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:
don't spend our time here
this is a spamm community
(10+2x)(8+2x) = 120
4x^2 + 36x + 80 = 120
x^2 + 9x + 20 = 30
x^2 + 9x - 10 = 0
(x+10)(x-1) = 0
x = 1 inch dimension
Answer:
3/6
Step-by-step explanation:
no idea if its right
