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.
The answer to this problem is (x,y) = (-4,2)
The slope intercept form looks like y=mx+b (m-slope, b is the y intercept)
y+3=1/2(x-2)
y=(1/2)x-(2/2)-3
y=(1/2)x-4
Answer:
Any radical in the form can be written using a fractional exponent in the form . The relationship between and works for rational exponents that have a numerator of 1 as well. For example, the radical can also be written as , since any number remains the same value if it is raised to the first power.
Step-by-step explanation:
