the AP Tutorial on Statistics so that I would be reminded on the prerequisites for regression analysis. The Coefficient of Determination or otherwise known as R² is a key output of regression analysis. To paraphrase the AP Tutorial for this case, the variance of the temperature anomalies are 8.9% predictable from the time variable. Ugh! So whether you look at the confidence interval, Standard Error, or R², it is reasonable to conclude that the temperature data is really ugly and to agree with Luboš that the underlying trend in 1995-2009 was "somewhat more likely than not"  a warming trend rather than a cooling trend.

For kicks I decided to follow up on a comment Bret made on this same article posted on the Watts Up With That? blog concerning using monthly data. I found some suitable monthly temperature data at http://www7.ncdc.noaa.gov/CDO/CDODivisionalSelect.jsp, imported it into Excel, and plotted it. Here is the result.

Some people may complain that this chart is a nonsensical plot since the seasonal variations overwhelms the temperature trend. On the other hand it highlights how small the slope of the trend line is compared to the seasonal variations. Regardless of which estimate of the temperature increase you choose, it is still a very small number compared to the seasonal temperature variations. Another complaint might be that I should have used temperature anomalies rather than the actual temperature data. This begs the question whether you can have a warming trend that shows up in the anomalies that doesn’t show up in actual temperature data. Oh-oh! I think I should move on to less contentious subjects.

Since I was on a roll I decided to go one step further and analyze the temperature data like it was weather. Oops! I put climate change and weather in the same sentence. Every time I see or hear a weather forecast they discuss the high and low temperatures for the day. I do not remember ever hearing a weather forecast that included the average temperature for the day. In some cases a weather forecast will include an average high or  low for the day. Hence I created a plot of the maximum and minimum monthly temperature for each year. This appealed to both my pragmatic view and my engineering background. If we can imagine that our climate is a control system, a control engineer would primarily be interested in setting control limits on the maximum and the minimum values. Surely we should get a better regression analysis using the annual maximum and minimum values.

Once again despite selecting a subset of the monthly temperature data that  should have had a better chance of a statistically relevant temperature trend, the regression statistics continue to make it difficult about drawing conclusions about warming or cooling trends. The R² value for the maximum temperature and minimum temperature trends are better than the raw monthly trend but they are still very low. Once again the underlying trend is "somewhat more likely than not"  a warming trend rather than a cooling trend. The R² value has improved but with it  so low, we are still saying very little.

Here are some more thoughts to ponder.

1. The range in the system from high to low temperature, 44 degrees, is very large compared to the warming trend(~ 1 degree/century). My inner engineer is having trouble understanding why a climate system that can handle a 44 degree swing in temperatures is having problems with a warming trend of about a degree per century.
2. Are the problems we are seeing with the R² value in our regressions analysis a natural result of the errors(noise) in the system of measuring temperatures? As an example if our temperature measurements are limited to an accuracy of plus or minus one degree, are we not fundamentally limited by the measurement errors in the system. What can we say about warming trends of one degree per century if our measurement errors are likely to be as large as the trend?
3. How low does a R² value or confidence limits in trends need to go before we decide to pack it in and say we do not know what is happening?

Finally for a humorous look at the scientific process courtesy of xkcd.com we have the Science Montage.

The article published at Pajamas Media, Climategate Computer Codes Are the Real Story, is the best explanation of why the global warming scientists have been so passionate in avoiding the Freedom of Information act requests. These scientists knew they were in a lose-lose predicament. They knew they were brushing up with the law if they ignored the freedom of information act requests but they definitely did not want Steve McIntyre to look at the raw data and the programs and go ballistic. So they decided to replicate the results using synthesized data and prayed that they could put off the freedom of information act requests indefinitely.

As an IT guy in my day job I have seen this problem and it is called lax change management. In the professional programming world there are a multitude of horror stories associated with lax change management. This problem has been attributed to major cost overruns, project cancellations, and occasionally the failure of a company. To avoid these types of problems most companies have instituted a very simple and effective philosophy. You skirt the rules of change management and something goes wrong, you are fired!

Somewhere some important people are gathering and trying to figure out what to do with these scientists and their projects. Global warming science is important but are these scientists “too big to fail”? I am not sure an audit or internal investigation will satisfy either the politicians or the public. Such a waste of time and money! This is going to get uglier before it gets better. There are probably several ideas the scientific community probably needs to implement to avoid disasters like this in the future. As a starter maybe the scientific community will finally open its publicly funded projects to outside scrutiny much earlier in the game.

Wow! I could almost vote Republican after this exchange between Congressman Burgess and Treasury Secretary Geithner before the Joint Economic Committee. Since I work for a small business I would say Congressman Burgess is just touching the surface of the fear running rampant through small business owners. Surviving the next six months is scary enough. The recycled Keynesian economic policies of this administration remind us why our government has not used them for over forty years. They just don’t work!

BURGESS: What’s happening in small businesses is people are frightened to add jobs, because they don’t know what we’re going to do to them in health care. They don’t know what we’re going to do to them in financial regulation. They’re scared of what we might do with energy prices in the future with cap and trade. Small business — medium sized business is frightened at jobs right now.

I could help the president and his panel. He doesn’t need another program. We don’t need another stimulus. We need to provide some tax relief and then get the heck out of the way, and the American economy will recover as it has always done.

GEITHNER: That broad philosophy helped produce the worst financial crisis and the worst recession we’d seen in generations. We had a pretty good test of that philosophy — a pretty good test of those policies that did not serve the country well. Now…

BURGESS: Mr. Geithner, when I came here in 2003, we were in a jobless recovery. Tax relief was passed in May of 2003, and as a consequence by July of that year, we were adding jobs at a significant rate. It seems to have worked fairly well.

Geithner on the Hot Seat
John
Fri, 20 Nov 2009 01:35:33 GMT

Shiller is not certain the housing market has bottomed, and says he thinks home prices could fall back. He predicts that housing prices will be much more volatile going forward, and that five years from now, home prices will probably be at about the same level they are today.

Robert Shiller Interview Housing Prices

A friend at work mentioned that he was looking at a propane powered smoker being sold at Walmart. He thought it was a good deal, $108, since the smoker had such good reviews. I took a look at it and agreed. So I bought it. My first test was pulled pork using an America’s Test Kitchen recipe. Yup, this makes barbecuing much easier. No reloading the charcoal after a couple of hours with this bad boy. I think I will smoke a turkey for Thanksgiving.

I tried this last week and the wife liked it. I think my son will like it, too. It is a nice combination of sweet and spicy. It is also easy enough to prepare for a weekday meal.

Updated. Originally posted in 2006.

This cashew chicken recipe is one of my favorites on the site, and not just because it hails from my friend Marc Canter who I don’t see nearly enough. I first posted it several years ago after a raucous and memorable dinner that Marc prepared, accompanied by his wife Lisa and their kids Mimi and Lucy (any dinner with little kids tends to be raucous and memorable, don’t you agree?) Marc had been serenading me with stories of his famous cashew chicken, his trademark dish, tested and perfected over decades of crashing at the homes of old and new friends in exchange for his cooking. One of the things I like so much about Marc’s cashew chicken is that it is a good base recipe from which one can easily expand. Several people suggested the addition of some ginger and onion greens, which I agree works well and I’ve added in this updated version as an option. I’ve also enjoyed it with some fresh chopped pineapple thrown in, giving it some sweetness. Others have added vegetables such as broccoli or snow peas. Note that the amounts are all approximate. Marc doesn’t really measure; like most natural cooks I know, he "eyeballs" it. But with these ingredients, in approximately the right proportions, it’s hard to go wrong.

Marc’s Cashew Chicken

You can whip this up pretty quickly if you first start with the marinade, then chop the vegetables and cook the cashews while the chicken is marinating. By the way, several people have suggested toasting the cashews in lieu of boiling them. I think that’s a great idea; it will bring out even more flavor and the cashews will be crunchy. To toast them, heat a skillet on medium high heat, add the cashews, stir until lightly browned.

Ingredients

• 4 skinless, boneless, chicken breasts (about 1 1/2 to 1 3/4 pounds total), cut into 1-inch cubes
• Peanut oil (about 3/4 cup, can substitute other vegetable oil)
• Chili Powder (about 3 Tbsp)
• Tamari (about 1/2 cup) (if you don’t have access to tamari you can substitute soy sauce, use Wheat-free tamari or soy sauce if you need to cook gluten-free)
• Honey (about 1/2 cup)
• 2 cups raw cashews
• Salt
• 3 cups roughly chopped onions (about 2 medium large onions)
• 3 cups roughly chopped mushrooms

Optional

• 1-2 teaspoons minced fresh ginger
• 1/4 cup chopped green onion greens

Method

1 Marinate the chicken. Place the cubed chicken in a medium bowl. Add the oil. Add the tamari until the marinade turns dark brown (about 2 Tbsp per breast). Sprinkle the chili powder over the chicken pieces while stirring, so that each piece of chicken gets well coated with the chili powder and marinade. Stir in the honey, about 2 tablespoons for each breast. Add chopped ginger if using. Marinate for 1/2 hour to several hours, the longer the better.

2 Place cashews in a saucepan, cover with water, add a teaspoon of salt. Bring the water to a boil and simmer until the cashews are soft, a couple of minutes (the water will get foamy). Remove from the heat, strain and set aside.

3 Heat a large skillet on medium high heat. Working in batches if needed so you don’t crowd the pan, use tongs to remove the chicken pieces from the marinade and place them in the pan, reserving the extra marinade. Sauté the chicken pieces until just cooked through, remove from the pan and set aside. Place any extra marinade back in the pan and simmer for several minutes (to kill any bacteria). Pour off all but 1 Tbsp of the marinade into a separate bowl and reserve.

4 In the same pan, sauté the onions on medium high to high heat for several minutes. Add mushrooms and continue to sauté until onions are translucent and mushrooms are cooked, several minutes more. Add some reserved marinade to the pan if necessary.

5 Combine chicken, mushrooms, onions, with the cashews. Stir in onion greens (if using) right before serving. Serve over rice.

Serves 4-6.