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