Answer:
Probability[Number greater than 4] = 1/2
Step-by-step explanation:
Given:
Total side of die = 8
Find:
Probability[Number greater than 4]
Computation:
Number greater than 4;
[5,6,7,8]
Total number greater than 4 = 4
Probability[Number greater than 4] = Total number greater than 4 / Total side of die
Probability[Number greater than 4] = 4 / 8
Probability[Number greater than 4] = 1/2
Step-by-step explanation and answer:
For this you just need to plug in x for y = f(1/5x)
1/5(-4) = -4/5 or -0.8
1/5(-1) = -1/5 or -0.2
1/5(0) = 0
1/5(3) = 3/5 or 0.6
1/5(6) = 6/5 or 1.2 or 1 1/5
Simplified expression: 120x dollars
Step-by-step explanation:
Given,
Number of cartons in case of Scout cookies = 10 cartons
Number of boxes in carton of Scout cookies = 12 boxes
Amount earned on whole case = 10(12x) dollars
To simplify the expression, we will multiply 10 by 12x
Amount earned on whole case = 120x dollars
Simplified expression: 120x dollars
Keywords: multiplication, variable
Learn more about multiplication at:
#LearnwithBrainly
Answer:
V = 3591.4 ft³
Step-by-step explanation:
The formula for the volume of a sphere whose radius is given is
V = (4/3)πr³
Here we have V = (4/3)(3.14)(9.5 ft)³, or:
V = 3591.4 ft³
Answer:
D. No, because the sample size is large enough.
Step-by-step explanation:
The central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".
Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".
If the sample size is higher than 30, on this case the answer would be:
D. No, because the sample size is large enough.
And the reason is given by The Central Limit Theorem since states if the individual distribution is normal then the sampling distribution for the sample mean is also normal.
From the central limit theorem we know that the distribution for the sample mean
is given by:
If the sample size it's not large enough n<30, on that case the distribution would be not normal.