scholarly journals Comparing Rating Scales of Different Lengths: Equivalence of Scores from 5-Point and 7-Point Scales

1997 ◽  
Vol 80 (2) ◽  
pp. 355-362 ◽  
Author(s):  
Andrew M. Colman ◽  
Claire E. Norris ◽  
Carolyn C. Preston
Keyword(s):  
Public Transport ◽  
Rating Scales ◽  
Cross Validation ◽  
Linear Equations ◽  
Retail Store ◽  
Least Squares Regression ◽  
Power Functions ◽  
Data Set ◽  
Self Administered Questionnaire ◽  
Variance Explained

Using a self-administered questionnaire, 227 respondents rated service elements associated with a restaurant, retail store, or public transport company on several 5-point and 7-point rating scales. Least-squares regression showed that linear equations for estimating 7-point from 5-point and 5-point from 7-point ratings explained over 85% of the variance and fitted the data almost as well as higher-order polynomials and power functions. In a cross-validation on a new data set the proportion of variance explained fell to about 76%. Functionally inverse versions of the derived linear equations were calculated for the convenience of researchers and psychometricians.

scholarly journals Bias and Sampling Error of the Estimated Proportion of Genotypic Variance Explained by Quantitative Trait Loci Determined From Experimental Data in Maize Using Cross Validation and Validation With Independent Samples

Genetics ◽  
2000 ◽  
Vol 154 (4) ◽  
pp. 1839-1849 ◽  
Author(s):  
H Friedrich Utz ◽  
Albrecht E Melchinger ◽  
Chris C Schön
Keyword(s):  
Cross Validation ◽  
Agronomic Traits ◽  
Sampling Error ◽  
Genotypic Variance ◽  
Data Set ◽  
Frequency Distributions ◽  
Independent Validation ◽  
Wide Range ◽  
Variance Explained ◽  
Qtl Effects

Abstract Cross validation (CV) was used to analyze the effects of different environments and different genotypic samples on estimates of the proportion of genotypic variance explained by QTL (p). Testcrosses of 344 F3 maize lines grown in four environments were evaluated for a number of agronomic traits. In each of 200 replicated CV runs, this data set was subdivided into an estimation set (ES) and various test sets (TS). ES were used to map QTL and estimate p for each run (p^ES) and its median (p~ES) across all runs. The bias of these estimates was assessed by comparison with the median (p~IS.ES) obtained from TS. We also used two independent validation samples derived from the same cross for further comparison. The median p~ES showed a large upward bias compared to p~TS.ES. Environmental sampling generally had a smaller effect on the bias of p~ES than genotypic sampling or both factors simultaneously. In independent validation, p~TS.ES was on average only 50% of p~ES. A wide range among p^ES reflected a large sampling error of these estimates. QTL frequency distributions and comparison of estimated QTL effects indicated a low precision of QTL localization and an upward bias in the absolute values of estimated QTL effects from ES. CV with data from three QTL studies reported in the literature yielded similar results as those obtained with maize testcrosses. We therefore recommend CV for obtaining asymptotically unbiased estimates of p and consequently a realistic assessment of the prospects of MAS.

scholarly journals Development of Ground Special Vehicle PHM with Case-Based Reason Model

2021 ◽  
Vol 11 (10) ◽  
pp. 4494
Author(s):  
Qicai Wu ◽  
Haiwen Yuan ◽  
Haibin Yuan
Keyword(s):  
Cross Validation ◽  
Optimization Methods ◽  
Engineering Practice ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Data Set ◽  
Attribute Weights ◽  
Special Vehicle ◽  
Weight Allocation ◽  
Case Based

The case-based reasoning (CBR) method can effectively predict the future health condition of the system based on past and present operating data records, so it can be applied to the prognostic and health management (PHM) framework, which is a type of data-driven problem-solving. The establishment of a CBR model for practical application of the Ground Special Vehicle (GSV) PHM framework is in great demand. Since many CBR algorithms are too complicated in weight optimization methods, and are difficult to establish effective knowledge and reasoning models for engineering practice, an application development using a CBR model that includes case representation, case retrieval, case reuse, and simulated annealing algorithm is introduced in this paper. The purpose is to solve the problem of normal/abnormal determination and the degree of health performance prediction. Based on the proposed CBR model, optimization methods for attribute weights are described. State classification accuracy rate and root mean square error are adopted to setup objective functions. According to the reasoning steps, attribute weights are trained and put into case retrieval; after that, different rules of case reuse are established for these two kinds of problems. To validate the model performance of the application, a cross-validation test is carried on a historical data set. Comparative analysis of even weight allocation CBR (EW-CBR) method, correlation coefficient weight allocation CBR (CW-CBR) method, and SA weight allocation CBR (SA-CBR) method is carried out. Cross-validation results show that the proposed method can reach better results compared with the EW-CBR model and CW-CBR model. The developed PHM framework is applied to practical usage for over three years, and the proposed CBR model is an effective approach toward the best PHM framework solutions in practical applications.

scholarly journals Development and double cross-validation of new spot urine sodium equation to predict 24-h urine sodium in the Malaysian population

2021 ◽  
Vol 40 (S1) ◽  
Author(s):  
Fatimah Othman ◽  
Rashidah Ambak ◽  
Mohd Azahadi Omar ◽  
Suzana Shahar ◽  
Noor Safiza Mohd Nor ◽  
...  
Keyword(s):  
Cross Validation ◽  
Sodium Intake ◽  
Study Groups ◽  
Urine Collection ◽  
Urine Sodium ◽  
Data Set ◽  
Spot Urine ◽  
Malaysian Population ◽  
New Equation ◽  
Double Cross

Abstract Background Monitoring sodium intake through 24-h urine collection sample is recommended, but the implementation of this method can be difficult. The objective of this study was to develop and validate an equation using spot urine concentration to predict 24-h sodium excretion in the Malaysian population. Methods This was a Malaysian Community Salt Study (MyCoSS) sub-study, which was conducted from October 2017 to March 2018. Out of 798 participants in the MyCoSS study who completed 24-h urine collection, 768 of them have collected one-time spot urine the following morning. They were randomly assigned into two groups to form separate spot urine equations. The final spot urine equation was derived from the entire data set after confirming the stability of the equation by double cross-validation in both study groups. Newly derived spot urine equation was developed using the coefficients from the multiple linear regression test. A Bland-Altman plot was used to measure the mean bias and limits of agreement between estimated and measured 24-h urine sodium. The estimation of sodium intake using the new equation was compared with other established equations, namely Tanaka and INTERSALT. Results The new equation showed the least mean bias between measured and predicted sodium, − 0.35 (− 72.26, 71.56) mg/day compared to Tanaka, 629.83 (532.19, 727.47) mg/day and INTERSALT, and 360.82 (284.34, 437.29) mg/day. Predicted sodium measured from the new equation showed greater correlation with measured sodium (r = 0.50) compared to Tanaka (r =0.24) and INTERSALT (r = 0.44), P < 0.05. Conclusion Our newly developed equation from spot urine can predict least mean bias of sodium intake among the Malaysian population when 24-h urine sodium collection is not feasible.

Factor Analysis of Students’ Exposure to Social Media for Food and Beverage

2021 ◽  
Vol 8 (2) ◽  
pp. 113-118
Author(s):  
Noora Shrestha
Keyword(s):  
Social Media ◽  
Factor Analysis ◽  
Data Set ◽  
Factors Associated ◽  
Rotation Method ◽  
The Social ◽  
Food And Beverage ◽  
Social Media Platforms ◽  
Strongly Agree ◽  
Self Administered Questionnaire

Food and beverage marketing on social media is a powerful factor to influence students’ exposure to social media and application for food and beverage. It is a well-known fact that most of the food and beverage business target young people on the social media. The objective of the study is to identify the factors associated to the students’ exposure in the social media platforms for food and beverage. The young students between the ages 20 to 26 years completed a self-administered questionnaire survey on their media use for food and beverages. The questionnaire was prepared using Likert scale with five options from strongly agree to strongly disagree. The data set was described with descriptive statistics such as mean and standard deviation. The exploratory factor analysis with varimax rotation method was used to extract the factors. The most popular social media among the respondents were Facebook, Instagram, and You Tube. 73.3% of the students were exposed to food and beverage application in their mobile device and 76% of them followed the popular food and beverage pages in social media. The result revealed that social media posts, promotional offer, and hygienic concept have positively influenced majority of the students’ exposure to social media for food and beverage. Keywords: Factor analysis, Social Media, Food and Beverage, Student, Promotional Offer.

scholarly journals Tolerance Intervals in a Heteroscedastic Linear Regression Context with Applications to Aerospace Equipment Surveillance

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Janet Myhre ◽  
Daniel R. Jeske ◽  
Michael Rennie ◽  
Yingtao Bi
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Performance Metrics ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Automated System ◽  
Least Squares Regression ◽  
Data Set ◽  
Tolerance Intervals ◽  
Power Of The Test

A heteroscedastic linear regression model is developed from plausible assumptions that describe the time evolution of performance metrics for equipment. The inherited motivation for the related weighted least squares analysis of the model is an essential and attractive selling point to engineers with interest in equipment surveillance methodologies. A simple test for the significance of the heteroscedasticity suggested by a data set is derived and a simulation study is used to evaluate the power of the test and compare it with several other applicable tests that were designed under different contexts. Tolerance intervals within the context of the model are derived, thus generalizing well-known tolerance intervals for ordinary least squares regression. Use of the model and its associated analyses is illustrated with an aerospace application where hundreds of electronic components are continuously monitored by an automated system that flags components that are suspected of unusual degradation patterns.

scholarly journals Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model

2009 ◽  
Vol 6 (3) ◽  
pp. 5271-5304 ◽  
Author(s):  
M. Jung ◽  
M. Reichstein ◽  
A. Bondeau
Keyword(s):  
Eddy Covariance ◽  
Learning Algorithm ◽  
Gross Primary Production ◽  
Global Network ◽  
Data Set ◽  
Model Trees ◽  
Ensemble Approach ◽  
Model Tree ◽  
Biosphere Model ◽  
Variance Explained

Abstract. Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current global network of eddy covariance towers (FLUXNET). We present a new model TRee Induction ALgorithm (TRIAL) that performs hierarchical stratification of the data set into units where particular multiple regressions for a target variable hold. We propose an ensemble approach (Evolving tRees with RandOm gRowth, ERROR) where the base learning algorithm is perturbed in order to gain a diverse sequence of different model trees which evolves over time. We evaluate the efficiency of the model tree ensemble approach using an artificial data set derived from the the Lund-Potsdam-Jena managed Land (LPJmL) biosphere model. We aim at reproducing global monthly gross primary production as simulated by LPJmL from 1998–2005 using only locations and months where high quality FLUXNET data exist for the training of the model trees. The model trees are trained with the LPJmL land cover and meteorological input data, climate data, and the fraction of absorbed photosynthetic active radiation simulated by LPJmL. Given that we know the "true result" in the form of global LPJmL simulations we can effectively study the performance of the model tree ensemble upscaling and associated problems of extrapolation capacity. We show that the model tree ensemble is able to explain 92% of the variability of the global LPJmL GPP simulations. The mean spatial pattern and the seasonal variability of GPP that constitute the largest sources of variance are very well reproduced (96% and 94% of variance explained respectively) while the monthly interannual anomalies which occupy much less variance are less well matched (41% of variance explained). We demonstrate the substantially improved accuracy of the model tree ensemble over individual model trees in particular for the monthly anomalies and for situations of extrapolation. We estimate that roughly one fifth of the domain is subject to extrapolation while the model tree ensemble is still able to reproduce 73% of the LPJmL GPP variability here. This paper presents for the first time a benchmark for a global FLUXNET upscaling approach that will be employed in future studies. Although the real world FLUXNET upscaling is more complicated than for a noise free and reduced complexity biosphere model as presented here, our results show that an empirical upscaling from the current FLUXNET network with a model tree ensemble is feasible and able to extract global patterns of carbon flux variability.

scholarly journals Analysis of stratospheric NO2 trends above Jungfraujoch using ground-based UV-visible, FTIR, and satellite nadir observations

2012 ◽  
Vol 12 (5) ◽  
pp. 12357-12389
Author(s):  
F. Hendrick ◽  
E. Mahieu ◽  
G. E. Bodeker ◽  
K. F. Boersma ◽  
M. P. Chipperfield ◽  
...  
Keyword(s):  
Linear Trend ◽  
Stratospheric Aerosol ◽  
Atmospheric Composition ◽  
Data Sets ◽  
Least Squares Regression ◽  
Quasi Biennial Oscillation ◽  
Data Set ◽  
Explanatory Variables ◽  
Stratospheric Cooling ◽  
Rule Out

Abstract. The trend in stratospheric NO2 column at the NDACC (Network for the Detection of Atmospheric Composition Change) station of Jungfraujoch (46.5° N, 8.0° E) is assessed using ground-based FTIR and zenith-scattered visible sunlight SAOZ measurements over the period 1990 to 2009 as well as a composite satellite nadir data set constructed from ERS-2/GOME, ENVISAT/SCIAMACHY, and METOP-A/GOME-2 observations over the 1996–2009 period. To calculate the trends, a linear least squares regression model including explanatory variables for a linear trend, the mean annual cycle, the quasi-biennial oscillation (QBO), solar activity, and stratospheric aerosol loading is used. For the 1990–2009 period, statistically indistinguishable trends of −3.7 ± 1.1%/decade and −3.6 ± 0.9%/decade are derived for the SAOZ and FTIR NO2 column time series, respectively. SAOZ, FTIR, and satellite nadir data sets show a similar decrease over the 1996–2009 period, with trends of −2.4 ± 1.1%/decade, −4.3 ± 1.4%/decade, and −3.6 ± 2.2%/decade, respectively. The fact that these declines are opposite in sign to the globally observed +2.5%/decade trend in N2O, suggests that factors other than N2O are driving the evolution of stratospheric NO2 at northern mid-latitudes. Possible causes of the decrease in stratospheric NO2 columns have been investigated. The most likely cause is a change in the NO2/NO partitioning in favor of NO, due to a possible stratospheric cooling and a decrease in stratospheric chlorine content, the latter being further confirmed by the negative trend in the ClONO2 column derived from FTIR observations at Jungfraujoch. Decreasing ClO concentrations slows the NO + ClO → NO2 + Cl reaction and a stratospheric cooling slows the NO + O3 → NO2 + O2 reaction, leaving more NOx in the form of NO. The slightly positive trends in ozone estimated from ground- and satellite-based data sets are also consistent with the decrease of NO2 through the NO2 + O3 → NO3 + O2 reaction. Finally, we cannot rule out the possibility that a strengthening of the Dobson-Brewer circulation, which reduces the time available for N2O photolysis in the stratosphere, could also contribute to the observed decline in stratospheric NO2 above Jungfraujoch.

Ability of Vis-PIR spectroscopy to monitor changes in organic carbon of loamy soils at two depths

2021 ◽  
Author(s):  
Hayfa Zayani ◽  
Youssef Fouad ◽  
Didier Michot ◽  
Zeineb Kassouk ◽  
Zohra Lili-Chabaane ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Near Infrared ◽  
Full Range ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Calibration Data ◽  
Least Squares Regression ◽  
Data Set ◽  
Soil Carbon Stocks ◽  
The Mean

&lt;p&gt;Visible-Near Infrared (Vis-NIR) spectroscopy has proven its efficiency in predicting several soil properties such as soil organic carbon (SOC) content. In this preliminary study, we explored the ability of Vis-NIR to assess the temporal evolution of SOC content. Soil samples were collected in a watershed (ORE AgrHys), located in Brittany (Western France). Two sampling campaigns were carried out 5 years apart: in 2013, 198 soil samples were collected respectively at two depths (0-15 and 15-25 cm) over an area of 1200 ha including different land use and land cover; in 2018, 111 sampling points out of 198 of 2013 were selected and soil samples were collected from the same two depths. Whole samples were analyzed for their SOC content and were scanned for their reflectance spectrum. Spectral information was acquired from samples sieved at 2 mm fraction and oven dried at 40&amp;#176;C, 24h prior to spectra acquisition, with a full range Vis-NIR spectroradiometer ASD Fieldspec&amp;#174;3. Data set of 2013 was used to calibrate the SOC content prediction model by the mean of Partial Least Squares Regression (PLSR). Data set of 2018 was therefore used as test set. Our results showed that the variation &amp;#8710;SOC&lt;sub&gt;obs&lt;/sub&gt;&lt;sub&gt;&lt;/sub&gt;obtained from observed values in 2013 and 2018 (&amp;#8710;SOC&lt;sub&gt;obs&lt;/sub&gt; = Observed SOC (2018) - Observed SOC (2013)) is ranging from 0.1 to 25.9 g/kg. Moreover, our results showed that the prediction performance of the calibrated model was improved by including 11 spectra of 2018 in the 2013 calibration data set (R&amp;#178;= 0.87, RMSE = 5.1 g/kg and RPD = 1.92). Furthermore, the comparison of predicted and observed &amp;#8710;SOC between 2018 and 2013 showed that 69% of the variations were of the same sign, either positive or negative. For the remaining 31%, the variations were of opposite signs but concerned mainly samples for which &amp;#8710;SOCobs is less than 1,5 g/kg. These results reveal that Vis-NIR spectroscopy was potentially appropriate to detect variations of SOC content and are encouraging to further explore Vis-NIR spectroscopy to detect changes in soil carbon stocks.&lt;/p&gt;

Global Mapping of Soil Saturated Hydraulic Conductivity Combining Legacy Data, Spatial Covariates and Machine Learning

2021 ◽  
Author(s):  
Surya Gupta ◽  
Peter Lehmann ◽  
Andreas Papritz ◽  
Tomislav Hengl ◽  
Sara Bonetti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hydraulic Conductivity ◽  
Cross Validation ◽  
Arable Land ◽  
Remotely Sensed ◽  
Saturated Hydraulic Conductivity ◽  
Pedotransfer Functions ◽  
Data Set ◽  
Global Database ◽  
Spatially Distributed

&lt;p&gt;Saturated soil hydraulic conductivity (Ksat) is a key parameter in many hydrological and climatic modeling applications, as it controls the partitioning between precipitation, infiltration and runoff. Values of Ksat are often deduced from Pedotransfer Functions (PTFs) using maps of soil attributes. To circumvent inherent limitations of present PTFs (heavy reliance of arable land measurements, ignoring soil structure, and geographic bias to temperate regions), we propose a new global Ksat map at 1&amp;#8211;km resolution by harnessing technological advances in machine learning and availability of remotely sensed surrogate information (terrain, climate and vegetation). We compiled a comprehensive Ksat data set with 13,258 data geo-referenced points from literature and other sources. The data were standardized and quality-checked in order to provide a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB was then applied to develop a Covariate-based GeoTransfer Function (CoGTF) model for predicting spatially distributed Ksat values using remotely sensed information on various environmental covariates. The model accuracy assessment based on spatial cross-validation shows a concordance correlation coefficient (CCC) of 0.16 and a root meansquare error (RMSE) of 1.18 for log10 Ksat values in cm/day (CCC=0.79 and RMSE=0.72 for non spatial cross-validation). The generated maps of Ksat represent spatial patterns of soil formation processes more distinctly than previous global maps of Ksat based on soil texture information and bulk density. The validation indicates that Ksat could be modeled without bias using CoGTFs that harness spatially distributed surface and climate attributes, compared to soil information based PTFs. The relatively poor performance of all models in the validation (low CCC and high RMSE) highlights the need for the collection of additional Ksat values to train the model for regions with sparse data.&lt;/p&gt;

Opinion on Different Classification Algorithms Used in Internet of Things Environment for Large Data Set

2019 ◽  
Vol 9 (1) ◽  
pp. 51-60
Author(s):  
Akhil Bansal ◽  
Piyush Kumar Shukla ◽  
Manish Kumar Ahirwar
Keyword(s):  
Internet Of Things ◽  
Economic Benefit ◽  
Cross Validation ◽  
Large Data ◽  
Classification Algorithms ◽  
Large Dataset ◽  
Data Set ◽  
Survey Article ◽  
Internet Of Things Environment ◽  
The Internet Of Things

Nowadays, IoT is an emerging technique and has evolved in many areas such as healthcare, smart homes, agriculture, smart city, education, industries, automation, etc. Many sensor and actuator-based devices deployed in these areas collect data or sense the environment. This data is further used to classify the complicated problem related to the particular environment around us, which also increases efficiency, productivity, accuracy and the economic benefit of the devices. The main aim of this survey article is how the data collected by these sensors in the Internet of Things-based applications are handled and classified by classification algorithms. This survey article also identifies various classification algorithms such as KNN, Random forest logistic regression, SVM with different parameters, such as accuracy cross validation, etc., applied on the large dataset generated by sensor-based devices in various IoT-based applications to classify it. In addition, this article also gives a brief review on advance IoT called CIoT.

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