Statistics Dictionary
To see a definition, select a term from the dropdown text box below. The statistics
dictionary will display the definition, plus links to related web pages.

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Statistics Dictionary
Absolute Value
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Column Vector
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Cumulative Frequency Plot
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Decision Rule
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Diagonal Matrix
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Identity Matrix
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Interaction Plot
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Interval Scale
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Joint Frequency
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Law of Large Numbers
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Line
Linear Combination of Vectors
Linear Dependence of Vectors
Linear Transformation
Logarithm
Lurking Variable
Margin of Error
Marginal Distribution
Marginal Frequency
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Matched Pairs Design
Matched-Pairs t-Test
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Mauchly's Sphericity Test
Mean
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Measurement Scales
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Mode
Multicollinearity
Multinomial Distribution
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Multiplication Rule
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Mutually Exclusive
Natural Logarithm
Negative Binomial Distribution
Negative Binomial Experiment
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Neyman Allocation
Nominal Scale
Nonlinear Transformation
Non-Probability Sampling
Nonresponse Bias
Normal Distribution
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One-Sample t-Test
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One-Way ANOVA
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Ordinal Scale
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Paired Data
Parallel Boxplots
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Pearson Product-Moment Correlation
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Poisson Distribution
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Random Effects Model
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Reduced Row Echelon Form
Region of Acceptance
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Regression
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Relative Frequency Table
Repeated Measures Design
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Test Statistic
Transpose
Treatment
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Two-Sample t-Test
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Two-Way Table
Type I Error
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Unbiased Estimate
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Uniform Distribution
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Union
Univariate Data
Variable
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Variance Inflation Factor
Vector Inner Product
Vector Outer Product
Vectors
Venn Diagram
Voluntary Response Bias
Voluntary Sample
Y Intercept
z-score

Hypergeometric Probability
Hypergeometric probability is the probability that an n -trial
hypergeometric experiment
results in exactly x successes,
when the population consists of N items, k of which are
classified as successes.

Hypergeometric probability is denoted by h(x ; N , n , k )
and can be computed according to the hypergeometric formula below.

Hypergeometric Formula. Suppose a
population consists of

N items,

k of which are successes. And a
random sample drawn from that population consists on

n items,

x of
which are successes. Then the hypergeometric probability is:

h(x ; N , n ,
k ) = [ _{k} C_{x} ] [ _{N-k} C_{n-x} ] / [ _{
N
} C_{n} ]

where

_{k} C

_{x} is the combination of

k things taken

x at a time.