*(Tests of Normality)***– A graph which is utilized to assess data for normality.**

__Q-Q Plot__**– A graph which is utilized to assess data for normality.**

__P-P Plot__**– A test which is utilized to test data for normality.**

__Shapiro-Wilk Normality Test__

*(Tests Related to Parametric Model Variable Correlation)***– A method which tests model variables for correlation.**

__Variance Influence Factor__**– A method which tests variables for correlation.**

__(Pearson) Coefficient of Correlation__**- A method which is utilized to measure the correlation between two variables, while also controlling for a third variable.**

__Partial Correlation__**– A method which tests model variables for correlation through the utilization of a Euclidean distance formula.**

__Distance Correlation__**– A method which assesses model variables for correlation through the combination of model variables into independent groups.**

__Canonical Correlation__

*(Tests Related to Non-Parametric Model Variable Correlation)*

**- A non-parametric alternative to the Pearson correlation. This method is utilized in circumstances when either data samples are non-linear, or the data type contained within those samples are ordinal. An example of ordinal data – “survey response data which asked the respondent to rank a particular item on a scale of 1-10”.**

__Spearman’s Rank Correlation__**- Like Spearman’s rho, Kendall’s Tau is also utilized in circumstances when either data samples are non-linear, or the data type contained within the samples is ordinal.**

__Kendall Rank Correlation Coefficient__

*(Tests of Significance Amongst Groups)***- This test is utilized to compare a sample mean to a specific value, it is used when the dependent variable is measured at the interval or ratio level.**

__One Sample T-Test__**- This test functions in the same manner as the above test. However, in the case of this model, data is randomly sampled from different sets of items from two separate control groups.**

__Two Sample T-Test__**- This test functions in the same manner as the above test. The only difference being, this method is utilized if data is randomly sampled from different sets of items from two separate control groups of uneven size.**

__The Welch Two Sample T-Test__**– Similar in composition to the Two Sample T-Test, this test is utilized if you are sampling the same set twice, once for each variable.**

__Paired T-Test__

*(Analysis of Variance “ANOVA”)*

**– Also known as ANOVA, this method is utilized to test for significance across the variances of multiple sample groups. In many ways, this test is similar to a t-test, however, ANOVA allows for multiple group comparison.**

__Analysis of Variance__**– An ANOVA model containing a single independent variable.**

__One Way Analysis of Variance (ANOVA)__**- An ANOVA model containing multiple independent variables.**

__Two Way Analysis of Variance (ANOVA)__**– An ANOVA model containing a single independent variable measured multiple times.**

__Repeated-Measures Analysis of Variance (ANOVA)__

*(Exotic Analysis of Variance “ANOVA” Variants)***– An ANOVA model which also factors for a covariate value which may impact the system as a whole.**

__Analysis of Covariance (ANCOVA)__https://statistics.laerd.com/spss-tutorials/ancova-using-spss-statistics.php

**– An ANOVA model which is synthesized from sampling from a greater population in order to determine inference.**

__Random Effects Analysis of Variance__https://stat.ethz.ch/education/semesters/as2015/anova/06_Random_Effects.pdf

**– An ANOVA model containing multiple dependent variables.**

__Multivariate Analysis of Variance (MANOVA)__https://statistics.laerd.com/spss-tutorials/one-way-manova-using-spss-statistics.php

**– An ANOVA model containing multiple dependent variables. Also factors for a covariate value which may impact the system as a whole.**

__Multivariate of Covariance (MANCOVA)__https://statistics.laerd.com/spss-tutorials/one-way-mancova-using-spss-statistics.php

*(Test of Significance for Nonparametric Data)***– The nonparametric alternative to a One Way ANOVA test.**

__Friedman Test (One Way Analysis of Variance)__**– The nonparametric alternative to the One Sample T-Test, and the Paired T-Test.**

__Wilcox Signed Rank Test (One Sample T-Test, Paired T-Test)__**– A nonparametric alternative to the One Way ANOVA test.**

__Mann-Whitney U Test (Two Sample T-Test)__

*(Tests of Significance Amongst Groups)***– A test which measures categorical significance as it pertains to a binary outcome variable.**

__Chi-Square__**– A test which measures categorical significance, limited to two initial categories, and two categorical outcomes. This test is typically utilized for drug trials.**

__McNemar's Test__

*(Metric to Assess Rate of Agreement Amongst Two Entitles)***– A test which measures the rate of agreement amongst two entities.**

__Cohen’s Kappa__

*(Tests of Significance Amongst Groups Comprised of Survey Questions)***- Cronbach’s Alpha is primarily utilized to measure the inter-relatedness of response data collected from sociological surveys. Specifically, the potential differentiation of response information related to certain interrelated categorical survey questions.**

__Cronbach’s Alpha__

*(Tests Pertaining to Stationarity and Random Walks)***– A methodology of analysis utilized to test data for stationarity.**

__Dicky-Fuller Test__**– A methodology utilized to test data for random walk potential.**

__Phillips-Perron Unit Root Test__

*(Comparison of Outcome Variables)***– A method which assesses model outcome variables through the utilization of a clustering technique.**

__Two Step Cluster__**- A method which assesses model outcome variables through the utilization of a clustering technique.**

__K-Means__**- A method which assesses model outcome variables through the utilization of a hierarchal technique.**

__Hierarchical Cluster__**– A method which compares similarity of outcome variables as determined by the values of the model’s independent variables.**

__K-Nearest Neighbor__

*(Reduction of Independent Variables through Variable Synthesis)***– A method which creates new variables with values that are determined by the original values of the independent model variables.**

__Dimension Reduction__

*(Impact Assessment)***– A method of analysis typically utilized for product and design studies. This technique assesses the most effective way to reach a sample target demographic.**

__TURF Analysis__

*(Survival Analysis)***- A statistical methodology which measures the probability of an event occurring within a group over a period of time.**

__Survival Analysis__

*(Sample Distribution Tests)***- A method for analyzing a single data set in order to determine whether the elements within the data set were sampled independently.**

__The Wald Wolfowitz Test__**- A method for analyzing two separate sets of data in order to determine whether they originate from similar distributions.**

__The Wald Wolfowitz Test (2-Sample)__**- A method for analyzing a single data set in order to determine whether the data was sampled from a normally distributed population.**

__The Kolmogorov-Smirnov Test__**- A method for analyzing two separate sets of data in order to determine whether they originate from similar distributions.**

__The Kolmogorov-Smirnov Test (2-Sample)__

*(Outcome Models – Conditions for Utilization)***– Continuous outcome variable. Continuous independent variable(s).**

__Linear Regression__**– Continuous outcome variable. Any type of independent variable(s).**

__General Linear Mixed Models__**– Binary outcome variable. Categorical or continuous independent variable(s).**

__Logistic Regression Analysis__**– Binary outcome variable. Categorical or continuous independent variable(s).**

__Discriminant Analysis__**- Binary outcome variable. Categorical independent variable(s).**

__Loglinear Analysis__**– Any type of outcome variable. Any type of independent variable(s).**

__Partial Least Squares Regression__**– Continuous outcome variable. Continuous independent variable(s).**

__Polynomial Regression__**– Categorical outcome variable. Categorical input variable(s).**

__Multinomial Logistical Analysis__**– Categorical outcome variable. Categorical input variable(s).**

__Logistical Ordinal Regression__**– Binary outcome variable. Categorical or continuous input variable(s).**

__Probit Regression__**- Categorical outcome variable. Continuous independent variable(s).**

__2-Stage Least Squares Regression__
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