Answer:
201
Step-by-step explanation:
3.14*8*8=201
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:
and 
the intersection points.
Step-by-step explanation:
Intersection point of two functions is a common point which satisfies both the functions.
Given functions are,
For a common point of these functions,
For 
,
For 
,
Therefore, and the intersection points.
Because x is negative the domain of the function is all negative real numbers.
Set the radical greater than or equal to 0:
-x >= 0
Turn x positive by multiplying both sides by -1. When you multiply an inequality by -1 you need to reverse the inequality sign:
X <= 0
Domain is all real numbers below 0 so all negative real numbers.
Answer:
3.
Step-by-step explanation:
