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.
Answer:
its27.54 8.1×3.4 =27.54 base×height
f(x)= -2 (x - 2) ( x - 4)
if x=2 ⇒ f(x)=y=0 ⇒ graph of f(x) intercept x axis in (2,0)
if x=4 ⇒ f(x)=y=0 ⇒ graph of f(x) intercept x axis in (4,0)
if x=0 ⇒f(x)= -2 *(-2)*(-4)= - 16 ⇒ graph of f(x) intercept y axis in (0,- 16)
⇒ f(x)= -2 (x - 2) ( x - 4) is the answer
Answer:
(0, -1)
Step-by-step explanation:
that is where the line intercepts on the vertical axis
Answer
Step-by-step explanation:
