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1. #HOW TO CALCULATE STANDARD ERROR OF THE REGRESSION HOW TO#
2. #HOW TO CALCULATE STANDARD ERROR OF THE REGRESSION CODE#

#HOW TO CALCULATE STANDARD ERROR OF THE REGRESSION HOW TO#

In summary: At this point you should know how to return linear regression stats such as standard errors or p-values in R programming. Extract Regression Coefficients of Linear Model in R.

#HOW TO CALCULATE STANDARD ERROR OF THE REGRESSION CODE#

In the video, I explain the R code of this tutorial in a live session.īesides the video, you may have a look at the other tutorials of this homepage:

Note that this p-value is basically zero in this example.ĭo you want to learn more about linear regression analysis? Then you may have a look at the following video of my YouTube channel.

Pf(mod_summary$fstatistic, # Applying pf() function Pf (mod_summary$fstatistic, # Applying pf() function Let’s fit a linear regression model based on these data in R: The variable y is our target variable and the variables x1-圆 are the predictors. # 6 1.74 1.68 1.61 -0.63 -3.16 -0.21 0.31Īs you can see based on the previous RStudio console output, our example data is a data frame containing seven columns. Head(data) # Showing head of example data Set.seed(1234421234) # Drawing randomly distributed data seed ( 1234421234 ) # Drawing randomly distributed data