Saturday, November 28, 2009

Removal and Installation of the Catalytic Converter on a SAAB 9-5

I fouled the stock converter on my SAAB while trying to recover from a failed turbo. The car ran poorly, with a rough idle, stalling, surging, and there was a lack of power and decrease in fuel mileage. A stock replacement catalytic converter from SAAB was around $1500. A 3 inch downpipe and race cat from GenuineSAAB.com was less than $600. It arrived at my door less than 48 hours after I ordered it.



Removing the old converter was straightforward. I drove the car onto ramps, blocked the rear wheels, set the brake and disconnected the battery. I also disconnected the wiring harnesses for the two O2 sensors. These are located near the firewall, next to the throttle body and brake reservoir.



There is no need to remove the oxygen sensors until the entire downpipe is loose. Remove the heat shield and pipe connecting the diverter valve to the turbo's cobra.



There are three nuts / threaded studs that connect the turbo to the downpipe. Remove these, but keep the old downpipe supported on the threaded studs that remain. I had two nuts come loose and one threaded stud come loose. It really doesn't matter.

Under the car there is a metal bracket with a bolt or two that will need to be loosened and there are three nuts that need to be removed where the downpipe meets the resonator in the middle of the car. Once these are loose you can start wiggling the old downpipe and converter  until it is free and can be slid out from under the car.



Once the old pipes are loose you can remove the oxygen sensors. I used liquid wrench and an open adjustable wrench with a rubber mallet to get mine off. This would be a good time to replace the two O2 sensors, if necessary.

The sensors are then installed on the new downpipe and installation under the car is done in reverse. The metal bracket by the oil sump is no longer used with the race cat. The O2 sensors might need to be rerouted differently to reach the wiring harness, but that's no big deal. Use the threaded studs at the turbo and resonator as extra hands to adjust the pipe into place. Take your time and tighten the bolts completely once in place to close any exhaust leaks.

Here is the inside of the racing cat.



This project gives you an opportunity to check on the health of your turbo. Here is the inside of my turbo, one year after it was installed.



The project was simple and the results were immediate. The car needed a working converter. The upgrade to a 3 inch downpipe brings me closer to the ability to go from my current stage-1 to a stage-3, once I get the correct software from BSR, Aero cobra, and a larger cat back exhaust.

-P. Econmancer

Thankful For My Peek Mobile Email Device

Next month I will have owned my Peek email device for one year. It was given to me as a gift last Christmas. There are very few items in this world that I feel are worth writing a post about; the iPod Touch, the Canon G10, and maybe the rubber broom we got from the "As Seen On TV" aisle at Target (those brooms truly are awesome).

The Peek deserves a spot with this list.



The Peek, so you don't have to google it, is a pocket sized device that sends a receives email. That's all.  It uses the same towers as T-Mobile phone service. You get your email anywhere you can get a mobile phone signal. There is no need for wi-fi hotspots. I've found that I even get Peek service where mobile phones are on the edge of a signal.

So why would I want it? I want it because it does mobile email perfectly. I want it because smart phone voice & data plans are expensive. I want it because I hate talking on the phone. I honestly spend around $3.00 a month on pre-paid mobile minutes for my Virgin Mobile phone. I want it because I can bide my time and give one precise response when I send an email, instead of having to immediately deal with someone's problem back-and-forth over the phone. "But wait", you say, "text messages can do a lot of these things". That's true, and my Peek also does text messages wonderfully.

My Peek has saved the day on several occasions. The best moment so far was when my family and I set out from our hotel in West Memphis towards Graceland and soon realized that none of us knew where it was beyond the general "Elvis Presley Boulevard". I emailed snapask.com for the Wikipedia page on Graceland. Moments later the article, along with the street address, was in my hand and we were on our way.

My Peek updates my Twitter account and my Facebook status. I get news feeds from my Facebook and Twitter. The Instant weather alerts from NOAA are great to have in the springtime (I live in Tornado Alley). I get maps, movie times, weather forcasts, all with quick emails to various free services.

My Peek is like my first iPod. It does on thing perfectly and I never expected  it would change my life as much as it has.

The customer service from Peek is at a Zappos.com level. I emailed support once and got a quick response with the information I needed. I called once to upgrade to lifetime service and the person on the phone was actually at Peek Inc, instead of some faceless call center. Peek has held several contests for users; during March Madness I predicted who would win the championship and Peek sent me a metal water bottle as a prize.

I purchased an upgrade cable from Peek so I could keep my software up to date and get the latest features. Doing it myself was easy enough, but they offer free upgrade service by mail if you don't mind giving up your device for a few days. Their customers are very active in the Peek community. People hold upgrade parties and release their own services for the Peek, such as maps and Facebook feeds. I was lucky enough to get a lifetime warranty on my Peek during a special event. I have a feeling the warranty was a one-time thing, but this means I have lifetime service and lifetime warranty on my Peek for less than $300.

The device itself is priced so low that I have an extra one still in the box for the day the battery, case, or charger wears out on my current Peek.

The Peek is not for everyone. Hell, I still keep my plastic mobile phone with mediocre voice, T9 text on squishy buttons, and a battery that needs to be recharged every night with me for emergencies. But the phone sits in my pocket while the Peek, with a sturdy metal shell, full Qwerty keyboard with clicky keys, and a battery that stays strong for days, is constantly being used.

(I am only a satisfied Peek client that loves his Peek. I received no compensation from Peek for this post. There is a generated link to Amazon below that would earns a commission from Amazon if an item is purchased.)

From Amazon:
Peek Pronto Mobile Messaging Device with Lifetime Service Included (Grey)


-P. Econmancer

Thursday, November 26, 2009

Statistics and Online Matchmaking

The dating site OKCupid maintains a blog about the data they collect from their users. OKTrends has some extremely entertaining posts, and they all happen to be about statistics. The site has data from 100 times the number of people polled for nation-wide Gallup poles (300k people v. 3000 people).

Commenters point out that, as opposed Gallup polling random people, OKCupid polls only members. The people who are members of OKCupid could be very different than the people who are not. Still, it's a lot of data and it's interesting to see differences between people by state and gender. For example; the length of a introductory message and the chance of a response, how different religions and races interact on the site.

I found the site through a post on BoingBoing. The interesting part of the post was how men and women respond to attractiveness. Men gave a fairly even bell-curve to the female attractiveness on the site. Men replied more often as the attractiveness of the woman increases, until the woman was so attractive that messages dropped off. This is about what I'd expect. Guys like attractive women and many guys would feel they had no chance with the extremely attractive memebers, so they wouldn't even try.

Woman, however, were different. The women on the site found very few males on the site to be attractive (rating 80% below average in looks) and tended to message the slightly below average men the most.

The discussion on the BoingBoing  post and the original post by OKTrends are both worth reading if you are interested. There is a lot of discussion about why women found so many of the men below average in looks. An important part of that discussion has to do with how OKCupid works. I don't understand it fully, I'm not a member, but from what I read in the comments there is a system that automatically alerts a person if you rate them at a certain level and above. The commenters suggested that women are intentionally not  selecting higher levels to avoid unwanted contact from the men they are rating. Another commenter complained that they system "baits and switches" when you are rated high by someone, by showing other profiles with the one person that thought you were attractive. The woman said she rates all of those people as" unattractive" in protest of this feature.

Sunday, November 22, 2009

Engine Displacement and Automotive Performance



I entered all of the data from the SCCOIA website into a spreadsheet and used a random number generator to pick a sample of fifty vehicles from the list of 1845. I then found the cubic inch displacement for each of the vehicles that were randomly chosen.

I made graphs of the displacement (x) and 1/4 mile times times (y), and displacement (x) and 0-60 mph times (y), then looked at the linear regression of the data. Please remember that this does not take into account the weight of the vehicles, gearing, tires, or countless other factors that change the performance of a vehicle.





Interpretation of R and R^2

The correlation coefficient of R is -.4739 for 1/4 mile times, and -0.4791 for 0-60 times. This indicates a negative linear association between the displacement of an engine and its track times. It makes sense that as the displacement gets larger, the times get smaller (quicker).

The coefficient of determination, R^2, is .2246 for 1/4 mile times and .2295 for 0-60 times. This indicates that about 22.5% - 23% of the performance times from these cars are accounted for by the displacement of the engines. This leaves 77% - 78% of the variation in the residuals.

t-Scores and P-Values

The t-scores are 28.95 (1/4 times) and 13.45 (0-60 times) and the P-values are 0.0000000000000000000000000000000048 (1/4 mile times) and 0.00000000000000000071 (0-60 times). This shows that the slope for either line is not zero.

Some interesting highlights from the random sample

The largest displacement was a 1973 Pontiac Firebird with a 455ci engine. The smallest displacement was a 1992 Geo Metro LSi with 61ci engine. The quickest 0-60 time was a 2002 Porsche 911 GT2 with 3.6 seconds. The slowest was a 1967 MG Midget III with 14.7 seconds. The quickest 1/4 mile time was the 2002 911 GT2 with 11.9 seconds. The slowest was the 1992 Geo Metro LSi with 19.4 seconds. The average car from the random sample would have a 212.28 cubic inch engine, go 0-60 in 8.02 seconds and have a 1/4 mile time of 15.91 seconds.

Most Frequent Lotto Numbers

I took ten years of Power Ball data and found the five most frequent lotto numbers and the one most frequent Power Ball ( these are also known as the mode).



Too bad the Power Ball doesn't have a memory.

From Amazon:
Millions: A Lottery Story

TDS Water Test



I have a little device that tests the TDS or "Total Dissolved Solids" in liquids. I decided to test the various water sources in my kitchen. The tester gives a number between 0-999 and one of five faces. 0-30 is a :D face, 31-100 is a :) face, 101-200 is a :| face, 201-300 is a :-? face, and 301-999 is a :( face.

I started with hot tap water because I thought it would be the highest, and I was correct. I got a 350 from my sample.



I then got a sample of cold tap water. It was also very high at 330.



Our refrigerator has a filter connected to it, so I tested the water from it. I was disappointed to see that it was 300.



We don't usually use the water from the fridge, we have a Britta water filter pitcher that we drink from. Luckily it seems to be a better job of filtering out the solids in the water. The sample showed a 190.



I took an ice cube from a bag of store-bought ice and let it melt in a clean baggy so I could test the water from it. I was pleasantly surprised to see that ice from the store had only a 3.



While I was in the freezer getting the Ice, I also pulled out a bottle of Cachaca and decided to test a sample of it with the meter.



It has a TDS of 20.



We didn't have any bottled water in the house at the moment, so that was one sample I wish I could have also tested. I thought about testing my spit, but I have a feeling I already know the results and it would have been a gross thing to do anyway.

From Amazon:
TDS Tester TDS-3 for commercial use

Linear Regression: Diamond Carat Weight and Price



I was interested in seeing exactly how the weight of a diamond relates to the price. I gathered some data and did a simple linear regression.

I collected a random sample of loose diamonds listed for sale on BlueNile.com. The sample was taken from all “round” cut diamonds graded as having clarity with very slight inclusions (VS1) and color ranging from D-H (near colorless) and having carat weights of .25-.50. Blue Nile had 614 individual stones that matched these categories. I labeled the diamonds 1-614 and took a random sample size of 40 using a random number generator. I then collected the size in carats (x) and price in dollars (y) of the selected diamonds. The data is listed below.

Size(ct.) Price ($)
0.27 509
0.27 509
0.28 518
0.25 582
0.31 597
0.35 660
0.36 661
0.32 670
0.32 670
0.36 678
0.3 696
0.36 700
0.33 727
0.28 747
0.31 782
0.31 782
0.3 800
0.33 808
0.33 829
0.3 849
0.37 851
0.32 852
0.32 859
0.39 890
0.39 890
0.41 981
0.4 1017
0.4 1068
0.41 1071
0.39 1113
0.39 1113
0.42 1119
0.42 1310
0.42 1331
0.41 1398
0.43 1476
0.42 1516
0.46 1543
0.46 1595
0.48 1803

Interpretation of Regression Coefficients.



The y-intercept, b0= -836.79 and the slope b1= 4950. These numbers might seem unusual, but it’s easy to think about what they mean when you use the y-intercept and slope to calculate that each .01ct increase of weight adds $49.50 to the predicted price of these diamonds.

Interpretation of R and R^2

The correlation coefficient of R is .8836 and indicates a strong positive linear association between the weight of a diamond and its price. The coefficient of determination, R^2, is .7807. This indicates that about 78.1% of the cost of these diamonds is accounted for in the weight of the stones. This leaves 21.9% of the variation in the residuals. This is the linear association expected from the scatter plot.
Sample Predictions of Price from Weight

The regression equation can now be used to predict the price of a diamond by its weight. For the example we use a diamond that is .25 carats because it is within the domain of the regression line.

4950.6(.25)-836.79=400.86

So it would be reasonable to expect to pay $400.86 for a .25 ct diamond.

None of the random diamonds in this data set were .5 ct, but I can use the equation to predict a price:

4950.6(.5)-836.79=1638.51

The equation predicts the cost of a .5 ct diamond to be $1638.51, so that is a price that I could reasonably expect to pay for a .5 ct stone.

Confidence Interval for Predicted Mean Value

There is 95% confidence that the average price of a .25 ct diamond falls between $293.22 and $508.50. The interval is not too broad and gives an idea of the average prices for a diamond that is .25 ct.

Prediction Interval for Individual Predicted Value

There is 95% confidence that the price of a .25 ct diamond will fall between $234.91 and $566.95. This interval is once again not too broad and gives a rough idea of the prices for a .25 ct stone, with the understand that selling a stone for less than $234.91 is pricing the diamond too cheap and more than $566.95 for a .25 ct diamond is overpriced.
Confidence Interval for Slope.

There is 95% confidence that the slope of the true regression line is between 4086.51 and 5814.77. This means that we can be 95% confident that the price of a diamond rises between $40.87 and $58.15 for every additional .01 ct in weight. It can be concluded that, because zero is not in the interval, there is a positive linear association between the variables.

Hypothesis:
Ho: B1== 0 The null hypotheses is that there is no linear association between the price of a diamond and its weight. (slope is zero)

Ha: B1=/= 0 The alternative hypotheses is that there is a linear association between the price of a diamond and its carat weight. (slope is not zero)

Model:
All of the linear regression t-test conditions are met
The scatter plot appears linear
The residual plot has no apparent pattern
The residuals are relatively spread consistently
The normal probability plot appears basically straight

Mechanics:
The statistics in the test have been calculated by Gnumeric. The statistics are also calculated below.

t=b1-0/SE(b1)= 4950.64/425.68=11.63

P=P(|t|>11.63)=4.37E-014 or about 0.000000000000004

Conclusion:
With this very small P-Value, the null hypothesis is rejected. The probability of calculating a slope of 4950.64 if the actual slope is zero is extremely small. This serves as significant evidence that there is a positive linear relationship between diamond weight and price.

From Amazon:
14k White Gold, Round, Diamond Stud Earrings (1/3 cttw, K-L Color, I3 Clarity)