I can’t answer anything from my side so I will guide you the best I can! Measure the highlighted lines on the image. Then record the numbers into the correct areas(length,width,height). Then multiply all of the numbers above. That’s how you answer question 3. I’m sorry that I can’t do much more on my side.
The Answer to <span>8x-6>12+2x = x>3</span>
Option a is correct. The calculated answer is 0.150
<h3>How to get the value using the cdf</h3>
In order to get P(0.5 ≤ X ≤ 1.5).
This can be rewritten as
p = 0.5
and P = 1.5
We have the equation as

This would be written as
1.5²/16 - 0.5²/16
= 0.1406 - 0.015625
= 0.124975
This is approximately 0.1250
Read more on cdf here:
brainly.com/question/19884447
#SPJ1
<h3>complete question</h3>
Use the cdf to determine P(0.5 ≤ X ≤ 1.5).
a) 0.1250
b) 0.0339
c) 0.1406
d) 0.0677
e) 0.8750
f) None of the above
Answer:
Subscribe to my animations Y0UTUBE channel! Channel name: Let Me Explain Studios. Have a nice day!
Step-by-step explanation:
Answer: The description are as follows:
Step-by-step explanation:
Correlation coefficients is a statistical measure that measures the relationship between the two variables.
(a) r = 1, it means that there is a Perfect positive relationship between the two variables. If there is positive increase in one variable then other variable also increases with a fixed proportion.
(b) r = -1, it means that there is a perfect negative relationship between the two variables. If there is positive increase in one variable then other variable decreases with a fixed proportion.
(c) r = 0, this is a situation which shows that there is no relationship between the two variables.
(d) r = 0.86, this is a situation which shows that there is a fairly strong positive relationship between the two variables.
(e) r = 0.06, it is nearly zero which represents that either there is a very minor positive relationship between the two variables or there is no relationship between them.
(f) r = -0.89, this is a situation which shows that there is a fairly strong negative relationship between the two variables.