Answer:
Lol don’t know
Step-by-step explanation:
Have a nice day uvu
Answer:
Option B - False
Step-by-step explanation:
Critical value is a point beyond which we normally reject the null hypothesis. Whereas, P-value is defined as the probability to the right of respective statistic which could either be Z, T or chi. Now, the benefit of using p-value is that it calculates a probability estimate which we will be able to test at any level of significance by comparing the probability directly with the significance level.
For example, let's assume that the Z-value for a particular experiment is 1.67, which will be greater than the critical value at 5% which will be 1.64. Thus, if we want to check for a different significance level of 1%, we will need to calculate a new critical value.
Whereas, if we calculate the p-value for say 1.67, it will give a value of about 0.047. This p-value can be used to reject the hypothesis at 5% significance level since 0.047 < 0.05. But with a significance level of 1%, the hypothesis can be accepted since 0.047 > 0.01.
Thus, it's clear critical values are different from P-values and they can't be used interchangeably.
4(0.5n-3)=n-0.25(12-8n)
2n-12=n-3+2n
2n-12=3n-3
2n-3n=-3+12
-n=9
n = -9
100% of Kerion's paper squares are:
(100% / 100%) = 1
Half of the squares of your paper are colored blue:
(1) / (2) = 1/2
Of the blue squares, 1/3 of them will also have stripes:
(1/2) * (1/3) = 1/6
answer
A fraction of (1/6) squares will be blue with strips
