We have seen hat adding useless predictors to a regression model will increase R2. Here, let's examine what our inference methods say if the predictors are infact useless. Suppose the true/pop fit is y 1(i. e, 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! 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 - 0.17
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

so far i think you have the right thing going for you just remember soh cah toh

step-by-step explanation:

sine over hypotenuse

cosine   equals adjacent

tangent over hypotenuse

eve and marks ratios are equivalent

step-by-step explanation:

in english this is

ex.15 pag.85 create. write in ten lines the continuation of the ballad of the five motanasi, using verbs to the imperfect and to the perfect compound. five stars

hope i with whatever it is that you need! : )

yes, gavin is right.

step-by-step explanation:

gavin is correct. 

this problem is about converting 9% per year to how many percent per quarter. the rate is still the same. it only differed in the time period. so, through dimensional analysis,

9% per year  × (1 year / 4 quarters) = 2.25% per quarter

if he was asked to convert 9% per year to how many percent per quarter  compounded quarterly, then you will not simply divide by four. this is because compounding is not linear.

hope this , have a good day. c;