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: See explanation
Step-by-step explanation:
John said he can find 38×10 by adding a 0 to the end of 38 to get 380. John finds 46.52×10 using the same method and gets 46.520.
For the first one that is, 38 × 10 = 380, John is correct. For the second one, that is, 46.52 × 10 = 46.520. John is incorrect. In this case, the first one is correct because John was multiplying by a whole number.
For the second one involving a decimal, John could have shifted the decimal point one place to the right and not add 0 to the end. Then he will get:
46.52 × 10 = 465.2
Answer:
Final result is 3.
Step-by-step explanation:
Quotient = (60 + 30) / 10
= 90/10
= 9.
Difference of quotient and 3
= 9 - 3
= 6
6 / 2 = 3.
45/15= 3
So 1 minute=3 cents
75*3=225=2.25 converted
75 minutes=$2.25
If the two angles are supplementary, they must equal 180 degrees
