A is the probability of obtaining a sample statistic that is at least as extreme as the original sample statistic, under the assumption that the null hypothesis is true.

p-value

Step-by-step explanation:

When performing any hypothesis test the first step is to formulate Null Hypothesis and Alternate Hypothesis. Next we calculate the test statistic and based on the test statistic we calculate a p-value.

This p-value gives the probability of obtaining the test result which is as atleast as extreme as our original test result, if the Null hypothesis is assumed to be true.

So, this means, the null hypothesis is assumed to be true and the statistical test is performed which gives a p-value. p-value gives a measure in terms of probability that how true is the result.

A higher p-value means probability occurrence of hypothesized value of Null Hypothesis is larger, so we accept the Null Hypothesis. A small p-value indicates the probability of occurrence of hypothesized value is  very small, and, therefore, the null hypothesis must be rejected.

  b.   the average rate of change of f(x) is less than the average rate of change of g(x).

step-by-step explanation:

since the length of the interval is the same in each case, we only need to compare the change in y-value to see which is greater.

so the change in y-value for function f is (6 )) = 9.

the table tells us

so the change in y-value for function g is (31 -1) = 30.

the change in y-value of f(x) on the interval is less than the change in y-value of g(x) on the same interval, so the average rate of change of f(x) is less than the average rate of change of g(x).

step-by-step explanation:

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