Statistics Dictionary
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Statistics Dictionary
Absolute Value
Accuracy
Addition Rule
Alpha
Alternative Hypothesis
ANOVA
Back-to-Back Stemplots
Balanced Design
Bar Chart
Bartlett's Test
Bayes Rule
Bayes Theorem
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Bivariate Data
Blinding
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Boxplot
Cartesian Plane
Categorical Variable
Census
Central Limit Theorem
Chi-Square Distribution
Chi-Square Goodness of Fit Test
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Chi-Square Test for Homogeneity
Chi-Square Test for Independence
Cluster
Cluster Sampling
Coefficient of Determination
Coefficient of Multiple Determination
Column Vector
Combination
Complement
Completely Randomized Design
Conditional Distribution
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Confidence Interval
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Contingency Table
Continuous Probability Distribution
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Critical Parameter Value
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Cumulative Frequency
Cumulative Frequency Plot
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Decision Rule
Degrees of Freedom
Dependent Variable
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Diagonal Matrix
Discrete Probability Distribution
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Disjoint
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Dotplot
Double Bar Chart
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E Notation
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Factorial Experiment
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Gaps in Graphs
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Hartley's Fmax Test
Heterogeneous
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Hypergeometric Experiment
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Identity Matrix
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Independent Groups Design
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Inner Product
Interaction Plot
Interactions
Interquartile Range
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Interval Estimate
Interval Scale
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Joint Frequency
Joint Probability Distribution
Law of Large Numbers
Level
Line
Linear Combination of Vectors
Linear Dependence of Vectors
Linear Transformation
Logarithm
Lurking Variable
Margin of Error
Marginal Distribution
Marginal Frequency
Marginal Mean
Matched Pairs Design
Matched-Pairs t-Test
Matrix
Matrix Dimension
Matrix Inverse
Matrix Order
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Matrix Transpose
Mauchly's Sphericity Test
Mean
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Measurement Scales
Median
Mixed Model
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Multicollinearity
Multinomial Distribution
Multinomial Experiment
Multiple Regression
Multiplication Rule
Multistage Sampling
Mutually Exclusive
Natural Logarithm
Negative Binomial Distribution
Negative Binomial Experiment
Negative Binomial Probability
Negative Binomial Random Variable
Neyman Allocation
Nominal Scale
Nonlinear Transformation
Non-Probability Sampling
Nonresponse Bias
Normal Distribution
Normal Random Variable
Null Hypothesis
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Observational Study
One-Sample t-Test
One-Sample z-Test
One-stage Sampling
One-Tailed Test
One-Way ANOVA
One-Way Table
Optimum Allocation
Ordinal Scale
Outer Product
Outlier
Paired Data
Parallel Boxplots
Parameter
Pearson Product-Moment Correlation
Percentage
Percentile
Permutation
Placebo
Point Estimate
Poisson Distribution
Poisson Experiment
Poisson Probability
Poisson Random Variable
Population
Power
Precision
Probability
Probability Density Function
Probability Distribution
Probability Sampling
Proportion
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Qualitative Variable
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Random Effects Model
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Random Number Table
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Randomized Block Design
Randomized Block Experiment
Range
Ratio Scale
Reduced Row Echelon Form
Region of Acceptance
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Regression
Relative Frequency
Relative Frequency Table
Repeated Measures Design
Replication
Representative
Residual
Residual Plot
Response Bias
Row Echelon Form
Row Vector
Sample
Sample Design
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Sample Space
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Scalar Matrix
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Scatterplot
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Set
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Stemplot
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Subtraction Rule
Sum Vector
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Symmetric Matrix
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Systematic Sampling
T Distribution
T Score
T Statistic
Test Statistic
Transpose
Treatment
t-Test
Two-Sample t-Test
Two-stage Sampling
Two-Tailed Test
Two-Way Table
Type I Error
Type II Error
Unbiased Estimate
Undercoverage
Uniform Distribution
Unimodal Distribution
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

Negative Binomial Experiment
A negative binomial experiment is a
statistical experiment
that has the following properties:

The experiment consists of x repeated trials.
Each trial can result in just two possible outcomes. We call one of these
outcomes a success and the other, a failure.
The probability of success, denoted by p , is the same on every
trial.
The trials are independent ;
that is, the outcome on one trial does not affect the outcome on other trials.
The experiment continues until r successes are observed, where r
is specified in advance.
Consider the following statistical experiment. You flip a coin repeatedly and count
the number of times the coin lands on heads. You continue flipping the coin until
it has landed 5 times on heads. This is a negative binomial experiment
because:

The experiment consists of repeated trials. We flip a coin repeatedly until it
has landed 5 times on heads.
Each trial can result in just two possible outcomes - heads or tails.
The probability of success is constant - 0.5 on every trial.
The trials are independent; that is, getting heads on one trial does not affect
whether we get heads on other trials.
The experiment continues until a fixed number of successes have occurred;
in this case, 5 heads.