A common misconception in statistics is confusing correlation with causation. If two events are correlated, it merely means that they share the same behaviour over time, but it doesn't imply in any way that those event are related by a common cause, or even worse, that one implies the other.
You can find several (even humorous) counter examples online. For example, if you plot the number of reported pirates assault against the global temperature in the last years, you'll se that temperature is rising (unfortunately...) while pirates are almost disappearing.
One could observe this strong negative correlation and claim that hotter climate has solved the pirate issue. Of course this is a joke, but it explains why you shouldn't confuse correlation with causation.
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:
7:25 P.M.
Step-by-step explanation:
Add 2:25, 1:25, and 2:40 = 6:30 and add that to 12:55= 19:25 which is also equal to 7:25 P.M.
Answer:
6 + 0 = 0 + 6 = 6
12 X (3 X 9)
2 + 6x
3 + 7 + 8 = 7 + __ + 3
27 - 108
-15 - 5m
(6 + 5) + 1
8x + 28
26 X 1 = 1 X 26 = 26
24 + 4s
Step-by-step explanation:
i hope this helps :))
