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Multiple Linear Regression

2017
In the previous chapter, we discussed situations where we had only one independent variable (X ) and evaluated its relationship with a dependent variable (Y ). This chapter goes beyond that and deals with the analysis of situations where we have more than one X (predictor) variable, using a technique called multiple regression.
Zealure C. Holcomb, Keith S. Cox
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Multiple criteria linear regression

European Journal of Operational Research, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Subhash C. Narula, John F. Wellington
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Multiple Linear Regression

2014
This chapter provides an overview of multiple linear regression, a statistical technique that predicts values of a quantitative dependent variable from values of two or more independent variables. By including more than one independent variable, a multiple linear regression can often account for more variability in the dependent variable than can a ...
William H. Holmes, William C. Rinaman
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Regression: multiple linear

International Journal of Injury Control and Safety Promotion, 2018
Simple linear regression models study the relationship between a single continuous dependent variable Y and one independent variable X (Bangdiwala, 2018).
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Multiple Linear Regression

2012
Chapters 13 and 14 examined in detail the simple regression model with one independent variable (such as amount of fertilizer) and one dependent variable (such as yield of corn). In many cases, however, more than one factor can affect the outcome under study. In addition to fertilizer, rainfall and temperature certainly influence the yield of corn.
Cheng-Few Lee, John C. Lee, Alice C. Lee
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