Sampling & Experiments
Tests and inferences
Data relationships
Probability and distributions
Symbols
100

Neither the subject nor those who measure the reponse variable know which treatment a subject received.

double-blind experiment

100

The type of significance test used for the mean of a single population when the standard deviation of the population is unknown.

T-test

100

observed y - predicted y

residual

100

This type of random variable requires a fixed number of trials. 

binomial random variable

100

a

alpha

200

A common form of blocking for comparing just two treatments.

matched pairs

200

The formula to calculate the one-sample z statistic.

z = (x bar minus mu sub o) divided by (sigma divided by the square root of n)

200

Measures the direction and strength of a linear relationship between two quantitative variables.

correlation or "r"

200

Events that have no outcomes in common and can never occur simultaneously, for which the addition rule is used.

mutually exclusive events

200

σ

sigma

300

When some groups in the population are left out of the process of choosing a sample

 undercoverage



300

The conditions to use this test include all expected counts be greater than or equal to 1 and no more than 20% of all the expected counts be less than 5.

goodness of fit test

300

The fraction of the variables in the values of y that is explained by the LSR of y on x.

coefficient of determination

300

The type of variable where the probability distribution assigns probability as the area under the density curve above a specific interval.

contnous random variable

300

x-bar

400

The population is divided into groups. Some groups are randomly selected and all individuals in the chosen groups are sampled.

cluster sampling

400

Two of the conditions to be verified for inference about a proportion.

the population size be greater than or equal to 10n and n times p hat & n times (1 - p hat) be greater than or equal to 10



400

Applying a logarithmic transformation to both variables causes this type of model to become linear

power model

400

The condition involving the population size that must be satisfied to use sigma divided by the square root of n as the standard deviation of a sampling distribution.

the population is at least 10 times the sample size

400

P(A)

probability of event a

500

The effects of two variables on the response cannot be distinguished from each other. 

confounding

500

b +/- t*SE sub b


the confidence interval for slope beta of a true regression line

500

when the p-value is low do what to the null

reject it

500

For a normal distribution, approximately 68% of the observations are within 1 standard deviation of the mean, approximately 95% of observations are within 2 standard deviations of the mean, and approximately 99.7% of observations are within 3 standard deviations of the mean

the empirical rule

500

N

population size

M
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