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One-Variable Data

100

This measure of spread is resistant to extreme values.

IQR

200

This measure of center is pulled in the direction of skew.

Mean

300

This distribution shape is suggested when the mean is larger than the median.

Right-skewed

400

These data points dramatically change the slope of a regression line when included.

Influential points

500

This rule identifies outliers using Q1, Q3, and a multiple of the IQR.

1.5×IQR rule

Two-Variable Data

100

This statistic measures the strength and direction of a linear relationship.

r (correlation)

200

This quantity represents the proportion of variation in y explained by x in a regression.

r²

300

This value is computed as observed minus predicted.

Residual

400

This transformation is often used to linearize exponential relationships.

Taking the log of y  

500

This term refers to predicting a response for an x-value outside the observed data range.

Extrapolation

Collecting Data

100

This sampling method selects individuals who are easiest for researchers to reach.

Convenience sampling

200

This feature of experiments allows researchers to infer cause and effect.

Random assignment

300

This experimental design strategy groups subjects by a variable that affects the response before randomizing treatment.

Blocking

400

This type of variable influences both the explanatory and response variables, creating a misleading association.

Lurking (Confounding) variable

500

This sampling method divides the population into homogeneous groups and samples from each group.

Stratified random sampling

Probability & Random Variables

100

This rule computes P(A or B) when the two events overlap.

P(A)+P(B)–P(A and B)

200

This condition defines independence between events A and B.

P(A | B) = P(A)

300

This formula is used to calculate the expected value of a discrete random variable.

Σ(x·p)

400

This is what happens to the variance of X when it is multiplied by a constant a.

It becomes a²Var(X)

500

This is the variance of X – Y when X and Y are independent random variables.

Var(X)+Var(Y)

Sampling Distributions

100

This value is the mean of the sampling distribution of sample means.

μ

200

This quantity is the standard deviation of the sampling distribution of xˉ\bar{x}xˉ.

σ/√n

300

These two conditions establish when the sampling distribution of p^\hat{p}p^ is approximately normal.

np ≥ 10 and n(1–p) ≥ 10

400

This formula gives the standard deviation of the sampling distribution of a sample proportion.

√[p(1–p)/n]

500

This theorem explains why the distribution of sample means becomes approximately normal as n increases.

Central Limit Theorem