The median of the data is 60. That means the two middle numbers divided by 2 = 60.
(x + x + 2) / 2 = 60...multiply by 2
2x + 2 = 120
2x = 120 - 2
2x = 118
x = 118/2
x = 59
x + 2 = 59 + 2 = 61
so ur numbers go : 26,29,42,53,59,61. 70, 75, 82, 93
Answer:
x = 30/48 or x = 0.625
Step-by-step explanation:
3/2 = 4/3x + 2/3
Convert to common denominator
9/6 = 8/6x + 4/6
subtract 4/6 from both sides
9/6 - 4/6 = 8/6x + 4/6 - 4/6
5/6 = 8/6x
Divide by 8/6
5/6 x 6/8 = x
30/48 = x
x = 30/48
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Diameter because it’s the diameter
Answer:
A. 12.68 - 14.72 hours
B. Normal distribution.
Step-by-step explanation:
Part A
This question is using quantitative data. A 99% confidence interval means that you want to know the range where 99% of the population will be. To find this you have to convert the 99% CI into the z-score which is -2.58SD to + 2.58SD.
Note that the standard deviation(SD) is from the sample, not the population. We still need to find the standard deviation of the population. The formula is:
population SD = ![\frac{o}{\sqrt[]{n} }](https://tex.z-dn.net/?f=%5Cfrac%7Bo%7D%7B%5Csqrt%5B%5D%7Bn%7D%20%7D)
Where the o= sample SD = 7.4
n= number of sample = 463
The calculation will be:
population SD = ![\frac{o}{\sqrt[]{n} }](https://tex.z-dn.net/?f=%5Cfrac%7Bo%7D%7B%5Csqrt%5B%5D%7Bn%7D%20%7D)
population SD =
= 0.3951
The bottom limit will be:
Mean - SD * z-score= 13.7 - 0.3951*2.58 = 12.68 hours
The upper limit will be:
Mean + SD * z-score= 13.7 + 0.3951*2.58 =14.72 hours
The 99% CI range will be 12.68 - 14.72 hours
Part B
The table used to convert confidence interval into z-score depends on the distribution type of the data. Most data is classified as normal distributed, a data type that will concentrated at mean and spread equally from the mean. Normal distribution data will look like a bell which make it also called bell curve.
The question tells you that the data is normal distribution, but that doesn't mean every data is normally distributed. There are a lot of other data distribution type so we have to do some tests to know the normality of the data in real-life data.