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Dear Frauke, good afternoon, Could you tell me which excel function didnt work for regression analysis and what excel version where you using? Best regards, Paul El 07/11/2012 11:55, "frauke" <fhoss@andrew.cmu.edu> escribió: > Hi David, hi Rui, > > thanks for your quick replies. I have replicated David's R results and > confirmed them with Minitab. Though

Dear R users, my problem, at the moment, is the following: I need to apply a script to a very big dataset, so I need first to subdivide the dataset into samples and then apply the script to every single sample. Now I tried to solve the problem with these linest: S00001<-data[data$sub==1,1:3] #which divide the dataset in samples based on the number on the column "sub" data<-S00001 #because in the script the dataset is always called "data" .Script("R",script.R,args=data,wri...

Hallo, I am totally confused why Excel and R give me totally different regression results for the data below. If you know the solution, please enlighten me. In Excel I used LINEST() and Data>Data Analysis>Regression and both (fortunately) gave the same result. The coefficients were: /b0=1.16, b1=0.957, b2=0.024, R2=0.0027, adjusted R2=-0.017/ In R I used model<-lm(as.numeric(test[,1]) /(wave sign here)/ as.numeric(test[,2]) + as.numeric(test[,3])) The result is: /...

Greetings, I have several sets of oscillation data and would like to estimate the parameters of a sine function to each set (and hopefully automate this). A colleague provided an excel sheet that uses solver to minimize the RSS after fitting the sine function to each data set, but this cumbersome and difficult to automate. Is there a method in R for fitting a given sine function to a

From: Anastasiia Lukianenko <anastasiia_lukianenko at epam.com> We are trying to use the clang-format approach as a base for Xen [1] style formatting. During the state of testing clang-format with different configurations, we found that some points regarding the Xen coding style are not configurable. Therefore, we decided to add them to be able to make a choice in different cases. Regards,

Dear R helpers     Suppose I have a following data   y  <- c(9.21, 9.51, 9.73, 9.88, 10.12. 10.21)   t  <- c(0, 0.25, 1, 3, 6, 12)   I want to find out the polynomial which fits y in terms of t i.e. y = f(t) some function of t.   e.g.   y = bo + b1*t + (b2 * t^2) + (b3 * t^3) + ...... and so on.   In Excel I have defined y as independent variable, then defined t, t^2 and t^3 and using

I am a beginner in using R and I need help in the interpretation of AR result by R. I used 12 observations for my AR(2) model and it turned out the intercept showed 5.23 while first and second AR coefficients showed 0.40 and 0.46. It is because my raw data are in million so it seems the intercept is too small and it doesn't make sense. Did i make any mistake in my code? My code is as follows: