Explanation:
The sample mean is not always equal to the population mean but if we take more and more number of samples from the population then the mean of the sample would become equal to the population mean.
The Central Limit Theorem states that we can have a normal distribution of sample means even if the original population doesn't follow normal distribution, But we have to take a lot of samples.
Suppose a population doesn't follow normal distribution and is very skewed then we can still have sampling distribution that is completely normal if we take a lot of samples.
(1) answer for this question is 6/51 to 1
(2)the answer for this question is (d)58.50
Experimental probability is the outcome during a certain experiment. add up the total number of times it was spinned. You should get 40. To find the experimental probability of each section, simply divide the number by 40. This will give you the percentage for each one. For example, spinner A. 14/40=.35 Multiply this by one hundred to find the percentage (35%). Do this for all and let me know if you need help converting them to fractions. :)
