business statistics

Stratified Random Sample

Choose a simple random sample from subgroups of a population

Population

the set of all the elements of interest in a study

Probability Sampling

each sample has a known chance of being selected

Parameters

numerical characteristics of a population such as the mean and standard deviation.

Nonprobability Sampling

probability for being selected for the sample is not known

Statistical Inference

purpose is to develop estimates and test hypotheses about population parameters using information contained in a sample i.e. obtain information from a population from information contained in a sample.

Finite Population

Every possible sample of size n out of a population of N has an equally likely chance of occurring

Cluster Sample

Choose a random set of groups and then select all individuals within those groups

Convenience Sample

Choose individuals in an easy, or convenient way

consistency

Consistency is a characteristic of the data. A point estimator is consistent if the values of the point estimator tend to become closer to the population parameter as the sample size becomes larger. The variance becomes smaller as the sample size increases

unbiased

If the expected value of the sample statistic is equal to the population parameter being estimated, the sample statistic is said to be an unbiased estimator of the population parameter.

Judgment

Individual who is most knowledgeable on the subject of the study selects elements of the population that he feels are most representative of the population

Infinite Population

Ongoing process that makes listing or counting every element in the population impossible

Central Limit Theorem

States that the sample means of large- sized samples will be normally distributed regardless of the shape of their population distributions.

Systematic Sample

Systematic sampling is appropriate when we do not have a list of all individuals in a population

efficiency

The point estimator with the smaller standard deviation is said to have greater relative efficiency than the other

Significance level

The probability that any given confidence interval will not contain the true population mean

Sample Statistic

To estimate the value of a population parameter, we compute a corresponding characteristic of the sample

Point Estimation

Use the data from a sample to compute a value of a sample statistic that serves as an estimate of a population parameter

Sampling Error

When the expected value of a point estimator is equal to the population parameter, the point estimator is said to be unbiased.

Sample

a subset of the population

Confidence interval for the mean

is an interval estimate around a sample mean that provides a range where the population mean lies

Confidence level

is the probability that the interval estimate will include the population parameter of interest