The completely factored form of p^4-16 is:

Step-by-step explanation:
We have to factorize the given expression to get the final result
Given

p^2-4 can further be factored again
![=(p^2+4)[(p)^2-(2)^2]\\=(p^2+4)(p+2)(p-2)](https://tex.z-dn.net/?f=%3D%28p%5E2%2B4%29%5B%28p%29%5E2-%282%29%5E2%5D%5C%5C%3D%28p%5E2%2B4%29%28p%2B2%29%28p-2%29)
The completely factored form of p^4-16 is:

Keywords: Factorization
Learn more about factorization at:
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Answer: 0.1289
Step-by-step explanation:
Given : The proportion of all students at a large university are absent on Mondays. : 
Sample size : 
Mean : 
Standard deviation = 

Let x be a binomial variable.
Using the standard normal distribution table ,
(1)
Z score fro normal distribution:-

For x=4

For x=3

Then , from (1)
Hence, the probability that four students are absent = 
Step-by-step explanation:
We have given that,
The inner radius of a bicycle wheel is 12 inches.
It is required to find the expression to find the inside circumference of this wheel.
The outer surface of the wheel is equal to its circumference. It is given by :

r is radius of wheel

This is the required explanation.
Answer:
Step-by-step explanation:
Hello!
Given the linear regression of Y: "Annual salary" as a function of X: "Mean score on teaching evaluation" of a population of university professors. It is desired to study whether student evaluations are related to salaries.
The population equation line is
E(Y)= β₀ + β₁X
Using the information of a n= 100 sample, the following data was calculated:
R²= 0.23
Coefficient Standard Error
Intercept 25675.5 11393
x 5321 2119
The estimated equation is
^Y= 25675.5 + 5321X
Now if the interest is to test if the teaching evaluation affects the proffesor's annual salary, the hypotheses are:
H₀: β = 0
H₁: β ≠ 0
There are two statistic you can use to make this test, a Student's t or an ANOVA F.
Since you have information about the estimation of β you can calculate the two tailed t test using the formula:
~
= 25.1109
The p-value is two-tailed, and is the probability of getting a value as extreme as the calculated
under the distribution 
p-value < 0.00001
I hope it helps!