QA 233 Freling Test 2

Exponential Probability Distribution

A continious probability distribution that is useful in computing probabilities for the time it takes to complete a task

Normal Probability Distribution

A continious probability distribution. Its probability density function is bell shaped and determined by its mean and standard deviation

Probability Density Function

A function used to compute probabilites for a conitinous random variable. The area under the graph of a probaility density function over an interval represents probability.

Standard Normal probability Distribution

A normal distribution with a mean of zero and a standard deviation of one

Uniform Probability Distribution

A continuous probability distribution for which the probability that the random variable will assume a value in any interval the dame for each interval of equal length

Big Data

Any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software.

Central Limit Theorem

A theorem that enables one to use the normal probability distribution to approximate the sampling distribution of mean whenever the sample size is large

CLuster Sampling

A probability sampling method in which the population is first divided into clusters and then a simple random sample of the clusters is taken

Conveneience Sampling

A nonprobability method of sampling whereby elements are selected for the sample on the basis of conveinance

Coverage Error

Nonsampling error that results when the research objective and the population from which the sample is to be drawn are not alligned

Finite Population Correction Factor

the term √((N-1)/(n-1)) that is used in the formulas for standard deviation of x bar and p bar whenever a finite population, rather than an infinite population, is being sampled. the generally expected rule of thumb is to ignore this when n/N ≤ .05

Frame

A listing of the elements the sample will be selected from

Judgment Sampling

A nonprobability method of sampling whereby elements are selected for the sample based on the judgment of the person doing the study

Measurement Error

Nonsampling error that results from the incorrect or imprecisse measurement of the population characteristic of interest

Nonresponse Error

Nonsampling error that results when potential respondents that belong to some segment of the population are less likely to respond to the survey mechanism than potential respondants that belong to other segments of the population

Nonsampling Error

All types of errors other than sampling error, such as coverage error, nonresponnse error, measurement error, interviewer error, and processing error

Paremeter

A numerical characteristic of a population, such as population mean, a population standard deviation, or a population porportion

Pointe Estimate

The value of a point estimator used in a particular instance as an estimate of a population parameter

Point Estimator

The sample statistic, such as xbar, sample mean, or sample porportion, that provides the point estimate of the population parameter

Random Sample

A random sample from an infinite population is a sample selected such that the following conditions are satisfied:

1. Each element selected comes from the same population

2. Each element is selected independently

Sample Stattistic

A sample characteristic, such as a sample mean , standard deviation, or a sample porportion. The value of the sample statistic is used to esimate the value of the corresponding population parameter

Sampled Population

The population from which the sample is taken

Sampling Distribution

A probability distribution consisting of all possible values of a sample statistic

Sampling Error

The error that occurs because a samplel, and not the entire population, is used to estmate a population parameter

Simple Random Sample

A simple random sample of size n from a finite population of size N is a sample selected such that each possible sample of size n has the same probability of being selected.

Standard Error

The standard deviation of a point estimator

Stratified Random Sampling

A probability sampling method in which the population is first divided into strata and a simple random sample is then taken from each stratum

Systematic Sampling

A probability sampling method in which we randomly slect one of the first k elements and the nselect every kth element thereafter

Tall Data

A data set that has so many observations that traditional statistical inference has little meaning

Target Population

The population for which statistical inferences such as point estimated are made. It is important for the target population to correspond as closely as possible to the sampled population

Unbiased

A property of a point estimator that is present when the expected value of the point estimator is equal to the population parameter it estimates

Variety

The diversity in types and structures of the data generated

Velocity

The speed at which the data are generated

Veracity

The reliability of the data generated

Volume

The amount of data generated

Wide Data

A data set that has so many variable that simultaneous consideration of all variables in infeasible

Confidence Coefficient

The confoidence level expressed as a decimal value. For example, .95 is the confidence coefficient for 95% confidence level

Confidence Interval

Another name for an interval estimate

Confidence Level

The confidence associated with an interval estimate. For example, if an interval estimation procedure provides intervals such that 95% of the intervals formed using the procedure will include the population parameter, the interval estimate is said to be constructed at the 95% confidence level

Degrees of Freedom

A parameter of the t distribution. When the t distribution is used in the computation of an interval estimate of a population mean, the appropriate t distributions has n-1 degrees of freedom, where n is the size of the sample

Interval Estimates

An estimate of a population parameter that provides an interval believed to contain the value of the parameter. For the interval estimates i nthis chapter, it hs the form: point estimate does not equal the margin of error

Level of Significance

The probability that the interval estimation procedure will generate an interval that does not contain the mean

Margin of Error

The + or - value added to and substracted from a point estimate in order to develop an interval estimate of a population parameter

Practical Significance

The real-world impact the result of statistical inference will have on business decisions

Known Standard Deviation

The case when historical data or other information provide a good value for the population standard deviation prior to taking a sample. The interval estimation procedure uses this lnown value of the standard deviation in computing the margin of error

Unkown Standard Deviation

The more common case when no good basis exists for estimating the population standard deviation prior to taking the sample. The interval estimation procedure uses the sample standard deviation s in computing the margin of error

T Distribution

A family of probability distributions that can be used to develop an interval estimate of a population mean whenever the population standard deviation is unknown and is estimated by the sample standard deviation S