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direction of the relationship between predictors and output vs direction of the relationship between predictors

Both phrases are correct, but they have slightly different meanings. The first phrase refers to the direction of the relationship between predictors and output, while the second phrase focuses solely on the direction of the relationship between predictors. The first phrase is more specific and includes the output variable in the relationship analysis.

Last updated: March 17, 2024

direction of the relationship between predictors and output

This phrase is correct and commonly used in the context of data analysis or statistical modeling.

This phrase is used when discussing the relationship between predictors (independent variables) and the output (dependent variable) in a statistical model or data analysis.

Examples:

In regression analysis, we need to understand the direction of the relationship between predictors and output.
The direction of the relationship between predictors and output can be positive or negative.
Analyzing the direction of the relationship between predictors and output is crucial for model interpretation.
Researchers often study the direction of the relationship between predictors and output to draw meaningful conclusions.
Understanding the direction of the relationship between predictors and output helps in making accurate predictions.

Alternatives:

relationship between predictors and output
relationship between predictors and dependent variable
association between predictors and output
correlation between predictors and output
impact of predictors on output

direction of the relationship between predictors

This phrase is correct and commonly used in the context of data analysis or statistical modeling.

This phrase is used when discussing the relationship between predictors (independent variables) without specifically mentioning the output variable.

Examples:

Understanding the direction of the relationship between predictors is essential in regression analysis.
Researchers often analyze the direction of the relationship between predictors to identify significant variables.
The direction of the relationship between predictors can provide insights into the underlying data patterns.
In data mining, exploring the direction of the relationship between predictors helps in feature selection.
Analyzing the direction of the relationship between predictors is a key step in building predictive models.

Show examples from the web [+]

Dec 3, 2010 ... We found basically the same results and this is supportive of the assumed direction of the relationship between predictors and outcome. Go to: ...
Cross-sectional studies have two important limitations: (1) the direction of the relationship between predictors and outcomes cannot be tested, and (2) the ...
May 13, 2013 ... Direction of the relationship between predictors and outcome variables .................. .............. 44. Figure 6. Multiple regression model when the ...
Aug 23, 2006 ... The direction of the relationship between predictors and predictions, extracted from the calculation of the connection weight algorithm, ...

Alternatives:

relationship between predictors
association between predictors
correlation between predictors
impact of predictors
influence of predictors on outcomes

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