For the answer to the question above asking w<span>hy do we need to integrate probabilities in statistics?
Well, i</span>ntegration is used very very often in theoretical statistics. Transformation relates to the result that data values that are modelled as being random variables from any given continuous distribution can be converted to random variables having a standard uniform distribution. So, we need to see other possibilities by combining<span> (one thing) with another.</span>
Answer:
In terms of negative correlation, I would say it's the longer you exercise, the more you sweat.
I’m pretty sure it’s 1. 8:1
Answer:
a. the less variability it has
Step-by-step explanation:
The standard deviation is a measure of the amount of variation or dispersion of a set of values.
When your standard deviation is big your data is more dispersed.
When your standar deviation is small your mean is a representative index of your data, and there is less variability.
If there was no dispersion of the data (if all your data be the same) then the standard deviation will be 0.
Answer:
If f(x) = x2 – 5 for all values x and f(a) = 4, what is one possible value of a? Possible Answers: ... If f(x) = x2 + 5x and g(x) = 2, what is f(g(4))?. Possible ... (1/3) * (27 + 15 – 15). (1/3) * (27)
