Estimation Of Correlation Coefficient Research Articles

Comparison of dietary intake measured by a web-based FFQ and repeated 24-hour dietary recalls: the Hordaland Health Study.

All dietary assessment methods inevitably introduce measurement errors, which should ideally be considered during data analysis and interpretation. Methodological studies should be conducted to address how well a given assessment method captures dietary intake and to highlight the extent and direction of the measurement error. Within a subgroup of the Hordaland Health Study (HUSK3), we examined the relative validity of a web-based food frequency questionnaire (WebFFQ) by comparing its estimates of mean daily intake of nutrients and foods with estimated mean daily intakes from repeated administrations of 24-hour dietary recall interviews (24-HDRs). Men and women born between 1950 and 1951 were recruited from HUSK3. The participants (n = 67) completed a WebFFQ and three non-consecutive 24-HDRs over the course of a year. Relative validity was assessed using Spearman's rank correlation, crosstab analysis and Bland-Altman plots. Linear regression models were used to compute the calibration coefficients. The estimated correlation coefficients were acceptable or strong for all nutrients and foods except iodine (rs = 0⋅19). The highest correlation coefficient was found for juice (rs = 0⋅71), whereas the lowest correlation coefficient was found for iodine (rs = 0⋅19). Cross-classification by quartiles categorised more than 72 % of the participants into the same or adjacent quartiles using the two methods. Few data points fell outside the limits of agreement in the Bland-Altman plots. Calibration coefficients ranged from 0⋅10 (wholegrain) to 0⋅81 (alcohol). Our findings suggest that the WebFFQ has reasonable ranking abilities for all the included nutrients and foods, except for iodine.

Selection of Conilon coffee clones for the Zona da Mata region of Minas Gerais, Brazil

Recently, the cultivation of Coffea canephora species has expanded as a consequence of climate change effects and emerging markets that use this coffee as raw material. To meet such demands of production, it is necessary to also improve yield in cultivated areas. Hence, this study aimed to evaluate Conilon coffee clones to identify the most promising fitting material for the Zona da Mata region of Minas Gerais, as well as to analyze the effect of the genotype x biennium interaction and estimate correlation coefficients among evaluated characteristics. Vegetative vigor, yield, main pests and diseases were evaluated in 36 clones from Incaper, ES. The statistical analyzes considered the averages of each biennium (2005/2006 and 2006/2007; 2007/2008 and 2008/2009) and also the average of three biennia concomitantly. Univariate and joint statistical analyzes were performed, as well as estimates of phenotypic, genotypic and environmental correlation coefficients among assessed characteristics. A wide variability among clones was observed for most of the evaluated characteristics for both each biennium and the average of three biennia. The severities of most diseases have reduced over the years of cultivation. The genotypic correlation coefficients have overcome phenotypic and environmental ones. In general, pests and diseases severities showed significant and negative correlations with vegetative vigor and yield. On the other hand, the last two characteristics were positively correlated. It was also evidenced a certain diversity among clones for most of the evaluated traits. Clone x biennium interaction was significant only for yield and leaf rust severity. There are negative correlations among the characteristics associated with yield and pests and diseases occurrences, which make it possible the use of yield variable as a selection parameter. Clones 24, 23, 28, 26, 21, 29 and 06 MG/ES present a higher potential for cultivation in the Zona da Mata of Minas Gerais. Key words: Breeding; coffea canephora; correlations; genotype-environment interaction.

Correlation between Transcutaneous Bilirubinemia and Blood Bilirubinemia in Screening Term Newborn for Neonatal Jaundice at the Essos Hospital Centre (EHC), Yaoundé, Cameroon

Background: Early and non-invasive diagnosis of neonatal hyperbilirubinemia remains critical in dark skinned babies of low resource settings. Objective: To assess correlation/agreement between transcutaneous bilirubin (Tcb) and serum bilirubin (Tsb) values in full term neonates with jaundice. Methodology: An analytical cross-sectional study was conducted at the neonatology unit of the Essos Hospital Centre (EHC) from January to June 2019. All full-term neonates aged 0 to 7 days with suspected jaundice who did not receive phototherapy were eligible for the study. The enrolled neonates in the study were assessed clinically, then with the MBJ20 transcutaneous bilirubinometer (TcB). The MBJ20 transcutaneous bilirubinometer highest measurement over the forehead and the sternum were compared to TsB. Data were entered and then analysed with the CsPro7.2 and R (version 3.6.0) software. Correlation was captured by Bland & Alman plots and Concordance Correlation Coefficient (CCC) estimates. The Pearson correlation coefficient and Student test for paired data were used for descriptions purposes, and the significance level was 5%. Results: We recruited 88 neonates. The sex ratio of the babies included was 1.25 favouring males. Median Post-natal age was 3 days with 62% aged 72 hours or more. The mean TcB corresponding to the maximum average between frontal and sternal measurement was 153 mg/dl ± 48 and the average Tsb was 123.80 mg/dl ± 50.48. A good linear correlation was found between TcB and total serum bilirubin level r = 0.86 [0.80; 0.91]. Positive correlation was noted between both (forehead and sternum) TcB measurements sites, namely r = 0.78 and r = 0.86. The Bland & Altman plot measured the bias at -29.68 mg/l (confidence interval at 95%, 21.14 - 80.50). The CCC estimate was 0.2 varying from -0.22 to 0.76 according to TcB measurement threshold and post-natal age. The ROC area under the curve value for a threshold Conclusion: A good linear correlation was found despite a poor agreement between TcB and Tsb. TcB method systematically overestimated the value of TsB.

Relationship between the Characteristics of Bread Wheat Grains, Storage Time and Germination.

Seed storage is important to farmers, breeders and for germplasm preservation. During storage, seeds accumulate damage at the structural and metabolic level, which disrupt their function and reduce resistance to adverse external conditions. In this regard, issues related to seed aging prove to be relevant for maintaining the viability of genetic collections. We analyzed morphological characteristics of grains and their coat color for 44 recombinant inbred lines (RILs) of bread wheat grown in four different seasons, 2003, 2004, 2009 and 2014. Our investigations were performed in 2020. For 19 RILs from the same seasons germination was evaluated. Our results demonstrate that genotype significantly affects the variability of all seed traits, and the year of harvesting affects about 80% of them (including all the traits of shape and size). To identify the trend between changes in grain characteristics and harvesting year, we estimated correlation coefficients between them. No significant trend was detected for the grain shape/size traits, while 90% of the color traits demonstrated such a trend. The most significant negative correlations were found between the harvesting year and the traits of grain redness: the greater the storage time, the more intensive is red color component for the grains. At the same time, it was shown that grains of longer storage time (earlier harvesting year) have lighter coat. Analysis of linear correlations between germination of wheat seeds of different genotypes and harvesting years and their seed traits revealed a negative linear relationship between the red component of coat color and germination: the redder the grains, the lower their germination rate. The results obtained demonstrate manifestations of metabolic changes in the coat of grains associated with storage time and their relationship with a decrease of seed viability.

A Fuzzy Multi-Criteria Model for Municipal Waste Treatment Systems Evaluation including Energy Recovery

One of the recent problems on waste sorting systems is their performance evaluation for proper decision making and management. For this purpose, multi-criteria methods can be used to evaluate the sorting system from both operational and financial perspectives. According to a recent literature review, there are no solutions for evaluating waste sorting systems that take into account: sorting point utilisation, sorting efficiency, waste stream irregularity, and technical system availability. In addition, the problem of data uncertainty and the need to use expert judgements indicate the need for the implementation of methods adjusted to the qualitative and quantitative assessment, such as the fuzzy approach. Following this, in order to overcome the presented limitations, the authors introduced the new assessment method for waste sorting systems based on multi-criteria model implementation and fuzzy theory use. Therefore, the developed model was based on a hierarchical fuzzy logic model for which appropriate membership function parameters and inference rules were defined. The specificity of the chosen assessment criteria and their justification was provided. The model has been implemented to evaluate one of the waste sorting plants in Wroclaw, Poland. Tests have been conducted for seven different configurations of waste sorting lines (with variable input parameters). The study focuses on analysing the amount of selected waste at each station in relation to the total stream size of each fraction. Efficiency was measured by the mass of the collected waste and the number of pieces of waste in each fraction. Based on the obtained results, estimations of particular parameters of the model were made, and the results were presented and commented on. It was shown that there is a significant relationship between the level of system evaluation and sorting efficiency and an inverse relationship with the level of RDF obtained. The analysis was based on Pearson’s linear correlation coefficient estimation and linear regression implementation.

Concordance correlation coefficients estimated by modified variance components and generalized estimating equations for longitudinal overdispersed Poisson data.

For longitudinal overdispersed Poisson data sets, estimators of the intra-, inter-, and total concordance correlation coefficient through variance components have been proposed. However, biased estimators of quadratic forms are used in concordance correlation coefficient estimation. In addition, the generalized estimating equations approach has been used in estimating agreement for longitudinal normal data and not for longitudinal overdispersed Poisson data. Therefore, this paper proposes a modified variance component approach to develop the unbiased estimators of the concordance correlation coefficient for longitudinal overdispersed Poisson data. Further, the indices of intra-, inter-, and total agreement through generalized estimating equations are also developed considering the correlation structure of longitudinal count repeated measurements. Simulation studies are conducted to compare the performance of the modified variance component and generalized estimating equation approaches for longitudinal Poisson and overdispersed Poisson data sets. An application of corticospinal diffusion tensor tractography study is used for illustration. In conclusion, the modified variance component approach performs outstandingly well with small mean square errors and nominal 95% coverage rates. The generalized estimating equation approach provides in model assumption flexibility of correlation structures for repeated measurements to produce satisfactory concordance correlation coefficient estimation results.

Methods of Estimating Correlation Coefficients in the Presence of Influential Outlier(s)

Correlation methods are indispensable in the study of the linear relationship between two variables. However, many researchers often adopt inappropriate correlation methods in the study of linear relationships which usually leads to unreliable results. Recurrently, most researchers ignorantly employ the Pearson method in a dataset that contained outliers, instead of more appropriate correlation methods such as Spearman, Kendall Tau, Median and Quadrant which might be suitable in the calculation of correlation coefficient in the presence of influential outliers. It is noted that the accuracy of estimation of correlation coefficients under outliers has been a long-standing problem for methodological researchers. This is due to low knowledge of correlation methods and their assumptions which have led to inappropriate application of correlation methods in research analysis. Five different methods of estimating correlation coefficients in the presence of influential outlier (contaminated data) were considered: Pearson Correlation Coefficient, Spearman Correlation Coefficient, Kendall Tau Correlation Coefficient, Median Correlation Coefficient and Quadrant Correlation Coefficient.

Latent Gaussian copula models for longitudinal binary data

Longitudinal binary data arise commonly in a variety of fields including public health, biomedicine, finance, agriculture and social science, among many others. In longitudinal binary studies, the aims are to assess the association of longitudinal binary response with certain covariates of interest, and to quantify the within-subject correlations for longitudinal binary responses. In the literature, various methods were developed to model longitudinal binary data but little work was done to account for the fact that the correlation coefficients of correlated binary responses have the so-called Fréchet–Hoeffding bounds. Ignoring this fact can lead to incorrect statistical inferences for longitudinal binary data. In this paper, based on latent Gaussian copula a new statistical modeling method is proposed to model the mean and within-subject correlation structures, simultaneously, for longitudinal binary data. Specifically, the mean structure is modeled by a semiparametric regression model, and the within-subject correlation coefficients are modeled through introducing a latent Gaussian copula model with certain latent correlation structures characterized by some parameters. Generalized estimating equations are then proposed to estimate the parameters in the mean and latent correlation structures, and consistency and asymptotic normality of the resulting parameter estimators are established. The proposed model and method ensure that the estimated correlation coefficients must satisfy the Fréchet–Hoeffding bounds for longitudinal binary data. Simulation studies show that the proposed method has a stable numerical performance. A practical data set is analyzed using the proposed method for illustration.

Four Decades of Intergenerational Educational Mobility in Sub-Saharan Africa

Using nationally representative survey data from 34 countries, we assess the extent of intergenerational educational mobility for three-quarters of Africa’s population, over four decades and by gender. We employ both transition probability matrices and regression-based estimates of intergenerational elasticities and correlation coefficients. We find that the educational status of parents is a strong determinant of their children’s educational outcomes, but the strength of this link has diminished in Africa between 1960 and 1999, particularly since the 1980s. We also find that there are considerable differences between countries and by gender. Those in the Southern and Central African region and those with historical links to former British colonies experience relatively higher intergenerational mobility in education. Intergenerational educational mobility is less pronounced for daughters than for sons and mother’s education is generally more strongly associated with children’s educational attainment than fathers’ education.

Ground Motion Correlations from Recorded Mexican Intermediate-depth, Intraslab Earthquakes

ABSTRACT Predictive models to estimate correlation coefficients between peak ground acceleration, peak ground velocity, and spectral acceleration residuals for Mexican intermediate-depth, intraslab earthquakes recorded on rock sites are presented in this study. The models were developed for the total, between-events, and within-event residuals using a recent ground-motion prediction equation (GMPE) and an updated ground-motion database. The results support the use of region- and mechanism-specific correlation models for the subduction intraslab zone of Mexico. An example of the application of the proposed correlation coefficient models consisting in evaluating a conditional mean spectrum (CMS), based on the Mexican seismic design normative, is presented.

Estimation of correlation coefficient under a linear multiplicative distortion measurement errors model

This paper studies the estimation of correlation coefficient between unobserved variables of interest. These unobservable variables are distorted in a new multiplicative fashion by an observed confounding variable, namely, a parametric distortion measurement errors model. The least squares estimation and weighted least squares estimation are used to estimate parameters in the multiplicative distortion functions. An interesting finding is that the calibrated variables obtained by using the weighted least squares estimators perform asymptotic equivalent to the calibrated variables obtained from the nonparametric kernel smoothing estimators in the literature. We then used the calibrated variables to estimate the correlation coefficient, and show that the proposed estimators of correlation coefficient are all asymptotically efficient. Moreover, we suggest an asymptotic normal approximation and an empirical likelihood-based statistic to construct the confidence intervals. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimators. These methods are applied to analyze two real datasets for an illustration.

Application of Fourier Transform infrared spectroscopy (FTIR) coupled with multivariate regression for calcium carbonate (CaCO3) quantification in cement

Partial least squares (PLS) regression models were developed to quantify CaCO3 in cement and to study the CO2 effect on the material matrix. PLS results presented good correlation coefficient (R2 = 0.9995) and low estimation error (RMSEP = 3.61 mg CaCO3/g cement). From the results, it was observed that the portlandite consumption, the increase in CaCO3 content and the C-S-H decalcification-polymerization are the most relevant cement chemical transformations. Thus, it was concluded that: i) it is possible to obtain fast, low-cost, and reliable models to quantify CaCO3 by FTIR and ii) the method is applicable to study carbonated cement-based materials.

Estimation of variability, correlation and path coefficient in mungbean [Vigna radiata (L.) Wilczek] genotypes for seed yield and its attributing characters

The research was conducted at S.D.A.U. during kharif-2019 to study variability, heritability, genetic advance, correlation and path coefficient in a set of thirty genotypes of mungbean [Vigna radiata (L.) Wilczek]. Randomized block design was used with four replications. Ten traits showed significance differences in analysis of variance. Majority traits showed higher genotypic variance than their relevant environmental variance. PCV values were closer to GCV for most of the characters. Seed yield per plant and number of branches per plant had highest GCV. Number of branches per plant, seed yield per plant and100-seed weight expressed high genetic advance and high heritability. Seed yield per plant gave highly significant and positive correlations with number of pods per plant, pod length, 100-seed weight, while highly significant and negative with days to flowering. Highest positive direct effect on seed yield was noticed by 100-seed weight followed by number of pods per plant and days to flowering. Negative direct effect on seed yield was found with plant height followed by protein content. In conclusion, 100-seed weight, number of pods per plant, number of seeds per pod, pod length and number of branches per plant were found key contributors for seed yield.

Improving the asymmetric stochastic volatility model with ex-post volatility: the identification of the asymmetry

Simulation studies show that the asymmetry stochastic volatility (ASV) models may infer erroneous correlation coefficients, due to their predetermined return-volatility specification. We propose identifying the correlation parameter by incorporating the ex-post volatility in the ASV framework. We obtain a significantly smaller magnitude in the estimated correlation coefficients between equity and volatility processes among major U.S. equity market indexes. Out-of-sample index return distribution forecasts demonstrate superior performance when jointly estimating the return and the ex-post volatility processes. The corrected return-volatility correlations by estimating proposed ASV models with subsample data further document the time-varying leverage effect.

Impact of complex, partially nested clustering in a three-arm individually randomized group treatment trial: A case study with the wHOPE trial.

When participants in individually randomized group treatment trials are treated by multiple clinicians or in multiple group treatment sessions throughout the trial, this induces partially nested clusters which can affect the power of a trial. We investigate this issue in the Whole Health Options and Pain Education trial, a three-arm pragmatic, individually randomized clinical trial. We evaluate whether partial clusters due to multiple visits delivered by different clinicians in the Whole Health Team arm and dynamic participant groups due to changing group leaders and/or participants across treatment sessions during treatment delivery in the Primary Care Group Education arm may impact the power of the trial. We also present a Bayesian approach to estimate the intraclass correlation coefficients. We present statistical models for each treatment arm of Whole Health Options and Pain Education trial in which power is estimated under different intraclass correlation coefficients and mapping matrices between participants and clinicians or treatment sessions. Power calculations are based on pairwise comparisons. In practice, sample size calculations depend on estimates of the intraclass correlation coefficients at the treatment sessions and clinician levels. To accommodate such complexities, we present a Bayesian framework for the estimation of intraclass correlation coefficients under different participant-to-session and participant-to-clinician mapping scenarios. We simulated continuous outcome data based on various clinical scenarios in Whole Health Options and Pain Education trial using a range of intraclass correlation coefficients and mapping matrices and used Gibbs samplers with conjugate priors to obtain posteriors of the intraclass correlation coefficients under those different scenarios. Posterior means and medians and their biases are calculated for the intraclass correlation coefficients to evaluate the operating characteristics of the Bayesian intraclass correlation coefficient estimators. Power for Whole Health Team versus Primary Care Group Education is sensitive to the intraclass correlation coefficient in the Whole Health Team arm. In these two arms, an increased number of clinicians, more evenly distributed workload of clinicians, or more homogeneous treatment group sizes leads to increased power. Our simulation study for the intraclass correlation coefficient estimation indicates that the posterior mean intraclass correlation coefficient estimator has less bias when the true intraclass correlation coefficients are large (i.e. 0.10), but when the intraclass correlation coefficient is small (i.e. 0.01), the posterior median intraclass correlation coefficient estimator is less biased. Knowledge of intraclass correlation coefficients and the structure of clustering are critical to the design of individually randomized group treatment trials with partially nested clusters. We demonstrate that the intraclass correlation coefficient of the Whole Health Team arm can affect power in the Whole Health Options and Pain Education trial. A Bayesian approach provides a flexible procedure for estimating the intraclass correlation coefficients under complex scenarios. More work is needed to educate the research community about the individually randomized group treatment design and encourage publication of intraclass correlation coefficients to help inform future trial designs.

Intracluster correlation coefficients in a large cluster randomized vaccine trial in schools: Transmission and impact of shared characteristics.

Cluster randomized trials (cRCT) to assess vaccine effectiveness incorporate indirect effects of vaccination, helping to inform vaccination policy. To calculate the sample size for a cRCT, an estimate of the intracluster correlation coefficient (ICC) is required. For infectious diseases, shared characteristics and social mixing behaviours may increase susceptibility and exposure, promote transmission and be a source of clustering. We present ICCs from a school-based cRCT assessing the effectiveness of a meningococcal B vaccine (Bexsero, GlaxoSmithKline) on reducing oropharyngeal carriage of Neisseria meningitidis (Nm) in 34,489 adolescents from 237 schools in South Australia in 2017/2018. We also explore the contribution of shared behaviours and characteristics to these ICCs. The ICC for carriage of disease-causing Nm genogroups (primary outcome) pre-vaccination was 0.004 (95% CI: 0.002, 0.007) and for all Nm was 0.007 (95%CI: 0.004, 0.011). Adjustment for social behaviours and personal characteristics reduced the ICC for carriage of disease-causing and all Nm genogroups by 25% (to 0.003) and 43% (to 0.004), respectively. ICCs are also reported for risk factors here, which may be outcomes in future research. Higher ICCs were observed for susceptibility and/or exposure variables related to Nm carriage (having a cold, spending ≥1 night out socializing or kissing ≥1 person in the previous week). In metropolitan areas, nights out socializing was a highly correlated behaviour. By contrast, smoking was a highly correlated behaviour in rural areas. A practical example to inform future cRCT sample size estimates is provided.

Correlation and Path Coefficient Analysis in Cluster Bean [Cyamopsis tetragonoloba (L.) Taub.] Genotypes

Background: Climate change is a major challenge faced by the agricultural sector all over the world. Different adaptation and mitigation strategies are used to cope with these weather aberrations. Growing climate resilient crops and cultivars suitable for different regions is one of the adaptation strategy. Leguminous crops are well known for their resilience, adaptation and tolerance to adverse conditions, among these crops cluster bean is a hardy and drought tolerant crop extremely suitable for warm tropical regions. Hence identification and improvement of selected genotypes of cluster bean have paramount importance for combating the challenges possessed by the changing climatic scenario. Methods: Thirty accessions of cluster bean were evaluated during 2018. The experiment was laid out in randomized block design with two replications. Statistical analysis was employed for estimation of correlation and path coefficients. Result: The correlation analysis revealed that pod yield/plant had positive significant correlation with number of branches, number of pod clusters/plant, number of pods/plant and days to first harvest hence, selection of accessions having higher mean values for these traits could improve yield in cluster bean. Path analysis revealed that number of pods/plant had maximum positive direct effect on pod yield/plant followed by number of branches and plant height. Thus, while attempting selection for higher pod yield in cluster bean, number of pods per plant, number of branches and plant height have to be taken into account.

Evaluation of the Validity and Reliability of Connected Insoles to Measure Gait Parameters in Healthy Adults.

The continuous, accurate and reliable estimation of gait parameters as a measure of mobility is essential to assess the loss of functional capacity related to the progression of disease. Connected insoles are suitable wearable devices which allow precise, continuous, remote and passive gait assessment. The data of 25 healthy volunteers aged 20 to 77 years were analysed in the study to validate gait parameters (stride length, velocity, stance, swing, step and single support durations and cadence) measured by FeetMe® insoles against the GAITRite® mat reference. The mean values and the values of variability were calculated per subject for GAITRite® and insoles. A t-test and Levene’s test were used to compare the gait parameters for means and variances, respectively, obtained for both devices. Additionally, measures of bias, standard deviation of differences, Pearson’s correlation and intraclass correlation were analysed to explore overall agreement between the two devices. No significant differences in mean and variance between the two devices were detected. Pearson’s correlation coefficients of averaged gait estimates were higher than 0.98 and 0.8, respectively, for unipedal and bipedal gait parameters, supporting a high level of agreement between the two devices. The connected insoles are therefore a device equivalent to GAITRite® to estimate the mean and variability of gait parameters.

Penalized joint generalized estimating equations for longitudinal binary data.

In statistical research, variable selection and feature extraction are a typical issue. Variable selection in linear models has been fully developed, while it has received relatively little attention for longitudinal data. Since a longitudinal study involves within-subject correlations, the likelihood function of discrete longitudinal responses generally cannot be expressed in analytically closed form, and standard variable selection methods cannot be directly applied. As an alternative, the penalized generalized estimating equation (PGEE) is helpful but very likely results in incorrect variable selection if the working correlation matrix is misspecified. In many circumstances, the within-subject correlations are of interest and need to be modeled together with the mean. For longitudinal binary data, it becomes more challenging because the within-subject correlation coefficients have the so-called Fréchet-Hoeffding upper bound. In this paper, we proposed smoothly clipped absolute deviation (SCAD)-based and least absolute shrinkage and selection operator (LASSO)-based penalized joint generalized estimating equation (PJGEE) methods to simultaneously model the mean and correlations for longitudinal binary data, together with variable selection in the mean model. The estimated correlation coefficients satisfy the upper bound constraints. Simulation studies under different scenarios are made to assess the performance of the proposed method. Compared to existing PGEE methods that specify a working correlation matrix for longitudinal binary data, the proposed PJGEE method works much better in terms of variable selection consistency and parameter estimation accuracy. A real data set on Clinical Global Impression is analyzed forillustration.

Correlation and Path Analysis of Yield and Related Traits of Upland Rice Genotypes across Weeding Regimes

This investigation was carried out at International Institute of Tropical Agriculture IITA, Ibadan, Nigeria to estimate correlation coefficients of yield and associated traits of thirty-six upland rice lines and the effect of these attributes in four weeding regimes. The weeding regimes were: weed free, weeding twice (21 and 42 days after sowing (DAS), weeding once at 42 DAS only, and weeding once at 21 DAS only, laid out in an alpha lattice design replicated two times. Correlation analysis indicated that yield was significantly and positively associated with panicle length, panicle number and 100 seed weight in both years. There was also a highly significant negative correlation between weed dry matter at all the sampling periods and number of tillers, plant height at maturity in 2011 and number of panicles and panicle length in 2012. Weed dry matter was negatively correlated with grain yield (P < 0.01) in both years clearly explaining the adverse effect of weed on the yield of rice. Path analysis revealed that days to 50% flowering, 100 seed weight, number of panicles and panicle length had direct positive effect on yield across the weeding regimes. Hence, selection based on these traits could help bring simultaneous improvement of yield and yield attributes. For further breeding activities, more emphasis should be given to 100 seed weight, days to 50% flowering, number of panicles and panicle length as they showed high correlation in addition to maximum direct positive effects on yield.