Backward: The procedure starts by simultaneously adding all variables.Forward: The procedure is the same as for stepwise selection, except that variables are only added and never removed.The procedure continues until no more variables can be added or removed. If the probability is greater than the "Probability of removal", the variable is removed. After the third variable is added, the impact of removing each variable that is present in the model after it has been added is evaluated (still using the t statistic). If a second variable is such that the probability associated with its t is less than the "Probability for entry", it is added to the model. Stepwise: The selection process starts by adding the variable with the largest contribution to the model (the criterion used is Student's t statistic).Furthermore, the user can choose several "criteria" to determine the best model: Adjusted R², Mean Square of Errors (MSE), Mallows Cp, Akaike's AIC, Schwarz's SBC, Amemiya's PC. Best model: This method allows you to select the best mode among all the models that can handle a number of variables varying from "Min variables" to "Max Variables".It is possible to select only the most important ones using one of the four methods available in XLSTAT: Not all variables are important or significant in the linear regression model. Going further: variable selection in linear regression The linear regression hypotheses are that the errors e i follow the same normal distribution N(0,s) and are independent. Since the model is found by using the ordinary least squares (OLS) method (the sum of squared errors e i² is minimized), many wonder: is OLS the same as linear regression? Not really, OLS is simply the name of the method that enables us to find the regression line equation. Where y i is the value observed for the dependent variable for observation i, x ki is the value taken by variable k for observation i, and e i is the error of the model. The linear regression equation is written for observation i as follows: The principle of linear regression is to model a quantitative dependent variable Y through a linear combination of p quantitative explanatory variables, X 1, X 2, …, X p. A distinction is usually made between simple regression (with only one explanatory variable) and multiple regression (several explanatory variables) although the overall concept and calculation methods are identical. Hope this helps someone.Linear regression is undoubtedly one of the most frequently used statistical modeling methods. (I had ealier checked my entire directory for XLSTAT.XLA)Īnyway, there you have it. I was initially confused by the XLSTART.XLA file rather than XLSTAT.XLA add-in I was looking for, but holding the arrow over that file showed it belonged to XLSTAT. in the above paths)Ĭ:\Documents and Settings\YourUserName\Application Data\Microsoft\Excel\XLStart\XLSTART.XLA I even searched for and deleted all the registry keys referring to XLSTAT. Mine was empty.Ĭ:\Program Files\Microsoft Office\Office10\Addins and remove the offending file. None of these worked, but many said look inĬ:\Program Files\Microsoft Office\Office10\XLStart and remove all the files. XLA files from a specific add-in directory. These included unchecking the Add-ins one by one and removing. "Compile error in hidden module: Procedures"Ī Google search notes it is in an XLA error in a Statistics program called XLSTAT, which I had on my machine for Trial, but uninstalled, or so I thought.Ī forum search on this forum "compile error in hidden module" threw up 3 pages of similar problems with solutions varying from those proffered by Microsoft to those proffered by other add-in vendors. My error message on Excel open and close, similar to all the others, but there was no MS fix as for the others. so this FIX is for anyone else going crazy.
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