Regression
Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fitzmaurice, Garrett M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Schlagwörter: |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: Levonorgestrel intra-uterine system as a treatment option for complex endometrial hyperplasia - Haoula, Zeina J. ELSEVIER, 2011, the continuously updated review of diagnostic histopathology, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:22 ; year:2016 ; number:7 ; pages:271-278 ; extent:8 |
| Links: |
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| DOI / URN: |
10.1016/j.mpdhp.2016.06.004 |
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ELV024382485 |
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| 520 | |a Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. | ||
| 520 | |a Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. | ||
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10.1016/j.mpdhp.2016.06.004 doi GBVA2016009000004.pica (DE-627)ELV024382485 (ELSEVIER)S1756-2317(16)30062-7 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.92 bkl Fitzmaurice, Garrett M. verfasserin aut Regression 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. generalized linear models Elsevier confounding Elsevier interaction Elsevier linear regression Elsevier prediction Elsevier logistic regression Elsevier Enthalten in Elsevier Haoula, Zeina J. ELSEVIER Levonorgestrel intra-uterine system as a treatment option for complex endometrial hyperplasia 2011 the continuously updated review of diagnostic histopathology Amsterdam [u.a.] (DE-627)ELV007989881 volume:22 year:2016 number:7 pages:271-278 extent:8 https://doi.org/10.1016/j.mpdhp.2016.06.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.92 Gynäkologie VZ AR 22 2016 7 271-278 8 045F 610 |
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10.1016/j.mpdhp.2016.06.004 doi GBVA2016009000004.pica (DE-627)ELV024382485 (ELSEVIER)S1756-2317(16)30062-7 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.92 bkl Fitzmaurice, Garrett M. verfasserin aut Regression 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. generalized linear models Elsevier confounding Elsevier interaction Elsevier linear regression Elsevier prediction Elsevier logistic regression Elsevier Enthalten in Elsevier Haoula, Zeina J. ELSEVIER Levonorgestrel intra-uterine system as a treatment option for complex endometrial hyperplasia 2011 the continuously updated review of diagnostic histopathology Amsterdam [u.a.] (DE-627)ELV007989881 volume:22 year:2016 number:7 pages:271-278 extent:8 https://doi.org/10.1016/j.mpdhp.2016.06.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.92 Gynäkologie VZ AR 22 2016 7 271-278 8 045F 610 |
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10.1016/j.mpdhp.2016.06.004 doi GBVA2016009000004.pica (DE-627)ELV024382485 (ELSEVIER)S1756-2317(16)30062-7 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.92 bkl Fitzmaurice, Garrett M. verfasserin aut Regression 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. generalized linear models Elsevier confounding Elsevier interaction Elsevier linear regression Elsevier prediction Elsevier logistic regression Elsevier Enthalten in Elsevier Haoula, Zeina J. ELSEVIER Levonorgestrel intra-uterine system as a treatment option for complex endometrial hyperplasia 2011 the continuously updated review of diagnostic histopathology Amsterdam [u.a.] (DE-627)ELV007989881 volume:22 year:2016 number:7 pages:271-278 extent:8 https://doi.org/10.1016/j.mpdhp.2016.06.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.92 Gynäkologie VZ AR 22 2016 7 271-278 8 045F 610 |
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10.1016/j.mpdhp.2016.06.004 doi GBVA2016009000004.pica (DE-627)ELV024382485 (ELSEVIER)S1756-2317(16)30062-7 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.92 bkl Fitzmaurice, Garrett M. verfasserin aut Regression 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. generalized linear models Elsevier confounding Elsevier interaction Elsevier linear regression Elsevier prediction Elsevier logistic regression Elsevier Enthalten in Elsevier Haoula, Zeina J. ELSEVIER Levonorgestrel intra-uterine system as a treatment option for complex endometrial hyperplasia 2011 the continuously updated review of diagnostic histopathology Amsterdam [u.a.] (DE-627)ELV007989881 volume:22 year:2016 number:7 pages:271-278 extent:8 https://doi.org/10.1016/j.mpdhp.2016.06.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.92 Gynäkologie VZ AR 22 2016 7 271-278 8 045F 610 |
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10.1016/j.mpdhp.2016.06.004 doi GBVA2016009000004.pica (DE-627)ELV024382485 (ELSEVIER)S1756-2317(16)30062-7 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.92 bkl Fitzmaurice, Garrett M. verfasserin aut Regression 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. generalized linear models Elsevier confounding Elsevier interaction Elsevier linear regression Elsevier prediction Elsevier logistic regression Elsevier Enthalten in Elsevier Haoula, Zeina J. ELSEVIER Levonorgestrel intra-uterine system as a treatment option for complex endometrial hyperplasia 2011 the continuously updated review of diagnostic histopathology Amsterdam [u.a.] (DE-627)ELV007989881 volume:22 year:2016 number:7 pages:271-278 extent:8 https://doi.org/10.1016/j.mpdhp.2016.06.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.92 Gynäkologie VZ AR 22 2016 7 271-278 8 045F 610 |
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Levonorgestrel intra-uterine system as a treatment option for complex endometrial hyperplasia |
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Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. |
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Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. |
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Regression models are widely used for addressing scientific questions of interest regarding the associations among a set of variables. In particular, linear regression models describe how part of the natural individual-to-individual variation in a continuous response variable can be explained by one or more explanatory variables. In this article we provide a general overview of regression concepts, emphasizing the two most common goals of regression analysis: explanation and prediction. We discuss various aspects of interpretation of regression coefficients. We also consider the notions of confounding and interaction within regression analyses. Finally, we consider important generalizations of linear regression to handle the case where the response variable is binary (logistic regression) and also settings with correlated responses (e.g., repeated measurements on individuals over time). We conclude by discussing how linear and logistic regression are special cases of a broad and useful collection of regression models known as generalized linear models. |
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