3 inches in 1 foot is: 1/4
3 inches in 1 yard is: 41667/500000
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:
A=ε*l*c
A= 2- log₁₀ % T
Step-by-step explanation:
There is a linear relationship between the concentration of a sample and absorbance according to Beer-Lambert Law.
A=ε*l*c
where;
A=absorbance
ε=absorption coefficient
l=path length
c=concentration
Because % transmittance is transmittance value multiplied by 100 then, the equation that will allow us calculate absorbance from % transmittance value will be;
A= 2- log₁₀ % T where T is transmittance.
Sorry, a bit late but i took it and it was A.
