glejser test for heteroscedasticity using equally spaced regressors?
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See p. 541 of Econometric Foundations by Mittelhammer, Judge and Miller.  Will be a bit tough to explain on Yahoo.  Actually, I don't like this book, but none of my other ones have this.
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Oy that is a great question.  I take it you taking a risk managment or a derivative course! ;)  Basically you would have to put in functions (functions of interest and volatility variations) for both and then evaluate it accordingly.  It is nearly impossible to do by hand but computer can provide you with some good insight :).  That's when 3D probability that engineers love so much kicks in.  \n\nRival
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Hi! I'm a statistician and I use http://www.statsoft.com as well as cheap textbooks from Half Priced Books in my area. Both seem to help. If you have a specific question, feel free to email me. Don't know how much good I'll be because I use fancy software for my analysis, but I'll try.
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I'm wondering why you created the data 1-3500?  I would think you would use the set of data available and figure the standard deviation and average.  With that information, you would be able to predict tomorrows value at a certain confidence level.  I forget is that the p test or the t test.  its been a while since I took statistics.
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h= (C*I/A+T/k)*z*log(.7*y)
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Try Chi square testing, it starts out complicated but once you understand it, it is way faster than punnett squares! You can find all kinds of stuff about it on the internet, it's a statistical analysis.
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The bias of large outliers.
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A relatively low cost, easy to operate video signature comparator and a corresponding method by which a known signature is visually compared to a sample signature to determine whether both signatures were made by the same individual. The signator comparator has particular application to forgery detection or to applications for verifying the authenticity of a signature. An electronic circuit is disclosed which displays video signals that are representative of the known and sample signatures. The circuit enhances video image density variations through extreme contrast and brightness adjustment and forces the lighter regions of each signature to white, such that only the dark stroke lines (indicative of those regions having the greatest density where the maker applied the most pressure) are displayed on video monitor. By visually comparing the characteristic density/pressure variations of the known signature to the sample, an observer can more accurately determine whether both signatures originated from the same maker.
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The variables invovled can be correlated.  Below is a link for the multiviate normal.  Notice that the variance-covariance matrix Σ is not restricted to be diagonal; it can be a general matrix, it just has to be positive semi-definite.  Note that if |Σ| = 0, then the pdf does not exist.  Anyway, the link includes the pdf (in the positive definite case of course), bivariate case, and the conditional distribution, which is also normal.  I have been using this distribution a lot in my research.
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http://www.utexas.edu/its/rc/tutorials/stat/spss/spss1/
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There is no accurate way to do this because too many variables are involved. You can do like I do though and run a series of tests and then average them.\n\nhttp://www.toast.net/
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Any equation explaning the relationship between two or more observations (or data points) could be called a regression. It's really an extrapolation of the underlying line (or non-linear relationship) that explains the variance.\n\nPerhaps you could do a simple one- pick a stock and use the closing price over a period (say 3 weeks, 15 observations). Then try to come up with a formula to explain the pattern.\n\nActually, that one is kind of hard without stats software. But there are others- such as average temperatures in a given area...
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http://en.wikipedia.org/wiki/Econometrics\nPlease click the above link to know in detail.
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on the web\nHoover's\nHarte-Hanks\nGardner Group
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Use 'Gaussian elimination' to obtain a basis for the span of matrix columns or rows.\n\nhttp://en.wikipedia.org/wiki/Gaussian_elimination
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See the link below. A .315 avg and .976 fielding pct. not too shabby
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Succinctly, for the normal ditribution, the three averages are equal, that is the mean, median and mode are all the same. Not so in other cases.