
ex = 18 e x = 18 Take the natural logarithm of both sides of the equation to remove the variable from the exponent. ln(ex) = ln(18) ln (e x) = ln (18)
Answer:
X = 1/2
Step-by-step explanation:
The question seems like (6*4x² = 3*4x)
This is a simple algebraic equation
First of all, multiply the coefficient of x by the number on each side.
6*4x² = 3*4x
24x² = 12x
Divide both sides by 12
2x² = x
Divide both sides by x
2x = 1
Solve for x
x = 1/2
Answer:
Situation 2 (B)
Step-by-step explanation:
I think it would be B since the power of the gear increases proportionally with the radius.
Answer:
= 15
Step-by-step explanation:
21 + 15 = 36
36/2 = 18
Therefore, 15 is the missing number :33
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.