Multiple Regression: Using Two or More Predictor Variables

2013 ◽  
pp. 133-188
Keyword(s):  
Multiple Regression ◽  
Predictor Variables

Sexual Satisfaction among Married Women Age 50 and Older

2000 ◽  
Vol 86 (3_part_2) ◽  
pp. 1107-1122
Author(s):  
Michael Young ◽  
George Denny ◽  
Tamera Young ◽  
Raffy Luquis
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Sexual Satisfaction ◽  
Married Women ◽  
Return Rate ◽  
Predictor Variables ◽  
Hierarchical Multiple Regression ◽  
Item Questionnaire ◽  
Stratified Sample

Correlates of sexual satisfaction were identified in a sample of older married women. A 70-item questionnaire was mailed to an age-stratified sample of 5,000 married persons, including 1,000 married women over the age of 50. Usable questionnaires were received from 148 participants (14.8% return rate). Hierarchical multiple regression analysis, using sexual satisfaction as the dependent variable, yielded five predictor variables that accounted for a significant portion of the variation in sexual satisfaction (Cumulative R2 = .73). These results serve as a reminder that sexual interactions cannot be compartmentalized but must be considered within the context of the overall marriage relationship. Given the low return rate, interpretations should be limited until replication with an adequate sample has been completed.

scholarly journals Forecasting Students’ Final Exam: Results Using Multiple Regression Analysis in an Undergraduate Business Statistics Course

2021 ◽  
pp. 30-40
Author(s):  
Gunawardena Egodawatte
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Multiple Regression Model ◽  
Predictor Variables ◽  
Teaching Tool ◽  
Final Examination ◽  
Final Exam ◽  
Final Model ◽  
Business Statistics ◽  
Statistics Course

This paper discusses the development of a multiple regression model to predict the final examination marks of students in an undergraduate business statistics course. The marks of a sample of 366 students in the Winter 2017 semester were used to fit the regression model. The final model contained three predictor variables namely two test marks and the homework assignment mark. The marks of another 194 students from Winter 2018 were used to validate the model. The model validation showed that it can be used for future cohorts of students for prediction. The two main objectives of the study were to use the model as a teaching tool in class and to use the model to predict final examination marks of future students.

Negative Consequences of Dichotomizing Continuous Predictor Variables

2003 ◽  
Vol 40 (3) ◽  
pp. 366-371 ◽  
Author(s):  
Julie R. Irwin ◽  
Gary H. McClelland
Keyword(s):  
Data Analysis ◽  
Multiple Regression ◽  
Model Specification ◽  
Predictor Variables ◽  
Negative Consequences ◽  
Nonnormal Distributions ◽  
Median Split ◽  
Continuous Predictor

Marketing researchers frequently split (dichotomize) continuous predictor variables into two groups, as with a median split, before performing data analysis. The practice is prevalent, but its effects are not well understood. In this article, the authors present historical results on the effects of dichotomization of normal predictor variables rederived in a regression context that may be more relevant to marketing researchers. The authors then present new results on the effect of dichotomizing continuous predictor variables with various nonnormal distributions and examine the effects of dichotomization on model specification and fit in multiple regression. The authors conclude that dichotomization has only negative consequences and should be avoided.

Sexual Satisfaction among Married Women Age 50 and Older

2000 ◽  
Vol 86 (3_suppl) ◽  
pp. 1107-1122 ◽  
Author(s):  
Michael Young ◽  
George Denny ◽  
Tamera Young ◽  
Raffy Luquis
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Sexual Satisfaction ◽  
Married Women ◽  
Return Rate ◽  
Predictor Variables ◽  
Hierarchical Multiple Regression ◽  
Item Questionnaire ◽  
Stratified Sample

Correlates of sexual satisfaction were identified in a sample of older married women. A 70-item questionnaire was mailed to an age-stratified sample of 5,000 married persons, including 1,000 married women over the age of 50. Usable questionnaires were received from 148 participants (14.8% return rate) Hierarchical multiple regression analysis, using sexual satisfaction as the dependent variable, yielded five predictor variables that accounted for a significant portion of the variation in sexual satisfaction (Cumulative R2 = .73). These results serve as a reminder that sexual interactions cannot be compartmentalized but must be considered within the context of the overall marriage relationship. Given the low return rate, interpretations should be limited until replication with an adequate sample has been completed.

scholarly journals Evaluation and reparametrization of mathematical models for prediction of the leaf area of Megathyrsus maximus cv. BRS Zuri

2020 ◽  
Vol 8 (3) ◽  
pp. 214-219
Author(s):  
Patrick Bezerra Fernandes ◽  
Rodrigo Amorim Barbosa ◽  
Maria Da Graça Morais ◽  
Cauby De Medeiros-Neto ◽  
Antonio Leandro Chaves Gurgel ◽  
...  
Keyword(s):  
Regression Model ◽  
Leaf Area ◽  
Multiple Regression ◽  
Leaf Blade ◽  
Multiple Regression Model ◽  
Leaf Length ◽  
Predictor Variables ◽  
Mato Grosso ◽  
Linear Multiple Regression ◽  
Model C

The aim of this study was to verify the precision and accuracy of 5 models for leaf area prediction using length and width of leaf blades of Megathyrsus maximus cv. BRS Zuri and to reparametrize models. Data for the predictor variables, length (L) and width (W) of leaf blades of BRS Zuri grass tillers, were collected in May 2018 in the experimental area of Embrapa Gado de Corte, Mato Grosso do Sul, Brazil. The predictor variables had high correlation values (P<0.001). In the analysis of adequacy of the models, the first-degree models that use leaf blade length (Model A), leaf width × leaf length (Model B) and linear multiple regression (Model C) promoted estimated values similar to the leaf area values observed (P>0.05), with high values for determination coefficient (>80%) and correlation concordance coefficient (>90%). Among the 5 models evaluated, the linear multiple regression (Model C: β0 = -5.97, β1 = 0.489, β2 = 1.11 and β3 = 0.351; R² = 89.64; P<0.001) and as predictor variables, width, length and length × width of the leaf blade, are the most adequate to generate precise and exact estimates of the leaf area of BRS Zuri grass.

scholarly journals rdacca.hp: an R package for generalizing hierarchical and variation partitioning in multiple regression and canonical analysis

2021 ◽  
Author(s):  
Jiangshan Lai ◽  
Yi Zou ◽  
Jinlong Zhang ◽  
Pedro Peres-Neto
Keyword(s):  
Multiple Regression ◽  
Variation Partitioning ◽  
R Package ◽  
Canonical Analysis ◽  
Predictor Variables ◽  
List Type ◽  
Multiple Response ◽  
Hierarchical Partitioning ◽  
Commonality Analysis ◽  
Single Response

SummaryCanonical analysis, a generalization of multiple regression to multiple response variables, is widely used in ecology. Because these models often involve large amounts of parameters (one slope per response per predictor), they pose challenges to model interpretation. Currently, multi-response canonical analysis is constrained by two major challenges. Firstly, we lack quantitative frameworks for estimating the overall importance of single predictors. Secondly, although the commonly used variation partitioning framework to estimate the importance of groups of multiple predictors can be used to estimate the importance of single predictors, it is currently computationally constrained to a maximum of four predictor matrices.We established that commonality analysis and hierarchical partitioning, widely used for both estimating predictor importance and improving the interpretation of single-response regression models, are related and complementary frameworks that can be expanded for the analysis of multiple-response models.In this application, we aim at: a) demonstrating the mathematical links between commonality analysis, variation and hierarchical partitioning; b) generalizing these frameworks to allow the analysis of any number of responses, predictor variables or groups of predictor variables in the case of variation partitioning; and c) introducing and demonstrating the usage of the R package rdacca.hp that implements these generalized frameworks.

The Importance of Structure Coefficients in Regression Research

1985 ◽  
Vol 45 (2) ◽  
pp. 203-209 ◽  
Author(s):  
Bruce Thompson ◽  
Gloria M. Borrello
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Experimental Research ◽  
Predictor Variables

Multiple regression analysis is frequently and increasingly being employed in both experimental and non-experimental research. However, when data include predictor variables that are correlated, either unavoidably or due to conscious design choices, some regression results can become difficult to interpret. The paper presents an actual study to provide a heuristic demonstration that structure coefficients may be helpful in these cases.

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