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.
Equalities will have the words "is" or "equals" or "the same as" in the wording. These produce a unique solution.
Inequalities have phrases like "at most" or "at least" or "no more than" in them. Solutions are a range of values, ranging between 2 values, or from a particular value to positive or negative infinity.
Answer:
5.5hr
Step-by-step explanation:
Distance=550Km
Speed=100Km/hr
Speed=(Distance/Time)
Time=(Distance/Speed)
Time=550/100=5.5hr
Answer:
the first one and the fith one
Step-by-step explanation:
Answer:
0.253
Step-by-step explanation:
since it is greater than 0.243
