| A |
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| * 10% Condition |
sample sizes should be no more than 10% of the population. |
10% Condition |
| * Absolute standard deviation |
the distance between each value in the data set and that data set’s mean or median |
Standard Deviation |
| * Adjusted R2 (Adjusted R-Squared) |
|
Adjusted R2 / Adjusted R-Squared |
| * Admissible Decision Rule |
An admissible decision rule is a rule for making a statistical decision |
Admissible Decision Rule |
| * α Alpha Level (Significance Level) |
the probability of making the wrong decision when the null hypothesis is true. |
α Alpha Level |
| * Alternate Hypothesis |
an alternative to the null |
Alternate Hypothesis |
| * ANOVA |
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ANOVA |
| * Arithmetic Mean |
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| * ARMA model |
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| * Asch Paradigm. |
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| * Assumption of Independence |
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| * Assumption of Normality |
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| * Average Deviation |
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| * B |
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| * Bell’s Numbers |
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| * Berkson’s Paradox |
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| * Bessel’s Correction. |
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| * Beta Level |
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| * Bias |
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| * Bin |
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| * Binomial Coefficient |
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| * Binomial Distribution |
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| * Bivariate Analysis |
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| * Bogardus Scale |
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| * Box and Whiskers Chart |
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| * Bray Curtis Dissimilarity. |
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| * Business Statistics |
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| * C |
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| * Cardinal Numbers |
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| * Causation |
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| * Cauchy-Schwarz Inequality |
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| * Ceiling Effect |
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| * Censoring |
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| * Central Tendency |
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| * Chauvenet’s Criterion |
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| * Chebyshev’s Inequality |
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| * Chi-Square Statistic |
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| * Classical Probability |
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| * Classical Test Theory |
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| * Closed form solution |
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| * Cluster Sampling |
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| * Clustered Standard Errors |
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| * Clustering |
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| * Cochran’s Q |
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| * Coefficients. |
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| * Coefficient of Association. |
measures the strength of a relationship |
Coefficient of Association |
| * Coefficient of Determination |
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| * Cohen’s Kappa Statistic |
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| * Cohort Study |
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| * Collinear |
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| * Combined Mean |
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| * Concordant and Discordant Pairs |
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| * Conditional Distribution |
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| * Conditional Probability |
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| * Condition Indices |
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| * Confidence Level |
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| * Conservative |
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| * Consistent Estimator |
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| * Contrast. |
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| * Construct Validity |
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| * Content Validity |
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| * Contingency Table |
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| * Continuity Correction Factor |
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| * Contour Plot |
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| * Correlation Coefficient Formula |
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| * Correlogram |
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| * Covariance in Statistics |
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| * Cramer-Rao Lower Bound |
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| * Cronbach’s Alpha |
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| * Cross Covariance |
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| * Cross Validation |
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| * Cumulant Generating Function |
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| * Curvilinear |
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| * D |
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| * Data Distribution |
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| * Data Mining / Data Sets |
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| * Decile |
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| * Delphi Method |
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| * The Delta Method |
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| * Density Curve |
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| * Design Effect |
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| * Deterministic |
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| * Difference in Differences |
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| * Dimensionality |
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| * Direction of Association |
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| * Discrete Choice Models |
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| * Discretization |
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| * Dispersion |
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| * Dot Plot |
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| * Durbin Watson Test |
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| * E |
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| * Effect Size |
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| * Empirical Research |
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| * Empirical Rule |
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| * Equivalence Class |
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| * Error Bar |
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| * Error Term |
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| * Estimator |
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| * ETA squared |
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| * Euler’s Number. |
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| * Expected Value Formula |
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| * Expected Monetary Value |
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| * Experimental Design |
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| * Explained Variance |
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| * External Validity |
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| * Extrapolation |
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| * F |
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| * Factor Analysis |
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| * Factorial! |
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| * F Statistic |
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F-Test |
| * False Alarm Ratio |
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| * False Positive and Negative |
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| * Finite Sets / Infinite Sets and Statistics |
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| * Fisher’s Exact Test of Independence |
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| * Fisher Information |
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| * Fisher Z |
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| * Five Number Summary |
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| * Fixed Effects |
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| * Floor Effect |
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| * Fractile |
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| * Free parameter |
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| * Friedman’s Test |
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| * Frequency Distribution Table |
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| * Frequency Domain |
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| * Frequency Polygon |
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| * Frequentist Statistics |
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| * Fudicial Inference |
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| * G |
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| * Gamma Function |
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| * Gauss Markov Theorem and Assumptions. |
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| * Geodesy |
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| * Geometric Distribution |
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| * Geometric Mean |
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| * Gini Coefficient |
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| * Goldfeld Quandt Test |
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| * Gold Standard Test |
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| * Goodness of Fit Test |
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| * Graph Theory |
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| * Greatest Possible Error |
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| * Greedy Matching Algorithm |
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| * Guttman’s Lambda-2 |
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| * H |
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| * Harmonic Mean |
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| * Heterogeneous |
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| * Heteroscedasticity |
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| * Hidden Markov Model |
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| * Hierarchical Linear Modeling |
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| * Hierarchical Model. |
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| * Homogeneity |
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| * Hotelling’s T-Squared. |
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| * Hypergeometric Distribution |
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| * I |
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| * IID |
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| * Ill posed problem |
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| * Ill-conditioning. |
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| * Illusory association. |
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| * Imaginary Numbers |
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| * Implicit Factors |
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| * Implicitization |
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| * Index Number |
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| * Inductive Statistics |
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| * Inferential Statistics |
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| * Infinitely Divisible |
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| * Integer |
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| * Internal Consistency |
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| * Internal Validity |
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| * Interquartile Range |
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| * Interval Estimate |
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| * Interval Scale |
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| * Inverse Distribution Function |
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| * Inverse Normal |
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| * Ipsative |
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| * Item Response Theory |
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| * J |
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| * Jaccard Index |
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| * Jarque-Bera Test |
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| * Jeffreys Prior |
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| * K |
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| * Kelly’s Measure of Skewness |
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| * KL Divergence. |
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| * Kruskal Wallis Test |
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| * Kuder-Richardson |
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| * Kurtosis |
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| * L |
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| * L-Estimator |
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| * Large Enough Sample Condition |
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| * Latent Semantic Analysis |
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| * Law of Large Numbers |
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| * Least Squares Regression Line |
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| * Levene Test |
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| * Likelihood Function. |
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| * Likelihood Ratio |
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| * Likert Scale |
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| * Limits of Agreement |
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| * Line Graph |
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| * Linear Discriminant Analysis |
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| * Location Parameter |
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| * Logarithms |
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| * Log-Rank test |
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| * Lowess Smoothing |
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| * M |
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| * Mann Whitney U Test |
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| * MAPE |
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| * Marginal Effects |
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| * Marginal Mean |
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| * Market Basket Analysis |
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| * Martingale |
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| * Matched Samples |
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| * Mathematical Statistics |
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| * McNemar Test |
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| * Mean |
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| * Mean Error |
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| * Mean Squared Error |
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| * Median |
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| * Median Absolute Deviation |
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| * Method of Moments |
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| * Middle Fifty |
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| * Midpoint. |
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| * Midrange |
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| * Minimal Detectable Difference |
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| * Minimum Description Length. |
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| * Minimum Spanning Tree |
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| * Mode |
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| * Monotone Likelihood Ratio |
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| * Monotonic Relationship |
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| * Monty Hall Problem |
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| * Monte Carlo Simulation |
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| * Morisita Index |
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| * Moving Average |
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| * Moment |
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| * Moment Generating Function |
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| * Multicollinearity |
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| * Multinomial Coefficient |
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| * Multinomial Theorem |
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| * Multiple Imputation |
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| * Multiset |
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| * N |
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| * Natural Number |
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| * Nearest Neighbor Matching. |
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| * Negative Binomial Experiment |
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| * Nested Model |
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| * Nominal Ordinal Interval Ratio |
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| * NonLinearity |
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| * Non Negative Integer |
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| * Non-Parametric Data and Tests |
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| * Non Centrality Parameter |
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| * Normal Probability Plot |
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| * Null Hypothesis |
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| * O |
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| * Observation in Statistics. |
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| * Order Statistics |
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| * Ordinal Numbers and Ordinal Data |
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| * Orthogonality |
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| * P |
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| * P-Value |
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| * Paired Data. |
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| * Parameter |
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| * Parameter Estimation |
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| * Parametric Modeling |
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| * Parametric Tests |
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| * Parameterization. |
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| * Pareto Efficiency. |
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| * Pareto Principle |
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| * Park Test |
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| * Pascal’s Triangle |
|
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| * Pearson Correlation |
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| * Pearson Mode Skewness |
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| * Pearson’s Coefficient of Skewness |
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| * Percent Error |
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| * Percentiles |
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| * Permuted Block Randomization |
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| * Point Estimate |
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| * Population |
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| * Population Mean |
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| * Population Density |
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| * Population Proportion |
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| * Population variance |
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| * Post-hoc |
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| * Power Law |
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| * Power Mean |
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| * Practical Significance |
|
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| * Predictive Analytics |
|
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| * Prevalence |
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| * Prime Numbers |
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| * Principal Component Analysis |
|
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| * Probabilistic Models |
|
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| * Process Capability Analysis. |
|
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| * Probability Density Function |
|
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| * Probability Distribution |
|
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| * Probability Distribution Table |
|
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| * Probability Generating Function |
|
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| * Propensity Score Matching |
|
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| * Proportion of Variance |
|
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| * Proportional reduction in error (PRE Test) |
|
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| * Pygmalion Effect |
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| * Purposive Sampling |
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| * Q |
|
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| * Quantile |
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| * Quartiles |
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| * Quasi-Statistical |
|
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| * Quintile |
|
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| * Q-Value |
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| * R |
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| * Random Seed |
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| * Range |
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| * Random Walk |
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| * Rank Biserial |
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| * Rank Histogram |
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| * Rao Blackwell Theorem |
|
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| * Rate Parameter |
|
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| * Rate Ratio |
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| * Ratios and Rates |
|
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| * Ratio Estimator |
|
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| * Ratio Scale |
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| * Real Numbers |
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| * Receiver Operating Characteristic Curve |
|
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| * Regression Equation |
|
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| * Relative Absolute Error |
|
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| * Relative Dispersion |
|
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| * Relative Error |
|
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| * Relative Precision |
|
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| * RFM (Customer Value) |
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| * Relative Standard Deviation |
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| * Relative Variance |
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| * Reliability |
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| * Representative Sample |
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| * Rescaling Data |
|
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| * Residual values |
|
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| * Residual Sum of Squares |
|
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| * Resistant Statistics |
|
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| * Rician Distribution |
|
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| * Risk Function |
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| * Robust Statistics |
|
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| * S |
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| * Sample Mean |
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| * Sample Statistic |
|
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| * Sampling Frame |
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| * Scale Factor |
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| * Scale Invariance |
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| * Scale Parameter |
|
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| * Seasonal Kendall Test |
|
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| * Segmented Bar Chart |
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| * Semantic Differential Scale |
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| * Semistructured Interview |
|
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| * Sensitivity/Specificity |
|
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| * Sensitivity Analysis |
|
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| * Serial Correlation |
|
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| * Sequence Effects |
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| * Shape Parameter |
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| * Shapiro-Wilk Test |
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| * Shared Variance |
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| * Shifting Data |
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| * Simple Effects (see: Main Effects) |
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| * Simple linear regression |
|
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| * Simple Random Sample |
|
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| * Simpson’s Diversity Index |
|
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| * Simpson’s Paradox |
|
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| * Slope |
|
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| * Slope Stability Analysis |
|
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| * Slutsky’s Theorem |
|
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| * Snowball Sampling |
|
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| * Spearman-Brown Formula |
|
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| * squared deviations |
|
squared deviations |
| * Standard Deviation |
|
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| * Standard Error of Measurement |
|
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| * Standard Error of a Sample |
|
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| * Standardized Residuals |
|
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| * Standardized Variables |
|
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| * Stanine Score |
|
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| * Stationarity |
|
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| * Statistic |
|
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| * Statistical Analysis |
|
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| * Statistical Conclusion Validity |
|
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| * Statistical Noise. |
|
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| * Statistical Power |
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| * Statistical Process Control |
|
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| * Statistical Relationship |
|
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| * Statistical Significance |
|
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| * Statistical Stability |
|
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| * Statistical Treatment |
|
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| * STEN Score |
|
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| * Stratum |
|
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| * Stratification |
|
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| * Stress Strength Model |
|
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| * Student’s T-Test |
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| * Summary Statistics |
|
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| * Summation Notation |
|
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| * Survival Analysis |
|
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| * T |
|
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| * T Score |
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| * T Statistic |
|
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| * Test of Association |
|
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| * Test-Retest Reliability |
|
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| * Test Statistic |
|
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| * Tikhonov Regularization |
|
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| * Thurstone Scale |
|
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| * Tolerance Level |
|
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| * Transformations |
|
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| * Treatment-As-Usual |
|
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| * Triangulation |
|
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| * Trimean |
|
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| * Trimmed Mean / Truncated Mean |
|
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| * Turning Point Test |
|
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| * Tweedie Distribution |
|
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| * Two Way Table |
|
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| * Type I and Type II Errors |
|
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| * Type III and Type IV Errors |
|
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| * U |
|
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| * Unbiased |
|
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| * Uncertainty |
|
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| * Uncertainty Coefficient |
|
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| * Undercoverage |
|
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| * Unidimensionality |
|
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| * Univariate Analysis |
|
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| * U Statistic. |
|
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| * Upper tail and lower tail. |
|
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| * V |
|
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| * Variable |
|
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| * Variability |
|
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| * Variance |
|
Variance |
| * Variance Inflation Factor |
|
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| * Variance Sum Law |
|
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| * Variate. |
|
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| * Varimax Rotation. |
|
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| * Volatility. |
|
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| * V Statistic |
|
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| * W |
|
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| * Weighting Factor |
|
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| * White Test |
|
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| * Whole Number |
|
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| * Wilcoxon Signed Rank Sum Test |
|
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| * Within Mean Square |
|
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| * Weighted Mean |
|
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| * W Statistic |
|
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| * Y/Z |
|
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| * Yates Correction |
|
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| * Z Test |
|
Z-Test |
| * Z-Score |
|