AP Stats Chapter 7 Vocabulary

Central Limit Theorem (CLT)

when n is large (equal to or greater than 30), the sampling distribution of the sample mean is approximately Normal

use with sample distribution of mean only

Distribution of sample data

shows values of variable for each individual in sample

idea is to take many samples, collect proportions, and display in graph

Large counts condition (LCC)

use for Normal approximation

when n is large, the sampling distribution is close to Normal distribution with mean p and standard deviation (p(1-p)^.5

np equal to or greater than 10 and n(1-p) equal to or greater than 10

both of these conditions must be true to approximate Normal

Parameter

a number that describes some characteristic of a population

mean, standard deviation, proportion are examples

Point estimate

the value of a statistics from a sample used to provide an estimate of a population parameter

Population Distribution

The values for a variable for all individuals in the population

Sampling distribution

the distribution of a statistic (mean, proportion, s) in all possible samples of the same size from the same population

Sampling distribution of a sample mean x-bar

the distribution of values taken by the sample mean x-bar in all possible samples of the same size from the same population

mean is x-bar and equal to mean of population proportion, SD is standard deviation over square root of sample size if 10% condition is satisfied

use CLT here to prove Normality

Sampling distribution of a sample proportion p-hat

descriebs how the statistic vaiers in all possible samples from population

Mean is mean of proportion, standard deviation square root of p(1-p)/n if 10% condition is met

Normal by LCC; as n increases, Normality increases

Sampling variability

the value of a statistics varies in repeated sampling

Statistic

a number that describes a characteristic of a sample

used to estimate parameter

either mean, standard dev. or proportion

Unbiased estimator

A statistics used to estimate a parameter

only qualifies if the mean of it's sampling distribution is equal to the true value for the parameter being estimated

Variability of a statistic

described by the spread of its sampling distribution

spread is determined by the size of random sample

larger sample means smaller spread

spread is independent of population if 10% condition applies