it's ok, honestly, i wanted to write a mlr for 4featurtes, which contain 288 rows and 450columns, I wanted to see that whether I can use this formula: lm(y~x1+x2+x3+x4, data=df) or not?

Assuming that `df`

has columns named y, x1, x2, x3, and x4 that should be fine.

thank you ever so much

is there any pamphlet here that I can take some info about linear regressions?

There are probably an infinite number of resources to learn about multiple regressions. Here's one (of many) that is specific to R: Multiple Linear Regression in R - Articles - STHDA

I would be more comfortable if there was consistency in terminology. "Features" is a machine learning term while dependent and independent variable are terms more common in statistics. In a machine learning approach, one often breaks the data into training, testing, and validation subsets. In classical statistics I need to assess if the model conforms to some basic assumptions. Asking questions about multiple regression and providing data on two variables is also concerning. Finally having a multiple regression of 4 independent variables in a dataset with 450 columns feels like there are other questions that should be asked/answered first.

yes, exactly but as the data sets are not covered here, it is quite bit hard for me to tell my question succinctly. by the way, thank you ever so much.

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