Answer:B
Step-by-step explanation:
Answer:
506 $
Step-by-step explanation:
Step-by-step explanation:
First, note that a flexible statistical learning method refers to using models that take into account agree difference in the observed data set, and are thus adjustable. While the inflexible method usually involves a model that has no regard to the kind of data set.
a) The sample size n is extremely large, and the number of predictors p is small. (BETTER)
In this case since the sample size is extremely large a flexible model is a best fit.
b) The number of predictors p is extremely large, and the number of observations n is small. (WORSE)
In such case overfiting the data is more likely because of of the small observations.
c) The relationship between the predictors and response is highly non-linear. (BETTER)
The flexible method would be a better fit.
d) The variance of the error terms, i.e. σ2=Var(ϵ), is extremely high. (WORSE)
In such case, using a flexible model is a best fit for the error terms because it can be adjusted.
Answer:
k=9
Step-by-step explanation:
First find r=22/20=1.1
The sum of the first k terms formula is a1•(1-r^k)/(1-r)
a1=20 and the sum is 271.59
Now plug in these values in the formula and find k.
271.59=20(1-1.1^k)/(1-1.1)
When you simplify this equation, you will get k=ln(2.358)/ln(1.1)
k=9
4 also 2 :))))))))))))))))
