Please include the R code thank you

We have seen that adding useless predictors to a regression model will increase R2. Here, let's examine what our inference methods say if the predictors are intact useless. Suppose the true/pop fit is y = Mie., no x at all), and so a possible sample from the population could be the following: set.seed(123) # Use this line to make sure we all get the same answes. n 20 y=1+ rnorm(n,0,1) a) Write code to make data on 10 useless predictors(and no useful predictors), fit the model y= alpha + beta 1 x1+ + beta10 x10, perform the test of model utility, and perform t-tests on each of the 10 coefficients to see if they are zero. Show/turn-in your R code b) According to the F-test of model utility, are any of the predictors useful at alpha-0.1? c) According to the t-tests, are any of the predictors useful at alpha = 0.1 ? See the solns to make sure you understand the moral of this exercise. We have seen that adding useless predictors to a regression model will increase R2. Here, let's examine what our inference methods say if the predictors are intact useless. Suppose the true/pop fit is y = Mie., no x at all), and so a possible sample from the population could be the following: set.seed(123) # Use this line to make sure we all get the same answes. n 20 y=1+ rnorm(n,0,1) a) Write code to make data on 10 useless predictors(and no useful predictors), fit the model y= alpha + beta 1 x1+ + beta10 x10, perform the test of model utility, and perform t-tests on each of the 10 coefficients to see if they are zero. Show/turn-in your R code b) According to the F-test of model utility, are any of the predictors useful at alpha-0.1? c) According to the t-tests, are any of the predictors useful at alpha = 0.1 ? See the solns to make sure you understand the moral of this exercise