The answer is 16.25 adding all of those up
Answer:
PR5T represents <em>6754.</em>
P = 6
R = 7
T = 4
Y = 5
Step-by-step explanation:
Let us try to understand the subtraction starting from the unit's digit.
P R 5 T
- 4 7 Y 6
-----------------
1 9 9 8
To get 8, 6 should be subtracted from 14
14 - 6 = 8
So, T should be equal unit digit of 14 <em>i.e.</em> T = 4 and 1 should be carried over from the ten's digit of PR5T.
Now, let us move to ten's digit:
P R 5 4
- 4 7 Y 6
-----------------
1 9 9 8
To get 9, 6 should be subtracted from 15. But 1 is already carried to unit's place So, we have actually 14 here
14 - Y = 9
So, Y = 5
Moving to hundred's place:
R - carry - 7 = 9
Carry is 1, so R = unit's place of 9 + 8 i.e. R = 7
Moving to thousand's place:
P - carry - 4 = 1
Here carry is 1
So, P = 1 + 4 + 1 = 6
So, the number PR5T is <em>6754.</em>
Answer - A) -5
Step 1 - 3 Divided by 1/2 = 6
Step 2 - 6 + -5 = 1
Best Of Luck,
- Ari -
Answer:
1/8
Step-by-step explanation:
16 (cups in a gallon ) divided by 2=8=1/8
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.
