Correlation Simple correlation; Measurement of a correlation; Correlation coefficients (Pearson product-moment, Spearman’s rho); Significance and correlation coefficients;Variance estimates; SPSS procedures for correlation;What you can’t assume with a correlation; Categorical variables; Common uses of correlation in psychology; Regression and multiple regression; Multiple predictions; Partial and semi-partial correlation; Regression coefficients; Effect size and power; Conducting a regression analysis in SPSS

2013 ◽  
pp. 438-484
Keyword(s):  
Regression Analysis ◽  
Multiple Regression ◽  
Effect Size ◽  
Partial Correlation ◽  
Correlation Coefficients ◽  
Correlation Measurement ◽  
Regression Coefficients ◽  
Categorical Variables ◽  
Simple Correlation ◽  
Variance Estimates

The Relative Importance of Socioeconomic and Political Variables for Public Policy

1977 ◽  
Vol 71 (2) ◽  
pp. 559-566 ◽  
Author(s):  
Michael S. Lewis-Beck
Keyword(s):  
Public Policy ◽  
Path Analysis ◽  
Multiple Regression ◽  
Partial Correlation ◽  
Policy Outcomes ◽  
Statistical Techniques ◽  
Relative Importance ◽  
Simple Correlation ◽  
Independent Variables ◽  
Political Variables

Since Dawson and Robinson, a dominant issue in the quantitative study of public policy has been the relative importance of socioeconomic and political variables for determining policy outcomes. It is argued here that past efforts to resolve this issue have been unsatisfactory, largely because they relied on inadequate statistical techniques, i.e., simple correlation, partial correlation, or multiple regression. Coefficients from these techniques are irrelevant for all but the most peculiar models of public policy. In general, if the researcher wishes to assess the relative importance of independent variables, it will be necessary to resort to path analysis of a formally constructed causal model. The comparison of “effects coefficients,” derived from path analysis, is offered as the preferred means of evaluating independent variables, superior to comparisons of coefficients from simple correlation, partial correlation, or multiple regression. When the effects coefficients are actually calculated for a popular model of welfare policy, socioeconomic variables appear much more important than political variables, contrary to interpretations coming from the more traditional statistical techniques.

scholarly journals Correlation and Multiple Regression Studies of Yield and Yield Contributing Characters in Cauliflower (Brassica oleracea var. Botrytis L.)

2019 ◽  
pp. 1-5
Author(s):  
K. P. Singh ◽  
B. Patel ◽  
Rakesh Kumar ◽  
R. K. Roy ◽  
S. K. Singh
Keyword(s):  
Regression Analysis ◽  
Brassica Oleracea ◽  
Multiple Regression ◽  
Plant Height ◽  
Regression Coefficients

The study on Cauliflower cv. ‘Pusa Dipali’ was carried out to find out the correlation and multiple regression coefficients studies of yield and yield contributing characters. Yield was found to be highly and significantly positively correlated with all the ancillary characters viz, curd depth (0.9180), curd diameter (0.9050), weight of curd (0.8990, plant height (0.8898), weight of plant (0.8768) and plant girth (0.6880). The multiple regression coefficients were found to be non significant due to multi collinearly between the characters. The step wise regression analysis showed that curd depth has highest contribution towards field followed by curd weight, curd diameter and plant height while the lowest contribution was due to plant girth and weight of plant.

scholarly journals Life Cycle Assessment of Seabass (Dicentrarchus labrax) Produced in Offshore Fish Farms: Variability and Multiple Regression Analysis

2019 ◽  
Vol 11 (13) ◽  
pp. 3523 ◽  
Author(s):  
Benjamín García García ◽  
Caridad Rosique Jiménez ◽  
Felipe Aguado-Giménez ◽  
José García García
Keyword(s):  
Regression Analysis ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Environmental Impacts ◽  
Dicentrarchus Labrax ◽  
Regression Coefficients ◽  
Feeding System ◽  
Fish Farms

Equations were developed through multiple regression analysis (MRA) to explain the variability of potential environmental impacts (PEIs) estimated by life cycle assessment (LCA). The case studied refers to the production of seabass in basic offshore fish farms. Contribution analysis showed that the components of the system which most influence the potential environmental impacts are the feed (54% of the overall impact) and the fuel consumed by vessels operating in the farm (23%). Feed and fuel varied widely from one fish farm to another due to different factors, such as the efficiency of the feeding system used in each of them, or the distance from the harbor to the farm. Therefore, a number of scenarios (13) were simulated with different values of both factors and the results of the PEI were fitted by MRA to the model: PEI = a + b × Feed + c × Fuel. For all the PEIs, the regression coefficients were significant (p < 0.05) and R2 was 1. These equations allow us to estimate simply and quickly very different scenarios that reflect the reality of different farms at the present time, but also future scenarios based on the implementation of technologies that will decrease both feed and fuel consumption.

Multivariate Multiple Regression Prediction Models: A Euclidean Distance Approach

2003 ◽  
Vol 92 (3) ◽  
pp. 763-769 ◽  
Author(s):  
Paul W. Mielke ◽  
Kenneth J. Berry
Keyword(s):  
Prediction Model ◽  
Multiple Regression ◽  
Effect Size ◽  
Euclidean Distance ◽  
Prediction Models ◽  
Regression Coefficients ◽  
Multiple Response ◽  
Multivariate Multiple Regression ◽  
Euclidean Distances ◽  
Distance Approach

An extension of a multiple regression prediction model to multiple response variables is presented. An algorithm using least sum of Euclidean distances between the multivariate observed and model-predicted response values provides regression coefficients, a measure of effect size, and inferential procedures for evaluating the extended multivariate multiple regression prediction model.

scholarly journals Standing Height and its Estimation Utilizing Foot Length Measurements in Kosovan Adults: National Survey

2017 ◽  
Vol 6 (2) ◽  
pp. 1-7 ◽  
Author(s):  
Stevo Popovic ◽  
Fitim Arifi ◽  
Dusko Bjelica
Keyword(s):  
Regression Analysis ◽  
Linear Regression Analysis ◽  
Body Height ◽  
Correlation Coefficients ◽  
Percent Confidence Interval ◽  
Simple Correlation ◽  
Standing Height ◽  
Foot Length ◽  
Arm Span ◽  
Length Measurements

The purpose of this research is to examine standing height in both Kosovan genders as well as its association with foot length, as an alternative to estimating standing height. A total of 1623 individuals (830 male and 793 female) participated in this research. The anthropometric measurements were taken according to the protocol of ISAK. The relationships between body height and foot length were determined using simple correlation coefficients at a ninety-five percent confidence interval. A comparison of means of standing height and foot length between genders was performed using a t-test. Then a linear regression analysis was carried out to examine extent to which foot length can reliably predict standing height. Results displayed that Kosovan male are 179.52±5.96cm tall and have a foot length of 26.22±1.19cm, while Kosovan female are 165.72±4.93cm tall and have a foot length of 23.52±1.01cm. The results have shown that both genders made Kosovans a tall nation but not even close to be in top tallest nations. Moreover, the foot length reliably predicts standing height in both genders; but, not reliably enough as arm span.

scholarly journals Correlation and Regression using VASSARSTATS

2016 ◽  
Vol 3 (2) ◽  
Author(s):  
Suma AP ◽  
KP Suresh
Keyword(s):  
Correlation Coefficient ◽  
Partial Correlation ◽  
Rank Order ◽  
Correlation Coefficients ◽  
Regression Coefficients ◽  
Multiple Correlation ◽  
Population Correlation ◽  
Coefficient Corrélation ◽  
Sample Correlation ◽  
Simple Logistic

In a bivariate or a multivariate data, to understand the association between the variables Correlation is the best tool. It gives the degree of relationship between the variables. Regression gives the exact linear relationship between the variables. This article gives details of capabilities of Vassarstats Correlation and Regression and procedure to calculate Correlation coefficient and Regression coefficients with examples. Vassarstats Correlation and Regression can perform Linear Correlation and Regression, Intercorrelations, Multiple Correlation and Regression, Partial Correlation, 0.95 and 0.99 Confidence intervals for population correlation coefficient, Estimating the Population Value of rho, Significance of value of r, Significance of difference between two correlation coefficients, Significance of difference between sample correlation coefficient and hypothetical value of population Correlation coefficient, Rank Order Correlation, Correlation coefficient for a 2*2 contingency table, Point biserial correlation coefficient, Correlation for unordered pairs, and then Simple Logistic Regression.

P0302NEUTROPHIL TO LYMPHOCYTE AND PLATELET TO LYMPHOCYTE RATIO IN PATIENTS UNDERGOING KIDNEY TRANSPLANT

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Sandra Fabiola Velasco Ramirez ◽  
Milagros Melissa Flores Fonseca ◽  
Ana Cristina Ramirez Anguiano ◽  
Martha Rosa Cueto Casilla ◽  
Yolanda Diaz Burke ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Kidney Transplant ◽  
Categorical Variables ◽  
Study Group ◽  
Platelet To Lymphocyte Ratio ◽  
Kidney Allograft ◽  
Lymphocyte Ratio ◽  
Allograft Dysfunction

Abstract Background and Aims Inflammation plays a central role before and after a kidney transplant recipients (KTR). Despite survival improvement, several factors are associated with poor outcomes after transplantation. In search of a cost effective inflammatory marker neutrophil to lymphocyte ratio (NLR) and the platelet to lymphocyte ratio (PLR) have shown a prognostic value. Therefore, the objective of this study was to determine the association of NLR and PLR and acute kidney allograft dysfunction as markers of inflammatory state in KTR. Method A single center, retrospective study. Our study group included 41 KTR with acute kidney allograft dysfunction from our center at Centro Medico Nacional de Occidente, Jalisco, Mexico. NLR and PLR were collected and evaluated 1 month prior KT, at the time, 6 months and one year after transplantation. Statistical analysis: Data were expressed as the mean ± SD, median and range or frequency, as appropriate. Intergroup comparisons were performed with a chi-squared test for categorical variables and Student’s t test or the Mann-Whitney test for continuous variables. Putative associations between clinical factors, biological factors and mortality were assessed in univariate and multivariate Cox models. Statistical analyses were performed using SPSS v26.0 (IBM Corporation, NY, USA); p &lt; 0.05 was considered statistically significant. Results Mean age was 29.54 ± 7.32 years, and 56% were women. Of those patients, all received living donor kidney transplantation. All patients in the study groups received standardized immunosuppressive regimen consisting of calcineurin inhibitors, mycophenolate mofetil and steroids. Median serum creatinine levels after KT were between 1.11 ± 0.36 mg/dl. The median NLR and PLR levels were significantly higher in the study group, with a median of 3.34 (1.83 –5.14) and 8.65 (343.47 – 360.59), not statistically associated. The best set of predictors in multiple regression analysis of higher levels of NLR were serum albumin (r = -0.432; p = 0.007) and C reactive protein (r = 0.641; p = 0.002). The best set of predictors in multiple regression analysis of higher levels of PLR were hematocrit (r = -0.313; p = 0.055) and serum glucose (r = 0.360; p = 0.026). Conclusion Our data showed that higher values of NLR and PLR are associated with inflammatory markers, leading to the conclusion that this finding must be confirmed with larger, prospective and controlled follow up.

scholarly journals Categorical Variables in Regression Analysis: A Comparison of Dummy and Effect Coding

2012 ◽  
Vol 4 (2) ◽  
pp. 202 ◽  
Author(s):  
Hussain Alkharusi
Keyword(s):  
Regression Analysis ◽  
Group Membership ◽  
Regression Coefficients ◽  
Categorical Variables ◽  
Sample Sizes ◽  
Independent Variables ◽  
Coding Method ◽  
Dependent Variables

The use of categorical variables in regression involves the application of coding methods. The purpose of this paper is to describe how categorical independent variables can be incorporated into regression by virtue of two coding methods: dummy and effect coding. The paper discusses the uses, interpretations, and underlying assumptions of each method. In general, overall results of the regression are unaffected by the methods used for coding the categorical independent variables. In any of the methods, the analysis tests whether group membership is related to the dependent variables. Both methods yield identical R2 and F. However, the interpretations of the intercept and regression coefficients depend on what coding method has been applied and whether the groups have equal sample sizes.

Sign in / Sign up

Export Citation Format

Share Document