scholarly journals Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 609
Author(s):  
María del Mar Rueda ◽  
Beatriz Cobo ◽  
Antonio Arcos
Keyword(s):  
Regression Analysis ◽  
Regression Models ◽  
Regression Coefficients ◽  
Randomized Response ◽  
Statistical Tool ◽  
Complex Survey ◽  
Complex Sample ◽  
Qualitative Variables ◽  
World Survey ◽  
Sensitive Variable

Randomized response (RR) techniques are widely used in research involving sensitive variables, such as drugs, violence or crime, especially when a population mean or prevalence must be estimated. However, they are not generally applied to examine relationships between a sensitive variable and other characteristics. This type of technique was initially applied to qualitative variables, and studies later showed that a logistic regression may be performed with RR data. Since many of the variables considered in this context are quantitative, RR techniques were extended to these cases to estimate the values required. Regression analysis is a valuable statistical tool for exploring relationships among variables and for establishing associations between responses and covariates. In this article, we propose a design-based regression analysis for complex sample designs based on the unified RR approach. We present estimators of the regression coefficients, study their theoretical properties and consider different ways to estimate their variance. The properties of these estimation techniques were simulated using various quantitative randomized models. The method proposed was also used to analyse the findings from a real-world survey.

scholarly journals Better Coverage Intervals for Estimators from a Complex Sample Survey

2020 ◽  
Author(s):  
Phillip S. Kott
Keyword(s):  
Normal Distribution ◽  
Parameter Estimate ◽  
Regression Coefficients ◽  
Sample Survey ◽  
Complex Survey ◽  
Asymptotically Normal ◽  
Complex Survey Data ◽  
Complex Sample ◽  
Chi Squared ◽  
Coverage Interval

Coverage intervals for a parameter estimate computed using complex survey data are often constructed by assuming the parameter estimate has an asymptotically normal distribution and the measure of the estimator’s variance is roughly chi-squared. The size of the sample and the nature of the parameter being estimated render this conventional “Wald” methodology dubious in many applications. I developed a revised method of coverage-interval construction that “speeds up the asymptotics” by incorporating an estimated measure of skewness. I discuss how skewness-adjusted intervals can be computed for ratios, differences between domain means, and regression coefficients.

Applications of Regression Analysis in Shipbuilding Planning and Control

1988 ◽  
Vol 4 (02) ◽  
pp. 94-103
Author(s):  
Kurt W. Hagemeister ◽  
A. N. Perakis
Keyword(s):  
Regression Analysis ◽  
Regression Models ◽  
Construction Process ◽  
Analysis Framework ◽  
Statistical Tool ◽  
Ship Construction ◽  
Planning And Control ◽  
Control And Management ◽  
And Control ◽  
Oil Tanker

This paper offers a regression analysis framework which can be used in the day-to-day planning, control, and management of the ship construction process. The purpose of this study is to provide managers/ analysts with a statistical tool which will help predict future results and aid in better decisionmaking. The framework is presented by using a specific example--the hull block assembly process for a crude oil tanker. Four regression models are selected and analyzed for each of twelve block types. Two of the four models are found to be highly useful for the prediction of assembly man-hour costs. Of these two, the model employing weight as a predictor of man-hours is the most effective. Finally, several areas of future study are offered, based on the results of the regression analysis.

Regression analysis of indicators of a numerical experiment to determine the cooling time of oil

2020 ◽  
pp. 89-97
Author(s):  
A. U. Yakupov ◽  
D. A. Cherentsov ◽  
K. S. Voronin ◽  
Yu. D. Zemenkov
Keyword(s):  
Regression Analysis ◽  
Computer Experiment ◽  
Calculation Model ◽  
Cooling Time ◽  
Regression Coefficients ◽  
Fisher Criterion ◽  
Empirical Relationships ◽  
Factors Affecting ◽  
Oil Pipeline ◽  
Significant Regression

The article performed the processing of the results of a computer experiment to determine the cooling time of oil in a stopped oil pipeline. We proposed a calculation model in previous works that allows you to simulate the process of cooling oil.There was a need to verify the previously obtained results when conducting a laboratory experiment on a stand with soil. To conduct the experiment, it was necessary to conduct the planning of the experiment. The factors affecting the cooling time of oil in the oil pipeline, which will vary in the proposed experiment, are determined, empirical relationships are established. A regression analysis was carried out, and the dispersion homogeneity was checked using the Cochren criterion. The estimates of reproducibility variances are calculated. The adequacy hypothesis was tested using the Fisher criterion. Significant regression coefficients are established.

scholarly journals Knowledge, perception, and practice related to sodium intake among Malaysian adults: findings from the Malaysian Community Salt Study (MyCoSS)

2021 ◽  
Vol 40 (S1) ◽  
Author(s):  
Siew Man Cheong ◽  
Rashidah Ambak ◽  
Fatimah Othman ◽  
Feng J. He ◽  
Ruhaya Salleh ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Sodium Intake ◽  
Dietary Sodium ◽  
Health Concern ◽  
Sodium Reduction ◽  
Salt Reduction ◽  
Complex Sample ◽  
Health Organization

Abstract Background Excessive intake of sodium is a major public health concern. Information on knowledge, perception, and practice (KPP) related to sodium intake in Malaysia is important for the development of an effective salt reduction strategy. This study aimed to investigate the KPP related to sodium intake among Malaysian adults and to determine associations between KPP and dietary sodium intake. Methods Data were obtained from Malaysian Community Salt Survey (MyCoSS) which is a nationally representative survey with proportionate stratified cluster sampling design. A pre-tested face-to-face questionnaire was used to collect information on socio-demographic background, and questions from the World Health Organization/Pan American Health Organization were adapted to assess the KPP related to sodium intake. Dietary sodium intake was determined using single 24-h urinary sodium excretion. Respondents were categorized into two categories: normal dietary sodium intake (< 2000 mg) and excessive dietary sodium intake (≥ 2000 mg). Out of 1440 respondents that were selected to participate, 1047 respondents completed the questionnaire and 798 of them provided valid urine samples. Factors associated with excessive dietary sodium intake were analyzed using complex sample logistic regression analysis. Results Majority of the respondents knew that excessive sodium intake could cause health problems (86.2%) and more than half of them (61.8%) perceived that they consume just the right amount of sodium. Overall, complex sample logistic regression analysis revealed that excessive dietary sodium intake was not significantly associated with KPP related to sodium intake among respondents (P > 0.05). Conclusion The absence of significant associations between KPP and excessive dietary sodium intake suggests that salt reduction strategies should focus on sodium reduction education includes measuring actual dietary sodium intake and educating the public about the source of sodium. In addition, the relationship between the authority and food industry in food reformulation needs to be strengthened for effective dietary sodium reduction in Malaysia.

scholarly journals Estimation of Outdoor PM2.5 Infiltration into Multifamily Homes Depending on Building Characteristics Using Regression Models

2021 ◽  
Vol 13 (10) ◽  
pp. 5708
Author(s):  
Bo-Ram Park ◽  
Ye-Seul Eom ◽  
Dong-Hee Choi ◽  
Dong-Hwa Kang
Keyword(s):  
Regression Analysis ◽  
Correlation Analysis ◽  
Field Test ◽  
Outer Surface ◽  
Regression Models ◽  
Field Tests ◽  
Test Results ◽  
Correlation And Regression Analysis ◽  
Building Characteristics ◽  
Pm2.5 Concentration

The purpose of this study was to evaluate outdoor PM2.5 infiltration into multifamily homes according to the building characteristics using regression models. Field test results from 23 multifamily homes were analyzed to investigate the infiltration factor and building characteristics including floor area, volume, outer surface area, building age, and airtightness. Correlation and regression analysis were then conducted to identify the building factor that is most strongly associated with the infiltration of outdoor PM2.5. The field tests revealed that the average PM2.5 infiltration factor was 0.71 (±0.19). The correlation analysis of the building characteristics and PM2.5 infiltration factor revealed that building airtightness metrics (ACH50, ELA/FA, and NL) had a statistically significant (p < 0.05) positive correlation (r = 0.70, 0.69, and 0.68, respectively) with the infiltration factor. Following the correlation analysis, a regression model for predicting PM2.5 infiltration based on the ACH50 airtightness index was proposed. The study confirmed that the outdoor-origin PM2.5 concentration in highly leaky units could be up to 1.59 times higher than that in airtight units.

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 The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jin Xu ◽  
Chao Yi
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Effective Theory ◽  
Standard Errors ◽  
Limited Data ◽  
Analysis Model ◽  
Multiple Objects ◽  
Model Categories ◽  
Model Expression

Cluster regression analysis model is an effective theory for a reasonable and fair player scoring game. It can roughly predict and evaluate the performance of athletes after the game with limited data and provide scientific predictions for the performance of athletes. The purpose of this research is to achieve the player’s postmatch scoring through the cluster regression model. Through the research and analysis of past ball games, the comparison and experiment of multiple objects based on different regression analysis theories, the following conclusions are drawn. Different regression models have different standard errors, but if the data in other model categories are put into the centroid model expression, the standard error and the error of the original model are within 0.3, which can replace other models for calculation. In the player’s postmatch scoring, although the expert’s prediction of the result is very accurate, within the error range of 1 copy, the player’s postmatch scoring mechanism based on the cluster regression analysis model is more accurate, and the error formula is in the 0.5 range. It is best to switch the data of the regression model twice to compare the scoring mechanism using different regression experiments.

COMPARISON OF OBSERVATIONS AND MACROCLIMATIC MODEL ESTIMATES OF MONTHLY WINTER SOIL TEMPERATURES AT OTTAWA

1985 ◽  
Vol 65 (1) ◽  
pp. 109-122 ◽  
Author(s):  
L. M. DWYER ◽  
H. N. HAYHOE
Keyword(s):  
Snow Cover ◽  
Predictive Models ◽  
Regression Models ◽  
Stepwise Regression ◽  
Regression Coefficients ◽  
Data Sets ◽  
Regression Equations ◽  
Grass Cover ◽  
Partial Regression ◽  
Soil Temperatures

Estimates of monthly soil temperatures under short-grass cover across Canada using a macroclimatic model (Ouellet 1973a) were compared to monthly averages of soil temperatures monitored over winter at Ottawa between November 1959 and April 1981. Although the fit between monthly estimates and Ottawa observations was generally good (R for all months and depths 0.10, 0.20, 0.50, 1.00 and 1.50 m was 0.90), it was noted that midwinter estimates were generally below observed temperatures at all soil depths. Data sets used in the development of the original Ouellet (1973a) multiple regression equations were collected from stations across Canada, many of which have reduced snow cover. It was found that the buffering capability of the snow cover accumulated at Ottawa during the winter months was underestimated by the pertinent partial regression coefficients in these equations. The coefficients were therefore modified for the Ottawa station during the winter months. The resultant regression models were used to estimate soil temperature during the winters of 1981–1982 and 1982–1983. Although the Ottawa-based models included fewer variables because of the smaller data base available from a single site, comparisons of model estimates and observations were good (R = 0.84 and 0.91) and midwinter estimates were not consistently underestimated as they were using the original Ouellet (1973a) model. Reliable monthly estimates of soil temperatures are important since they are a necessary input to more detailed predictive models of daily soil temperatures. Key words: Regression model, snowcover, stepwise regression, variable selection

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