I want to calculate correlation coeffient,r of the ff sets of data for my research using Pearson Product Momet
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It is explained here:\n\nhttp://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient
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Try standardandpoors.com and also you can get it on Yahoo and also run a stock screener to get a list of lets say companies with highest beta. Please remember that beta by itself is not a sufficient enough measure of correlation since the r^2 could be very low meaning that the beta is irrelevant
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Sorry, but I think you need to do this yourself.  Here are some guidelines though; give them a try.\n1.  Make a line drawing so you can see the info you're given and the path to the required answer.\n2.  Make a list of ALL the related formulae you were given.  Check each one against the given data; if you find one in which you have all the variables EXCEPT one, substitute and solve for the unknown.\n3.  Sometimes, you may have to use one formula to find a variable and THEN use that in ANOTHER formula to arrive at your ansver.\n\nGood luck!!
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You should look at the hamming distance between the two binary vectors, which is defined to be the number of coordinates that  have different values.  So in your example:\n  0001111000001100\n  0011110000001000\nhave 3 coordinates that have different values and\n  0011100000000000\n  0000000001111111\n10 coordinates that have different values. \n\nIf you define the correlation to be the number of coordinates in total minus the coordinates that have different values, you'll have a high correlation for the first pair, and a low correlation for the second pair.
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(1) The correlation answers the STRENGTH of linear association between paired variables, say X and Y. On the other hand, the regression tells us the FORM of linear association that best predicts Y from the values of X.\n\n(2a) Correlation is calculated whenever:\n* both X and Y is measured in each subject and quantify how much they are linearly associated.\n* in particular the Pearson's product moment correlation coefficient is used when the assumption of both X and Y are sampled from normally-distributed populations are satisfied \n* or the Spearman's moment order correlation coefficient is used if the assumption of normality is not satisfied.\n* correlation is not used when the variables are manipulated, for example, in experiments.\n \n(2b) Linear regression is used whenever:\n* at least one of the independent variables (Xi's) is to predict the dependent variable Y. Note: Some of the Xi's are dummy variables, i.e. Xi = 0 or 1, which are used to code some nominal variables.\n* if one manipulates the X variable, e.g. in an experiment. \n\n\n(3) Linear regression are not symmetric in terms of X and Y. That is interchanging X and Y will give a different regression model (i.e. X in terms of Y) against the original Y in terms of X.\nOn the other hand, if you interchange variables X and Y in the calculation of correlation coefficient you will get the same value of this correlation coefficient.\n\n(4) The "best" linear regression model is obtained by selecting the variables (X's) with at least strong correlation to Y, i.e. >= 0.80 or <= -0.80 \n\n(5) The same underlying distribution is assumed for all variables in linear regression. Thus, linear regression will underestimate the correlation of the independent and dependent when they (X's and Y) come from different underlying distributions.
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Matlab, Gauss, S+ and probably a lot more.\n\nIn a pinch, you can also use MS-Excel.\nDownload this pdf file from Cliff Ragsdale´s book site:\nhttp://swlearning.com/quant/ragsdale/chapter12.exe\nIt actually works better than you´d think.\n\nOr go to http://swlearning.com/quant/ragsdale/fourth_edition/ragsdale.html\npick previous edition and student resource.
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If you have access to a copy of the ARRL handbook, there are several nomographs in there for RF calculations.  Not exactly a slide rule, but it will do the job.  I copied the graphs and laminated them.  Then I use a dry erase marker to line up the data points and read the results.
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http://www.aea-bank.com/calc/CompoundSavings.html
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Excel is a great tool.... I'm sorry I don't remember all the specifics but I know that there is more than one way to do it.  If you can narrow down which variables you expect to be most related that would be a great start.  If you don't have a physical (real-life) basis for that, you can try graphing all 6 variables against a column just numbered with integers from 1 to n (the number of data in each column).  Then you can plot pairs of columns against each other and find equations to fit the curves (polynomial up to 6th order, exponential, logarithmic) and use the R squared values to determine how well the equation represents the correlation of the data.  (R squared = 1 is a perfect match.)  Now when you start getting into cases where there are multiple independent variables effecting the dependent variables, it gets a little more complicated, and unfortunately I don't have Excel on this computer and I don't have my old notes or textbooks with me.  I hope this at least helps you get started.
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No problem \n\ngo to google and search under "Z table" +statistics. \n\nYou'll find a lot.
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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 no of degrees of freedom for a chi-square distribution ia n-1
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Try this website for help with your statistics homework:\n\nhttp://www.statsoft.com/textbook/stathome.html
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in Excel, use NormDist() function to answer a and b,\nNormInv() for c and d, and read your textbook for e and f.\n\nyou will fail final exam if you do not understand how these things work, and if you want me to help you more, it will be $25/hour.
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correlation coefficient i really couldnt tell you a formula, but there is a button on your graphing calculator that tells you it, correlation coefficient is a number from 0 to 1, stating how close a best fit line is to a series of points, 1 being perfectly on the line, so unless your dealing with a best fit line i have no idea...
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some formulas that can help :\nhttp://www.eskridgeinc.com/support/formulas.html
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http://www.wolfram.com/webresources.html