Answer: P(22 ≤ x ≤ 29) = 0.703
Step-by-step explanation:
Since the machine's output is normally distributed, we would apply the formula for normal distribution which is expressed as
z = (x - µ)/σ
Where
x = output of the machine in ounces per cup.
µ = mean output
σ = standard deviation
From the information given,
µ = 27
σ = 3
The probability of filling a cup between 22 and 29 ounces is expressed as
P(22 ≤ x ≤ 29)
For x = 22,
z = (22 - 27)/3 = - 1.67
Looking at the normal distribution table, the probability corresponding to the z score is 0.047
For x = 29,
z = (29 - 27)/3 = 0.67
Looking at the normal distribution table, the probability corresponding to the z score is 0.75
Therefore,
P(22 ≤ x ≤ 29) = 0.75 - 0.047 = 0.703
Answer:
Factors of the constant term
Step-by-step explanation:
1. 140 - The reason it would be one 40 is honestly a really simple explanation. Is the ratio of boys to girls in 7-2 and the amount of girls is 40. That means that per 1 number, is 20 people since 40 divided by 2 is 20. So if I do 2 x 7 that would be 140.
2. I didnt really understand this one sorryyy.
3. 1200 (same rule applies) - Since The ratio is 1:11 and the 1 represents 100 beans. If I doo 100 x 11 that would be 1100, then I would add the 1100 to the 100 (from the 1) and that would be 1200.
4. 6:7 - If I do 650 - 350 that would be 300. Which means the ratio would be 300:350. But once simplified the answer would be 6:7.
5. 1:2 - 720 - 240 is 480. Therefore the ratio would be 240:480. Once simplified, the ratio is 1:2.
Hope this helped. Good luck!!! xo
Answer:
0.25 or 25%
Step-by-step explanation:
There are 4 possible outcomes for each die, which gives us 16 possible combinations (4 x 4). In order for the sum to exceed 5, the possible outcomes are:
Red = 3, and Green = 3
Red = 3, and Green = 4
Red = 4, and Green = 3
Red = 4, and Green = 4
Therefore, the probability of winning on a single play is:
The probability is 0.25 or 25%.
The null hypothesis is often used as a statement <span>about the population the researcher suspects is true and is trying to find evidence for in formulating a statistical test of significance. In addition to that, it is commonly used when stating that there is no significant difference between subjects.</span>
