### The Systematic Review

An overview based on a series of guidelines published in Joanna Briggs Library

An overview based on a series of guidelines published in Joanna Briggs Library

Running non-parametric tests on COVID-19 data collected for the U.S. and several individual states. Spearman and Kendall correlation coefficients, Mann-Whitney, Mann-Kendall tests, and Sen’s slopes explore possible trends in the data.

Do first born children have more self-control and self-esteem? Are African Americans who murder whites disproportionally sentenced to death? Do the data of an experiment follow a well-determined distribution? Chi-square goodness of fit and chi-square test of independence come to the rescue!

Multiple regression models based on coded and un-coded data. Multiple regression models for output variables measured in different units. Lack of fit. Canonical path.

Determine when regression analysis is the appropriate technique in analyzing an experiment. Check the important assumptions underlying regression analysis.

Read and interpret the results of the R output. Study the graphical presentation of the surface associated to the model.

Matrix equation equivalent to a second-order regression model. Canonical analysis (Linear Algebra comes to the rescue). Surface associated to the second-order regression model on the coordinate system of eigenvectors.

by Christine Apostolopoulou

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- Canonical Analysis
- Chi-square Goodness-of-Fit Test
- Chi-square Test for Independence
- Coded Data
- Contingency Tables
- COVID-19 Data
- Diagnostic Plots
- Eigenvectors and Eigenvalues of a Matrix
- Kendall's Correlation Coefficient
- Lack of Fit
- Mann-Kendall Test
- Matrix Equation Equivalent to a Second Order Regression Model
- Multiple Regression Model
- Non-Parametric Tests
- Response Surface
- Sen's Slope
- Spearman's Correlation Coefficients
- Stationary Points of a Surface
- Systematic Reviews
- Uncoded Data