in simple language what is std deviation and regression?
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Standard deviation is a measure of the spread of a normally distributed variable. If the variable is not normally distributed, another measure should be used. \n\nRegression is a means of fitting an underlying function (usually a straight line) to a sample that shows a normal distribution around a central value at each point. Usually the independent variables to which the sample is being fitted are assumed not to have errors. For instance , you may fit say income (which varies) to age (which is known for each person in the sample).
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Standard deviation tells us how far from the mean the data points tend to be.\n\nFor example, if the mean is 20 and SD is 5. Then all the numbers used to calculate the average (20) can be 5 standard deviation away from it. So the numbers can be from anywhere of 15-25.
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Univariate problems depend on one variable only. multivariate pepend on several variables. A log regression curve when you have two varaibles y and x, and the relation between  x and y is of the form y = k log x
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Angle of deviation, d = i1 + i2 - A\nA= r1 + r2\nIn minimum deviation, µ = {sin(A+D)/2}/(sinA/2)
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well a bug is a virus, an error is..well its an error that has either occurred through the computer or the computer programmer(you). A defect is an internal problem and default usually means computers, or factories original settings. not quite sure about debug.
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z=(x-mu)/sigma\n\nWhere, x is the sample, mu is the average, and sigma is the standard deviation.\n\nfor the first repair, z=(75-90)/22 = -.681818\nfor the second repair, z=(100-90)/22 = 0.454545
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If you are using a sample set of data you can press stat/edit - then press enter/enter a list of numbers/ press stat again/move the arrow to calc/ select 1-Var stats/ press enter/press 2nd and then 1 (or L1)/ press enter.\n\nx bar = the sample mean\nSx = the sample standard deviation\nVariance = Sx squared
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its measure of defect present in any software component.it is broadly defined as:\n\nd.d.=defects/thousand lines of source.\n\nif we talk about any source code it will be:\n\ncode defect density=defects/thousand lines of code
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Statistics studies, among other things, deviations from normals and can help to explain errors (as well as help explain how errors are propagated and where their sources can be).\n\nFor example, if you have a numerical amount with an error and you multiply that value by something known and constant (like conversion from USD to Euros, for example), that error will be multiplied by that conversion rate.\n\nStatistics uses error calculation such as standard deviation (how much a value can vary from the average amount) and goes very far into detail about sums of errors etc. and how they affect averages, errors, and final values.\n\nAdditinoally, there are some formulae that are used all throughout the sciences that can be applied to accounting, and those sciences use statistics, again, to calculate errors.  For example, growth and decay of a radioactive substance has a certain formula that uses exponential (e to the power of something), but this is also used to calculate economic growth rates and can be applied to the stock market (it can also be used to explain traffic jams and other civil engineering problems).
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See about 'regression'.
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There are two that I've seen used:\n\n-margin for error\n-probability curves (discrete or continuous)
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I would say trial and error. Look at the r^2 of the regressions you get from using various internatl standards and report the one that yielded the best regression.
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Standard deviation tells you how spread out a set of numbers is.  A small standard deviation means that the numbers are close together.  A large one says that they're far apart.\n\nIt's often used as a measure of distance away from the mean.  In a normal distribution, about 68% of numbers are within one standard deviation from the mean.  For example, if you have a normal distribution with a mean of 50 and a standard dev of 10, then about 68% of the numbers are between 40 and 60.  And about 95% of the numbers are within two standard deviations, or between 30 and 70.
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Off the top of my head, don't hate me if there are errors...\n\nclass MyVar\n{\npublic:\n  int a;\n  MyVar ( MyVar& src )\n  { a = src.a; }\n}\n\n...\nMyVar p;\np.a = 5;\nMyVar q(p);\n// q.a==5 is true
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The two sigma error is the region around that mean that you would expect a randomly selected member of a set to fall into 95% of time.  If you had a normally distributed population with a mean of 5 and a standard deviation of 1, the two sigma region would be 3 to 7 or "2 sigma" in either direction.  Sigma is the Greek letter used for standard deviation.
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First load the add-in:\nTools > Add-Ins > Analysis Tool Pack\nthen find it:\nTools > Data Analysis & Choose Regression\nIf you need any more help mail me.
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==================\n#include <iostream.h>\n\nint main(void)\n{\n\n   int count; // holds the count\n   float sum, number, avg;\n\n   sum = 0.0;\n   count = 0;\n\n   cout << endl << endl   //  Make some space\n        << endl << "Enter a number.  Enter a zero (0) when you have"\n        << endl << "finished entering numbers and want to compute the"\n        << endl << "average of the inputted numbers." ;\n\n   do {\n         cout << endl << endl << "Data element #" << count + 1\n              << ": ";\n\n         cin >> number;\n\n         if (number != 0.)\n           sum = sum + number;\n           count = count + 1;\n\n      } while (number != 0.0);\n\n      averg = sum / (count-1);\n      cout << endl << "The average is: " << avg\n           << endl << endl ;\n      return 0;\n}\n\n\n==================\n\nThis program computes the average of a set of data values. Commented a bit for you to understand\n\nEnjoy