How does multiple regression analysis differ from "trial and error"?
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Multiple regression is often used to test a model with a few free parameters. There exist several methods to find the optimal values. \n\nTrial and error *is* one of these methods but not a very efficient one. It is often used as starting method to get an idea of *where* in the parameter space the optimal solutions are located.
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Type 1 error:\n\nRejection of a true hypothesis\n\nType 2 error:\n\nFailure to reject a false hypothesis
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Multivariate analysis is a statistical technique to examine the effect of many variables on a single outcome.  For example, if you were studying what factors go into the price of a house, you might collect data about location, age of the house, and size of the house.  You could then perform a multivariate analysis to determine the relative contribution of each of those factors to the price of the home.
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In statistics, the residual sum of squares (RSS) is the sum of squares of residuals.\n\nIn a standard regression model ,y=a+bx+ε where a and b are coefficients, y and x are the regressand and the regressor, respectively, and ε is the error term. The sum of squares of residuals is the sum of squares of estimates of εi.\n\nIn general: total sum of squares = explained sum of squares + residual sum of squares.
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Here is a link to a document that clearly explains the case:\nhttp://www.uvm.edu/~jleonard/AGRI85/spring2004/Standard_Error_Bars_in_Excel.html
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On the off chance you want to know the steps, rather than a specific regression computer application, here's what you do:\n\nStep 1: collect data.  A minimum of two sets of data (like house prices and interest rates).  So, in this case, a single data "point" (p) would have p(price,rate) as the two sets of data making up each point.\n\nStep 2: after collecting a sufficient number of data points (more than 30 is good for better results), designate one set of data the "dependent" variable (e.g., y) and the other sets the "independent" variables (e.g., x1, x2, etc.)\n\nStep 3: set up a general linear equation (for linear regression) in the form of y = mx + b or, more generally, y = m1*x1 + m2*x2 + ... + mN xN + b, where each x term represents a set of independent data, y is the dependent data set, and b is a constant called the intercept.\n\nStep 4: enter the dependent and independent data sets into your regression tool (I use Excel's LINEST function for example).  Make sure you enter your dependent data into the y part of your regression tool and the x's, your independent data, into the independent data slots of the tool.  Otherwise, you'll get meaningless output from the tool\n\nStep 5: run the regression tool with your data entered. \n\nStep 6: note the output, which typically includes the mN coefficients for each x data set, the b intercept, R^2 the coefficient of determination, and measures of spread or uncertainty for each of the coeffcients and the intercept.
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<>Sources of error refers to potential points or places in the research/study that could give erroneous results.
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Well, the y-intercept would be acceleration when the force on the object is zero.  You'd better hope it's zero!  If it's not, you might want to fix the point in your analysis (this can be done in Excel) or redo the experiment.\n\nEdit: If you're plotting acceleration vs. force, the intercept really should be zero.  Those values appear small enough to be within the error of the experiment.  They could be indicative of the effect of a systematic error, but are more likely the result of random noise.  Try fixing the intercepts at zero in Exel (right-click on the regression line and open up the options for it; there should be a checkbox for "fix intercept").  An intercept value that is not equal to zero indicates that there was acceleration when there was no force!
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Don't understand your question very clearly. \n\nIf looking at regressing testing, you can go to open source projects like gcc, mysql, etc. There are regression testing code in their source release.
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Accuracy of data.  Crap in, crap out.  Your analysis is only as reliable as the data which it was built upon.  Do the work necessary up front to ensure that you will receive, retain and transpose data properly.  If you're measuring effectiveness of a program your survey methods have to be developed with an idea in mind.  What are you trying to measure, what are you hoping to learn.  Now, does your survey capture the info that will give you those answers?  \n\nThis is increasingly easier with the automation of our programs, but user error is never to be taken lightly.  \n\nGood luck!
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Well, your difference in results could be a number of a things. As far as Excel is concerned, pay close attention to how many decimal places you allow in each cell. If your cell is too small, the number will be rounded up and it's possible it could be radically different from the number SPSS & MINITAB give you.\n\nI like SPSS because the syntax editing is pretty simple. At work, I use SPSS, Excel and STATA, and honestly, I prefer STATA. It's very flexible, even if it is trickier to use.\n\nAnyway, It's hard to answer your questio without more details. Your values can also vary if you haven't done the right command. And finally, as one person mentioned above, each program uses a different algorithm to compute the values your asking for, so your answers will probably vary slightly from time to time. However, if there are big differences, then you should check your method or your data, because something in the command you executed or missing data could be causing you problems.
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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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The value of textual statistics is now widely acknowledged in many branches of the social sciences, reflecting the fact that there are few fields of research in which the text is not a major element for analysis.\n\nThe statistical processing may be of responses to open questions, interviews, texts and even individual itineraries. The procedures are numerous and can be adapted to different kinds of material. The aim in the present article is to give a broad overview of the various applications of these methods, ranging from the most traditional to the most recent, and focusing on both the methodology used and the results obtained.
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The bias of large outliers.
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This depends on the focus of your study.  What or who are you trying to assess the risk of?  Biostatistics has several methods for assessing risk when it comes to diseases.  Quality Assurance methods assess the risk of malfunction and ways of reducing the likelihood of it happening.  I'm sure there are many other forms of assessing risk as well, so I'm afraid your question needs to be a little more specific.
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It is not exactly clear what you need, but maybe you can use the Kolmogorov-Smirnov test (KS-test) for your problem. The KS-test tries to determine if two datasets differ significantly. The KS-test has the advantage of making no assumption about the distribution of data.  Note however, that this generality comes at some cost: other tests (for example Student's t-test) may be more sensitive if the data meet the requirements of the test.
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There are two types of errors. One human error another instrumental error. \nWe know the accuracy of measurement of instruments. \n\nAny error in the result must be within this instrumetal error.\n\nHuman error must be eliminated in total. \n\nThis can be done only by repating the experiment.\n\nAny result which is not within the instrumetal error must be totaly rejected and we find the mean value of the results which are within the instrumental errors