Experimental Design and Analysis Methods in Multiple Deterministic Modelling for Quantifying Hydrocarbon In-Place Probability Distribution Curve

Synthesizing data analysis methods adopted under in situ global project in Nepal along withvariables and nature of study could be guiding reference for researchers especially to those involvedin on farm research. The review work was conducted with the objective to help in utilizing andmanaging in situ database system. The objectives of the experiment, the structure of the treatmentsand the experimental design used primarily determine the type of analysis. There were 60 papers ofthis project published in Nepal. All these papers are grouped under 8 thematic groups namely 1.Agroecosystem (3 papers), 2. Agromorphological and farmers’ perception (7 papers), 3. Croppopulation structure (5 papers), 4. Gender, policy and general (15 papers), 5. Isozyme andmolecular (6 papers), 6. Seed systems and farmers’ networks (5 papers), 7. Social, cultural andeconomical (11 papers) and 8. Value addition (8 papers). All these papers were reviewed basicallyfor data type, sample size, sampling methods, statistical methods and tools, varieties and purposes.Descriptive and inferential statistics along with multivariate methods were commonly used in onfarm research. Experimental design, the most common in on station trial was least used. Study overspace and time was not adopted. There were 5 kinds of data generated, 45 statistical tools adoptedin eight different crop species. Among the 5 kinds of data under these eight subject areas,categorical type was highest followed by discrete numerical. Binary type was least in frequency.Most of the papers were related to rice followed by taro and finger millet. Cucumber and pigeonpea were studied least. Descriptive statistics along with Χ2, multivariate analysis and regressionapproaches would be appropriate tools. Similarly SPSS and MINITAB may be good software. Thebest one among a number of statistical tools should be selected and utmost care must be exercisedwhile collecting data.Key words: Data analysis methods; on farm research; on station research; subject areasDOI: 10.3126/narj.v6i0.3371Nepal Agriculture Research Journal Vol.6 2005 pp.98-108

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Experimental design and primary data analysis methods for comparing adaptive interventions.

Psychological Methods ◽

10.1037/a0029372 ◽

2012 ◽

Vol 17 (4) ◽

pp. 457-477 ◽

Cited By ~ 117

Author(s):

Inbal Nahum-Shani ◽

Min Qian ◽

Daniel Almirall ◽

William E. Pelham ◽

Beth Gnagy ◽

...

Keyword(s):

Data Analysis ◽

Experimental Design ◽

Primary Data ◽

Adaptive Interventions ◽

Analysis Methods ◽

Primary Data Analysis ◽

Data Analysis Methods

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Asset market bubbles in an experiment with sequential information releases

Review of Behavioral Finance ◽

10.1108/rbf-11-2020-0280 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Brian D. Kluger

Keyword(s):

Experimental Design ◽

Probability Distribution ◽

Design Methodology ◽

Asset Price ◽

Common Factors ◽

Asset Market ◽

Content Type ◽

Present Design ◽

Sequential Information ◽

Fundamental Values

PurposeMuch of the author’s understanding of experimental asset market bubbles is based on the Smith, Suchanek and Williams (SSW) design. The purpose of this paper is to find alternative bubble-producing designs, which is a promising path for new insights.Design/methodology/approachThe Smith et al. (1988) experimental design has been widely used to study bubbles. This paper introduces a novel modification, where the asset has a binary liquidation value and no dividends. Dividends are replaced by the events affecting the liquidation value probability distribution.FindingsOverpricing is common and consistent with subject optimism concerning the random liquidation value. Bubbles are also observed, as the degree of overpricing often rises and then fall during the experiments. However, crashes where the asset price drops below fundamental values are not observed.Research limitations/implicationsSubject over optimism, speculation and/or subject confusion are possible bubble ingredients. More research is needed to determine how much the factors responsible for these bubbles differ from the factors responsible for the SSW design. However, it seems likely that there are at least some common factors given the structural similarities between the two designs.Originality/valueThe present design is novel and may provide a means to better generalize results from previous experiments based on the SSW design.

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Climatic and basin factors affecting the flood frequency curve: PART II – A full sensitivity analysis based on the continuous simulation approach combined with a factorial experimental design

Hydrology and Earth System Sciences ◽

10.5194/hess-4-483-2000 ◽

2000 ◽

Vol 4 (3) ◽

pp. 483-498 ◽

Cited By ~ 6

Author(s):

M. Franchini ◽

A. M. Hashemi ◽

P. E. O’Connell

Keyword(s):

Sensitivity Analysis ◽

Soil Moisture ◽

Experimental Design ◽

Probability Distribution ◽

Flood Frequency ◽

Rainfall Regime ◽

Model Parameters ◽

Rainfall Runoff ◽

Factorial Experimental Design ◽

Frequency Curve

Abstract. The sensitivity analysis described in Hashemi et al. (2000) is based on one-at-a-time perturbations to the model parameters. This type of analysis cannot highlight the presence of parameter interactions which might indeed affect the characteristics of the flood frequency curve (ffc) even more than the individual parameters. For this reason, the effects of the parameters of the rainfall, rainfall runoff models and of the potential evapotranspiration demand on the ffc are investigated here through an analysis of the results obtained from a factorial experimental design, where all the parameters are allowed to vary simultaneously. This latter, more complex, analysis confirms the results obtained in Hashemi et al. (2000) thus making the conclusions drawn there of wider validity and not related strictly to the reference set selected. However, it is shown that two-factor interactions are present not only between different pairs of parameters of an individual model, but also between pairs of parameters of different models, such as rainfall and rainfall-runoff models, thus demonstrating the complex interaction between climate and basin characteristics affecting the ffc and in particular its curvature. Furthermore, the wider range of climatic regime behaviour produced within the factorial experimental design shows that the probability distribution of soil moisture content at the storm arrival time is no longer sufficient to explain the link between the perturbations to the parameters and their effects on the ffc, as was suggested in Hashemi et al. (2000). Other factors have to be considered, such as the probability distribution of the soil moisture capacity, and the rainfall regime, expressed through the annual maximum rainfalls over different durations. Keywords: Monte Carlo simulation; factorial experimental design; analysis of variance (ANOVA)

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Research Convergence

IASL Annual Conference Proceedings ◽

10.29173/iasl7379 ◽

2019 ◽

Author(s):

Barbara A. Schultz-Jones ◽

Laura Pasquini

Keyword(s):

Experimental Design ◽

Data Collection ◽

School Leadership ◽

Empirical Studies ◽

School Libraries ◽

Instructional Interventions ◽

Academic Skill ◽

Analysis Methods ◽

Data Collection And Analysis ◽

Quasi Experimental

A subset of international scholarship from the full Causality: School Libraries and Student Success corpus comprising empirical studies conducted in non-American locations (n=47) are examined for: geographic distribution, publication outlets, citations, data collection and analysis methods, and research strands. The majority of papers used one experimental design or two or more methods for quasi-experimental design approach for data collection, and used at least one or more often two or more data analysis methods. Six categories describe the research: learning environment, student attributes, teacher and school leadership characteristics, instructional interventions, academic skill development, and external factors for achievement.

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Non-Intrusive Monitoring Algorithm for Resident Loads with Similar Electrical Characteristic

Processes ◽

10.3390/pr8111385 ◽

2020 ◽

Vol 8 (11) ◽

pp. 1385

Author(s):

Sheng Wu ◽

Kwok L. Lo

Keyword(s):

Neural Network ◽

Probability Distribution ◽

Bp Neural Network ◽

Distribution Curve ◽

Electrical Characteristics ◽

Monitoring Methods ◽

Monitoring Method ◽

Current Frequency ◽

Operation Pattern ◽

Load Monitoring

Non-intrusive load monitoring is a vital part of an overall load management scheme. One major disadvantage of existing non-intrusive load monitoring methods is the difficulty to accurately identify loads with similar electrical characteristics. To overcome the various switching probability of loads with similar characteristics in a specific time period, a new non-intrusive load monitoring method is proposed in this paper which will modify monitoring results based on load switching probability distribution curve. Firstly, according to the addition theorem of load working currents, the complex current is decomposed into the independently working current of each load. Secondly, based on the load working current, the initial identification of load is achieved with current frequency domain components, and then the load switching times in each hour is counted due to the initial identified results. Thirdly, a back propagation (BP) neural network is trained by the counted results, the switching probability distribution curve of an identified load is fitted with the BP neural network. Finally, the load operation pattern is profiled according to the switching probability distribution curve, the load operation pattern is used to modify identification result. The effectiveness of the method is verified by the measured data. This approach combines the operation pattern of load to modify the identification results, which improves the ability to identify loads with similar electrical characteristics.

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