Answer:
C - 3 (negative 64 divided by 8) + 25 =1
Step-by-step explanation:
Answer: When you divide by 100 you are essentially moving the decimal of the number two places to the left. In undoing this you would have to move the decimal of the number two places to the right.
28.003 would then turn into 2,800.3
Step-by-step explanation:
Unfortunately I cannot draw a chart on here but that is the best I can do.
If the new technology innovation improves the production by 10%, they are increasing the amount of cars made by 10%.
Originally, 120 cars were made per day.
10% of 120 is 12.
Since the amount of cars made per day was increased by 12, we can add 12 to 120 to get 132 cars made per day (as the new unit rate).
The question asks how many cars can be produced in 5 days (after the car production increase). We can get the answer by multiplying our new daily amount of cars by 5: 132 times 5.
132 times 5 = 660
So, 660 cars can be produced in the factory in 5 days.
The equation has no solution
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.