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:
(1 , 4)
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Answer:
3. Tony's deposit earned $40.80 in simple interest over 3 years in an account with an interest rate of 1.7%. How much did Tony deposit?
Answer: k ≤ -4 or k ≥ 
<u>Explanation:</u>
| 3k + 4 | ≥ 8
3k + 4 ≥ 8 or 3k + 4 ≤ -8
<u> -4</u> <u> -4 </u> <u> -4</u> <u>-4 </u>
3k ≥ 4 or 3k ≤ -12
<u>÷3 </u> <u>÷3 </u> <u> ÷3 </u> <u>÷3 </u>
k ≥ or k ≤ -4
