On January 26, 2012 President Obama flew to Arizona. Governor Jan Brewer greeted him at the airport, and pretty near shoved her finger in his face. This provoked a frenzy of charges that she is racist. However none of the news coverage beat the Torah Code, which seems to have predicted the incident over 3,300 years ago. On the matrix below the axis term is JAN BREWER at the 3rd shortest skip of the name (in wrapped Torah). The open text word FINGER touches her name. At the same absolute skip as her name (but opposite direction) is TO OBAMA.
STATISTICAL SIGNIFICANCE OF THE MATRIX. As per my standard protocol, no statistical significance is assigned to the axis term, here JAN BREWER at the 3rd shortest skip. On the full 400 letter matrix odds against finding FINGER in the open text were about 42 to 1. If nothing else were shown but this a-priori term, then we could say that odds against it being so close (in 40 letters) to the Governor's name would be about 141 to 1. But the full matrix also showsTO OBAMA at the same absolute skip as her name. Here some caution is required with respect to odds on two counts. First, the letter lamed (which means TO) was only noticed after I searched for OBAMA. So the more honest odds ignoreTO. Second, the preferred spelling of OBAMA has a hey for the last letter, but (shown here) alef works too. So we must use frequencies for both spellings. As such, the odds against a 400-letter matrix having FINGER in the open text and OBAMA at a special case skip (+/- 1 or the absolute skip of the axis term) were about 1,367 to 1. If I had originally sought TO OBAMA then the odds against this matrix would have been set at about 10,887 to 1. Either way, the matrix is very significant.
This matrix is one of an ever growing series of matrices that have Obama parallel to, and often at the same absolute skip as the axis term. He seems to be encoded too often at the focal point of an extremely important moment in history. A few matrices that fit the parallel requirement for the axis term with Obama follow: