Answer:
ooohh yeah i kinda suck at geometry lol nearly failed sophomore year last year haha
Step-by-step explanation:
A . the 13th spot will have a star
Answer:
Amplitude: 3
Period: 2π
Step-by-step explanation:
The data is not linear. From (3, 1) to (7, 2), we add 4 to the x-value and add 1 to the y-value. This pattern continues for a little while, however, from (11, 3) to (18, 5), we add only 7 to the x-value even though we added 2 to the y-value. Given this information, we can determine that this is not linear data.
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.
