for a normal population with = 80 and = 20 which of the following samples course hero

What is the z-score of the sample mean for a population?

1. For a normal population with m = 80 and theta = 20 which of the following sample means is an extreme and unrepresentative value?". a. M greater than 90 for a sample of n =4. b. M greater than 85 for a sample of n = 4. C. M greater than 85 for a sample of n = 25 D. M greater than 90 for a sample of n = 25 2. A random sample of n = 9 scores is obtained from a population with m=50 …

What is the mean and standard deviation of the a population?

Oct 23, 2020 · Height, birth weight, reading ability, job satisfaction, or SAT scores are just a few examples of such variables. Because normally distributed variables are so common, many statistical tests are designed for normally distributed populations. Understanding the properties of normal distributions means you can use inferential statistics to compare ...

What is the z-score of a population with m=37 and O=6?

Math. Statistics and Probability. Statistics and Probability questions and answers. For a normal population with m = 40 and s = 10 which of the following samples is least likely to be obtained? σm = 38 for a sample of n = 4 σm = 36 for a sample of n = 4 σm = 38 for a sample of n = 100 σm = 36 for a sample of n = 100.

What is a normal distribution?

In a normal distribution , data is symmetrically distributed with no skew. Most values cluster around a central region, with values tapering off a...

What is a standard normal distribution?

The standard normal distribution , also called the z -distribution, is a special normal distribution where the mean is 0 and the standard de...

What is the empirical rule?

The empirical rule, or the 68-95-99.7 rule, tells you where most of the values lie in a normal distribution : Around 68% of values are within 1 s...

What is a t-distribution?

The t -distribution is a way of describing a set of observations where most observations fall close to the mean , and the rest of the observatio...

Why are statistical tests designed for normally distributed populations?

Because normally distributed variables are so common, many statistical tests are designed for normally distributed populations. Understanding the properties of normal distributions means you can use inferential statistics to compare different groups and make estimates about populations using samples.

What are some examples of normally distributed variables?

All kinds of variables in natural and social sciences are normally or approximately normally distributed. Height, birth weight, reading ability, job satisfaction, or SAT scores are just a few examples of such variables. Because normally distributed variables are so common, many statistical tests are designed for normally distributed populations.

How to fit a normal curve to data?

Formula of the normal curve. Once you have the mean and standard deviation of a normal distribution, you can fit a normal curve to your data using a probability density function. In a probability density function, the area under the curve tells you probability.

What are the characteristics of a normal distribution?

Normal distributions have key characteristics that are easy to spot in graphs: The mean, median and mode are exactly the same. The distribution is symmetric about the mean—half the values fall below the mean and half above the mean. The distribution can be described by two values: the mean and the standard deviation.

What happens when you increase the sample size?

Law of Large Numbers: As you increase sample size (or the number of samples), then the sample mean will approach the population mean. With multiple large samples, the sampling distribution of the mean is normally distributed, even if your original variable is not normally distributed.

What is a z score?

Each z -score is associated with a probability, or p -value, that tells you the likelihood of values below that z -score occurring. If you convert an individual value into a z -score, you can then find the probability of all values up to that value occurring in a normal distribution.

Is a sample size of 30 or more considered large?

A sample size of 30 or more is generally considered large . For small samples, the assumption of normality is important because the sampling distribution of the mean isn’t known. For accurate results, you have to be sure that the population is normally distributed before you can use parametric tests with small samples.