The sampling distribbbution of the mean is defined as the

a. mean of sample data that follows a normal distribution distribution of the sample means of all possible samples of a certain size

b. mean of sample data that has been collected from a simple random sample distribution of data that has been collected from a simple random sample

Check Explanation

Step-by-step explanation:

The sample distribution of the mean is basically a collection of the means of all sample data that have been randomly obtained full data/population.

Imagine a series of different sets of samples are randomly extracted from the full population. We then have a number of different sets of samples. If the mean of each sample set is then obtained, then the set of these means is the sampling distribution of the mean. So, if the full data is a normal distribution, then the sampling distribution of the mean too will be a normal distribution.

And the mean of this sampling distribution of the mean is simply called the mean of the sampling distributions of the mean.

So, in a sense, both statements provided above are both correct, but the option B is a more general definition.

1 number

if we use a graphing calculator, there is only one such number that satisfies the conclusion of the mean value theorem, that is, a number such that

where the right-hand side is the average rate of change of f on the interval [0.5,0.75], which is the slope of the secant line.

in the attached graph, the black line is the secant line which has its slope as the average rate of change of f on [0.5,0.75], while the orange line is the only tangent line to the graph of f in the interval that satisfies the mvt conclusion.

answer: -10

step-by-step explanation:

s(t) = 1 - 10t

s(x) = 1 - 10x

s(x + h) = 1 - 10(x + h)

            = 1 - 10x - 10h

s(x + h) - s(x) = 1 - 10x - 10h - (1 - 10x)

                      = 1 - 10x - 10h - 1 + 10x

                      = -10h

= -10