Page 141 - Coincidences in the Bible and in Biblical Hebrew

COINCIDENCES IN THE BIBLE AND IN BIBLICAL HEBREW
120
120 COINCIDENCES IN THE BIBLE AND IN BIBLICAL HEBREW
Thus, the combination of the moon’s size and its distance from the earth (both
relative to the sun’s) causes the moon to appear the same size in the sky as the sun.
This is one reason we can have total solar eclipses.

The two seemingly contradictory sentences in the first chapter of the Bible

now make sense. The first sentence talks about reality as observed by mortals
on earth in ancient times (namely, prior to the invention of the telescope). The
second sentence addresses reality as it is: the sun is larger than the moon.
The only question left open: How was it known in ancient times to earth-
bound observers which “light” is bigger and which “light” is smaller?

8.3 Predicting Diameters

8.3.1 What a Linear Regression Model Implies (Here)

A statistical analysis has been conducted to find out whether the size of the moon,

the earth, and the sun can be predicted from the numerical values of their respec-
1
2
tive most common Hebrew names—namely, yareach (moon), Eretz (Earth) and
7
shemesh (sun).
Linear regression analysis was applied to the data. However, before we detail
the analysis and its results, some general explanation is needed regarding the
implication of obtaining, from the analyses implemented in this book, statistically

significant linear regression models .
There are altogether over fifteen statistical analyses in the book (for their ocations

l

refer to the list of figures, adjacent to the table of contents). All analyses share two

properties in common. First, they all use a linear regression model, with a single

independent variable (regressor ). Secondly, all models use as the regressor (the pre-
dicting variable) numerical values of the relevant Hebrew words, where these values
are the total sum of the numerical values of the respective constituent letters.
What does a statistically valid linear model imply?

For a statistically significant linear regression model with one independent
variable , a linear relationship implies that the response (the dependent variable ) is

a linear transformation of the regressor . This implies that both variables represent

the same “entity”—yet they differ by location and scale . In other words, the two
variables are one and the same, differing only in their measurement scale.
To demonstrate what is meant by difference in location and scale, suppose that
at a speed of V kilometers per hour, a driver travels in his of her car from city A to
city B. The distance between these cities is D AB. After time T, the driver wishes to
inform of his or her location. He or she may do that by saying how far he or she is
from city A, in which case the position, P A, is specifi ed as