- StudyBlue
- Arkansas
- University of Arkansas - Fayetteville
- Business
- Business 1033
- Jensen
- Data Analysis: 1/18- 1/27

Rachel C.

standard deviation

square root of the variance and possibly the most important statistical inference we will use

Ordinal Data

appear nominal but values have order like 1st, 2nd, 3rd place in race

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inferential statistics

draw conclusions about characteristics of a population based on sample data

sigma

standard deviation for the population

mean

average

variance

a measure of variability

xbar

mean for the sample

interval data

real numbers

mode

most frequently occuring score

nominal data

categorical data

Sample

subset of the population ie all incoming freshmen to the WCOB in 2010

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descriptive statistics

organizing, summarizing, and presenting data

Population

a group of all items of interest ie all incoming freshmen to the U of A in 2010

statistic

used to make inferences about parameters - describes a sample

median

exact middle score

confidence level

proportion of times an estimating procedure is correct

significance level

measures how frequently a conclusion will be wrong in the long run

range

the difference between the highest and lowest number in a data set

empirical rule

used to explain a distribution of scores when they are bell shaped

parameter

descriptive measure of a population

correlation

the covariance divided by standard deviation - shows how variables are linearly related

little s

standard deviation for the sample

Mu

mean for a population

Standard Error

the standard deviation of the sampling distribution

Central Limit Theorem

the sampling distribution of the mean of a random sample drawn from any population is approximately normal for a sufficiently large sample size. The larger the sample size, the more closely the sampling distribution of xbar will resemble a normal distribution

Finite Population Correction Factor

the standard error is where N is the population size

Standard Error of the Proportion

the standard deviation of P^

Point Estimator

draws inferences about a population by estimating the value of an unknown parameter using a single value or point

interval estimator

draws inferences about a population by estimating the value of an unknown parameter using an interval

Unbiased Estimator

estimator whose expected value is equal to a population parameter

Consistency

if the difference between the estimator and the parameter grows smaller as the sample size grows larger

Relatively Efficient

if there are two unbiased estimators of a parameter, the one whose variance is smaller is called

95% Confidence Interval Estimator of Standard Deviation

the resulting confidence interval estimator

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