Results 221 to 230 of about 2,203,514 (272)
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2011
The main purpose of this chapter is to predict the value of some variable Y based on a given set of variables X i where i = 1, 2, …, p − 1 and p is an integer larger than or equal to 2. The following example will be examined in detail throughout this chapter.
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The main purpose of this chapter is to predict the value of some variable Y based on a given set of variables X i where i = 1, 2, …, p − 1 and p is an integer larger than or equal to 2. The following example will be examined in detail throughout this chapter.
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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.
Paul D. Berger +2 more
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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.
Paul D. Berger +2 more
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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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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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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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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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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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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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2007
This chapter describes multiple linear regression, a statistical approach used to describe the simultaneous associations of several variables with one continuous outcome. Important steps in using this approach include estimation and inference, variable selection in model building, and assessing model fit.
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This chapter describes multiple linear regression, a statistical approach used to describe the simultaneous associations of several variables with one continuous outcome. Important steps in using this approach include estimation and inference, variable selection in model building, and assessing model fit.
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2017
Sie lernen in Erweiterung der Ihnen bekannten einfachen linearen Regression, wie man aus den Daten einer Stichprobe eine lineare funktionale Beziehung zwischen einem abhangigen Merkmal und mehreren unabhangigen Merkmalen gewinnt. Sie haben verstanden, dass die grundsatzlichen Uberlegungen ganz weitgehend analog zum Fall der einfachen Regression sind ...
Thomas Schuster, Arndt Liesen
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Sie lernen in Erweiterung der Ihnen bekannten einfachen linearen Regression, wie man aus den Daten einer Stichprobe eine lineare funktionale Beziehung zwischen einem abhangigen Merkmal und mehreren unabhangigen Merkmalen gewinnt. Sie haben verstanden, dass die grundsatzlichen Uberlegungen ganz weitgehend analog zum Fall der einfachen Regression sind ...
Thomas Schuster, Arndt Liesen
openaire +1 more source