Answer the statistics questions for 50 points! (: (Standard deviation, distribution, plots)

What conditions do you have to check before you can assume a distribution is normal?

How do you find the mean and standard deviation of sampling distribution?

Finding how big a sample size needs to be to find a certain standard deviation.

Finding the Probability from a sample distribution.

Interpreting a Confidence Interval and understanding statistical significance.

How to find margin of error and how the margin of error changes when sample size
changes.

How to find a Confidence Interval from a stemplot.

The central limit theorem states that if you have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement text annotation indicator, 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 (usually n > 30). If the population is normal, then the theorem holds true even for samples smaller than 30. In fact, this also holds true even if the population is binomial, provided that min(np, n(1-p))> 5, where n is the sample size and p is the probability of success in the population. This means that we can use the normal probability model to quantify uncertainty when making inferences about a population mean based on the sample mean.

Step-by-step explanation:

step-by-step explanation: