Statistics Dictionary
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Statistics Dictionary
Absolute Value
Accuracy
Addition Rule
Alpha
Alternative Hypothesis
Back-to-Back Stemplots
Bar Chart
Bayes Rule
Bayes Theorem
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Biased Estimate
Bimodal Distribution
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Bivariate Data
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Cartesian Plane
Categorical Variable
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Central Limit Theorem
Chi-Square Distribution
Chi-Square Goodness of Fit Test
Chi-Square Statistic
Chi-Square Test for Homogeneity
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Cluster
Cluster Sampling
Coefficient of Determination
Coefficient of Multiple Determination
Column Vector
Combination
Complement
Completely Randomized Design
Conditional Distribution
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Decision Rule
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Diagonal Matrix
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Discriminant Analysis
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E Notation
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Gaps in Graphs
Geometric Distribution
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Histogram
Homogeneous
Hypergeometric Distribution
Hypergeometric Experiment
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Interaction Plot
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Margin of Error
Marginal Distribution
Marginal Frequency
Matched Pairs Design
Matched-Pairs t-Test
Matrix
Matrix Dimension
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Matrix Transpose
Mean
Measurement Scales
Median
Mode
Multicollinearity
Multinomial Distribution
Multinomial Experiment
Multiple Regression
Multiplication Rule
Multistage Sampling
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
Normal Random Variable
Null Hypothesis
Null Set
Observational Study
One-Sample t-Test
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One-stage Sampling
One-Tailed Test
One-Way Table
Optimum Allocation
Ordinal Scale
Outer Product
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Paired Data
Parallel Boxplots
Parameter
Pearson Product-Moment Correlation
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Proportion
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Qualitative Variable
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Random Number Table
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Range
Ratio Scale
Reduced Row Echelon Form
Region of Acceptance
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Regression
Relative Frequency
Relative Frequency Table
Replication
Representative
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Row Echelon Form
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Scalar Matrix
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Scatterplot
Selection Bias
Set
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Simple Random Sampling
Simple Regression
Singular Matrix
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Standard Deviation
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Subtraction Rule
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T Distribution
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Transpose
Treatment
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Two-Tailed Test
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Type I Error
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Unbiased Estimate
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Uniform Distribution
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Union
Univariate Data
Variable
Variance
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.