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Essay Available:
Pages:
1 page/≈275 words
Sources:
2 Sources
Style:
APA
Subject:
IT & Computer Science
Type:
Coursework
Language:
English (U.S.)
Document:
MS Word
Date:
Total cost:
$ 7.29
Topic:

Testing Collinearity and Interactions

Coursework Instructions:

Guidelines for Submission: Submit your flowchart in whichever flowcharting tool you prefer (e.g., PowerPoint, Visio, Word). Include the image of the flowchart
along with the written notes as either a Microsoft Word document or a PDF.
Critical Elements:
Indicate steps for testing collinearity and interactions
Indicate steps for identifying variables for the multivariable model
Indicate criteria for assessing model fit
Indicate steps taken for collecting and storing model output
Indicate steps for graphically representing results of modelWords Characters Reading time

Coursework Sample Content Preview:

Flow Chart and Notes
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Flow Chart and Notes
Collinearity
Collinearity is the correlation of independent variables in a linear regression model. A high correlation of independent variables leads to multi-collinearity, making it difficult to interpret the model and also causes overfitting (Glen, 2020). Therefore, it is crucial to test variables for multi-collinearity before developing a regression model. There are two options for testing for multi-collinearity; correlation analysis and Variance Inflation Factor.
Correlation analysis
The correlation of independent variables is computed, and a correlation matrix is developed. We can observe the pairwise correlation between all variables after plotting the correlation matrix and color scaling the background. From the correlation matrix, one can identify the closely related variables and, as a result, consider the variables during the selection of variables.
Variance Inflation Factor(VIF)
Under the Variance Inflation Factor method, every independent variable is tested for Variance Inflation Factor. The VIF of every variable is computed compared to the rest of the regression variables (Glen, 2020). The higher the score for VIF for a variable, the more likelihood of correlated with other...
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