Running Eclipse CDT on Mac OS X 10.9 – problem with creating binary

If you are getting the error “launch failed. binary not found” when trying to run C++ code in CDT on Mac OS X you need to:

  1. Install the Xcode Command Line Tools on a Mac (this can be installed manually through the Apple developer link at or through download tab under preferences in Xcode)
  2. The compiler should point to “/Applications/” the old path was /usr/include

Things to look for in an experiment

Sometimes the outcome of an experiment is a very unexpected result for the researcher. This happens often in running imperial results on economic theories. Peter Kennedy lists the source of such problems and examples in his paper, “Oh No! I Got the Wrong Sign! What Should I Do?” by Peter Kennedy. Here is a summary of the most important things that a researcher should look for when running an experiment.

  • real versus nominal: using real interest rate rather than nominal interest rate
  • defining learning: “researchers regressed learning, measured as the difference between posttest and pretest scores, on the pretest score (as a measure of student ability) and other explanatory variables, obtaining a negative sign on pretest”. For example “the true specification may be that the posttest score depends on the pretest score with a coefficient less than unity. Subtracting pretest from both sides of this relationship produces a negative coefficient on pretest in the relationship connecting the score difference to the pretest score.”
  • forward looking: for example having making predictions speculation when there is a forward looking in the data

I have to reiterate the quote that peter brought in his paper from Thaler’s (2000, 139) remark that “Some economists seem to feel that data-driven theory is, somehow, unscientific. Of course, just the opposite is true.”

Hoover (1995, 243) “ . . . data mining is misunderstood, and once it is properly understood, it is seen to be no sin at all.” Needless to say that the experiment design and researcher’s approach in arriving at a conclusion play a key role in making statistical analysis and data mining methods a useless endeavor or a powerful tool.

A ground breaking result in data mining!

Be hold, the most ground breaking results in data mining is finally revealed! Today, I finally got the results of a decision tree that I was making for classification on a dataset and the results were amazing! After using a data set of many samples (let’s say 175738 observations) and many variables (let’s say 69) and having the code running for the past four days the incredible decision tree tells me that only one variable was used and that was the SYMBOL in the dataset!! Assume you have thousands of samples from cow, sheep, horse, mouse, and dog and the task is building a model for classification of proteins. Then your model tells you that it is ignoring all the variables but the name of these animals (this would be SYMBOL variable in my dataset)! how amazing :)) It is funnier to see that in the comments I wrote “the SYMBOL variable should probably be removed” :)))