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On 2012-06-15 12:47, Robert Peirce wrote:
If I use a small amount (3 pairs) of dummy information, the intercept,
slope and R^2 are exactly what I expect them to be.  If I use something
on the order of 400 pairs it makes no sense at all.  When I use Linest,
the results are reasonable.

I am evaluating stock trading systems against holding the market.  The
data pairs are the monthly percentage change in the model vs. the
market.  A regression produces the y-intercept (called alpha), the slope
(called beta) and R^2.

Generally, if I know the total return for the model and the market the

    "total model return" = "total market return" * beta + alpha
    y = mx + b

is fairly close if I get the results from linest.  They are way off from
intercept and slope.

If the dependent variable is in A and the independent in B and there are
400 pairs, I can use INTERCEPT|SLOPE(A1:A400; B1:B400).  The results
aren't even close and I don't know why.  Plugging the average returns
for the market into the equation to calculate the expected return for
the model produces nothing reasonable.

Any ideas?  I am probably missing something really obvious.

I filled a column X with =RAND()*500 and a column Y with =B476*2+RAND()*2+5
Column B is the X values. The slope looks about right as does the intercept (keep refreshing with F9). I created a chart and added the trend line and the trend line matches the functions SLOPE and INTERCEPT.
R squared is 0.999

Opensuse LO 3.4.5


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