Results 201 to 210 of about 929,368 (246)
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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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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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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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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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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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Journal of Quality Technology, 1971
(1971). Multiple Linear Regression. Journal of Quality Technology: Vol. 3, No. 4, pp. 184-189.
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(1971). Multiple Linear Regression. Journal of Quality Technology: Vol. 3, No. 4, pp. 184-189.
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1991
The multiple linear regression model is the most commonly applied statistical technique for relating a set of two or more variables. In Chapter 3 the concept of a regression model was introduced to study the relationship between two quantitative variables X and Y.
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The multiple linear regression model is the most commonly applied statistical technique for relating a set of two or more variables. In Chapter 3 the concept of a regression model was introduced to study the relationship between two quantitative variables X and Y.
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1987
The multiple linear regression model describes the relationship between a response variable y and a number of explanatory variables x1,…xp,p≥2. A multiple regression analysis is an analysis of a data set composed of n cases (yi,xi1,…xip), i=1,…,n, where each case consists of joint observations of the response variable and the explanatory variables as ...
Erling B. Andersen +2 more
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The multiple linear regression model describes the relationship between a response variable y and a number of explanatory variables x1,…xp,p≥2. A multiple regression analysis is an analysis of a data set composed of n cases (yi,xi1,…xip), i=1,…,n, where each case consists of joint observations of the response variable and the explanatory variables as ...
Erling B. Andersen +2 more
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