Polynomial Regression Research Papers - Academia.edu (original) (raw)

This study examines the effects of leaders' self-awareness of their own leadership on followers' satisfaction, self-leadership, and leader effectiveness. A leader's self-awareness was conceptualized as the degree of similarity... more

This study examines the effects of leaders' self-awareness of their own leadership on followers' satisfaction, self-leadership, and leader effectiveness. A leader's self-awareness was conceptualized as the degree of similarity between the leader's self-description and his or her followers' descriptions of leader behaviors. Transformational and empowering leadership are measured from 48 leaders and 222 of their followers. Results from confirmatory factor analyses provide support for two types of leadership: transformational and empowering. Results from polynomial regression analyses indicate that self-awareness of transformational leadership is related to leader effectiveness and followers' supervisory satisfaction. In contrast, self-awareness of empowering leadership is related to followers' self-leadership. These effects of leadership self-awareness extend beyond the direct effect of leadership on the outcome variables.

Experiment was conducted with the aim of determining the effect of varying water temperature and ration size on growth and body composition of fry of the common carp, Cyprinus carpio. Common carp fry with an initial body weight (BW) of... more

Experiment was conducted with the aim of determining the effect of varying water temperature and ration size on growth and body composition of fry of the common carp, Cyprinus carpio. Common carp fry with an initial body weight (BW) of 0.86 g were fed a diet (34.9% protein, 18.3 KJ/g diet) at four ration sizes 4%, 5%, 6% and 7% of their body weight per day and reared at two water temperatures 28 and 32 °C for 60 days. Fry fed with 6% ration showed the highest mean final body weight at 28 °C. Final body weight was significantly (P<0.05) affected by ration and temperature. Cyprinus carpio fry raised at 28 °C had higher feed efficiency (FE) (44.36%) than the fry reared at 32 °C (40.98%) with 4% ration. Further, feed efficiency decreased with increase in ration levels in both temperatures. Protein efficiency ratio (PER) was higher (1.26) at 28 °C than at 32 °C (1.17). At 6% ration, common carp fry showed highest specific growth rate (SGR) (3.82%/day) at 28 °C as compared with at 32 °C (3.57%/day). A linear increase in protein and lipid contents was evident with increasing ration levels up to 6% body weight at both temperatures 28 and 32 °C. Second-order polynomial regression analysis of weight gain and SGR indicated the breakpoints at ration level 6.04% and 6.08% body weight per day at 28 and 32 °C. Hepatosomatic index (HSI) not affected by temperature and ration size while, viscerosomatic index (VSI) influenced (P<0.05) by ration size and temperature. Based on the above results, it may be concluded that 6% BW/day ration is optimal for growth of Cyprinus carpio fry at both the temperatures 28 and 32 °C.

An 8-week growth trial was conducted to assess the effect of dietary protein on growth, feed utilization, protein retention efficiency, and body composition of young Heteropneustes fossilis (10.02 ± 0.09 g; 9.93 ± 0.07 cm). Isocaloric... more

An 8-week growth trial was conducted to assess the effect of dietary protein on growth, feed utilization, protein retention efficiency, and body composition of young Heteropneustes fossilis (10.02 ± 0.09 g; 9.93 ± 0.07 cm). Isocaloric (4.15 kcal g−1, GE) diets with varying levels of protein (25, 30, 35, 40, 45, and 50% of the diet) were fed near to satiation to triplicate groups of fish. Optimum dietary protein was determined by analyzing live weight gain (LWG%), feed conversion ratio (FCR), protein efficiency ratio (PER), specific growth rate (SGR%), and protein retention efficiency (PRE%) data. Maximum LWG% (167), best FCR (1.42), PER (1.75), SGR (1.76), and PRE (31.7%) were evident in fish fed 40% protein diet (Diet 4). Body protein data also supported the above level. However, second-degree polynomial regression analysis of the above data indicated that inclusion of dietary protein in the range of 40–43% is optimum for the growth of young H. fossilis.

Regression models such as polynomial regression when deployed for training on training instances may sometimes not optimize well and leads to poor generalization on new training instances due to high bias or underfitting due to small... more

Regression models such as polynomial regression when deployed for training on training instances may sometimes not optimize well and leads to poor generalization on new training instances due to high bias or underfitting due to small value of polynomial degree and may lead to high variance or overfitting due to high degree of polynomial fitting degree. The hypothesis curve is not able to fit all the training instances with a smaller degree due to the changing curvature of curve again and again and also due to the increasing and decreasing nature of curve arising from the local extremas from the plot of points of the dataset curve. The local extremas in between the curve makes the hypothesis curve difficult to fit through all the training instances due to the small polynomial degree. Better optimization and generalization can be achieved by breaking the hypothesis curve into extremas i.e. local maximas and local minimas and deploying separate regression models for each maxima-minima or minima-maxima interval. The number of training instances used to fit the model can be reduced due to very less change in curvature of the curve between an interval due to absence of any local extrema. The time taken by the algorithm reduces due to reduction in the training instances to train which makes the model very less computationally expensive. The algorithm when tested on the UCI machine learning repository datasets gave an accuracy of 53.47% using polynomial regression and 92.06% using our algorithm on Combined Cycle Power Plant Data Set [1] and accuracy of 85.41% using polynomial regression and 96.33% by our algorithm on Real estate valuation Data Set [2]. The approach can be very beneficial for any betterment of mathematical field of study related to bias-variance, cost minimization and better fitting of curves in statistics. CCS Concepts • Computing methodologies➝ Modeling and simulation • Computing methodologies➝Model development and analysis

Soil bin investigations were carried out to study the influence of some soil parameters namely: moisture content and cone index, on draught force and soil disturbance of model tillage tools. The tools were tines in the groups of very... more

Soil bin investigations were carried out to study the influence of some soil parameters namely: moisture content and cone index, on draught force and soil disturbance of model tillage tools. The tools were tines in the groups of very narrow tines, narrow tines and wide tines. The soil under study was a sandy clay loam. It was observed that draught

Many social programs have a hard time making sure the right people receive the enough financial aid. It's tricky when a program focuses on the poorest segment of the population. This segment of population can't provide the necessary... more

Many social programs have a hard time making sure the right people receive the enough financial aid. It's tricky when a program focuses on the poorest segment of the population. This segment of population can't provide the necessary income and expense records to prove that they qualify to receive any financial aid under particular Govt. scheme. In this paper we predict someone's "Income Level" using the "Random Forest Classifier" based on the various attributes or features.

An accurate estimation of the pressure drop in well tubing is essential for the solution of a number of important production engineering and reservoir analysis problems. Several empirical correlations and mechanistic models have been... more

An accurate estimation of the pressure drop in well tubing is essential for the solution of a number of important production engineering and reservoir analysis problems. Several empirical correlations and mechanistic models have been proposed in the literature to estimate the pressure drop in vertical wells that produce a mixture of oil, water, and gas. Although many correlations and models are available to calculate the pressure loss, these models were developed based on a certain set of assumptions and for particular range of data where it may not
be applicable for use in different conditions. In this paper, group methods of data handling (GMDH) is used to build a model to predict the pressure drop in multiphase vertical wells. The developed GMDH model has shown the outstanding results, and it has outperformed all empirical correlations and mechanistic models, which have been compared to. The analysis of the results also confirmed that
the testing set achieves accurate estimation of the pressure drop. Trend analysis of the model showed that the model is correctly predicting the expected effects of the independent variables on pressure drop.

Objective To determine blood pressure distribution in schoolchildren and to derive population specific reference values appropriate for age, gender and height status. Design Cross sectional observational study. Setting Schools in... more

Objective To determine blood pressure distribution in schoolchildren and to derive population specific reference values appropriate for age, gender and height status. Design Cross sectional observational study. Setting Schools in Ernakulam district, Kerala, India, during 2005–06. Methods Stratified random cluster sampling method was used to select the children. Blood pressure and anthropometric data were collected from 20,263 students of 5–16 years age. Three readings of blood pressures of each child were taken by mercury sphygmomanometer and mean was taken for analysis. Blood pressure percentiles in relation to gender, age and height were estimated from a non-overweight population of 18,931 children using polynomial regression models. Results Children from study population have higher diastolic pressures for both sexes than international standard across all age groups. For systolic blood pressure, girls showed higher values than the international standard while for boys, the difference appears to be minimal. Conclusions Blood pressure distribution in children from our study population demonstrates a different pattern in comparison to existing international reference. Higher blood pressure values in the study population are of considerable public health significance.

The role of organizational structure as an important contextual variable has long been recognized in affecting a host of employee attitudes and behaviors, but there is a dearth of theoretical and empirical research that examines the ways... more

The role of organizational structure as an important contextual variable has long been recognized in affecting a host of employee attitudes and behaviors, but there is a dearth of theoretical and empirical research that examines the ways in which organizational structure influences the occurrence of self-efficacy and its performance effects. This study addresses this gap by exploring how the two core structural components-formalization and centralization-separately and jointly affect employee self-efficacy and how they interact with self-efficacy to influence employee task performance. The study further examines the extent to which structure weaves its influence on individual performance through perceptions of self-efficacy. Data from 120 Pakistani public sector employees were analyzed using partial least squares structural equation modelling (PLS-SEM) and polynomial regression to assess the hypothesized relationships. The empirical analysis shows that formalization is positively associated with self-efficacy while centralization has a negative association, and such an improvement/attenuation in self-efficacy is partly transformed into performance improvements. The findings further reveal that self-efficacy and performance relationship is diminished under conditions of high formalization and high centralization. We discuss implications for theory and practice and delineate directions for future research.

In community-based healthcare, the nursing workforce requires low-skilled nursing automation in the hospital to accelerate talent development towards high-skilled advance practice nurse for community deployment. As precursor, the hospital... more

In community-based healthcare, the nursing workforce requires low-skilled nursing automation in the hospital to accelerate talent development towards high-skilled advance practice nurse for community deployment. As precursor, the hospital bed pushing operation for medium-risk patient was hypothesized as a novice nursing task where artificial intelligence automation is possible. The solution framework was embodied by a concept of operation with non-invasive vitals monitoring as priority to study feasibility in addressing patient life-safety requirements. Polynomial regression machine learning of 65 one-hour sets of finger PPG data from a single subject were collected and studied. Convergence of finger PPG to 8th degree polynomial was observed which suggested process feasibility towards establishing patient safe states during autonomous journey. Process reliability ranged between 2% to 95% with long PPG counts as influencing factor for drops in reliability score. Motivation/Background: A predictable non-invasive vitals monitoring was priority to enable autonomous hospital bed pushing framework to address patient life-safety concerns during autonomous journey. Finger PPG is a non-invasive and easy to use method to monitor heart related activities and used to study for convergence and reliability within the framework. Method:65 one-hour sets of finger PPG was recorded from a single male, age 27 subject. The data was processed by polynomial regression machine learning technique to output the degree of polynomial with highest cross validation score mean. Results: Convergence of regressed PPG data to 8th degree for both pre-journey and journey datasets and degree of polynomial matching reliability of 2% to 95% were observed. Conclusions: Convergence of PPG data facilitates the establishment of safe physical states in vitals monitoring, enabling the autonomous hospital bed pushing framework for further development. Reliability remains an area for improvement via medical grade.

Past research on democracy and politicalcorruption produced mixed results becauseof differences in sampling and analyticalmethods. Moreover, an important shortcominghas been researchers' focus on detectinglinear effects alone. In the... more

Past research on democracy and politicalcorruption produced mixed results becauseof differences in sampling and analyticalmethods. Moreover, an important shortcominghas been researchers' focus on detectinglinear effects alone. In the current study,I statistically controlled for potentiallyconfounding economic factors and usedhierarchical polynomial regression toevaluate the form of thedemocracy-corruption relationship. Resultsshowed that a cubic function best fittedthe data. Despite eruptions of corruptionamong intermediate democracies, theconsolidation of advanced democraticinstitutions eventually reduced corruption.Ultimately, the initial politicalconditions and the final democraticachievements determined the magnitude ofpolitical corruption in a country.