Assessing Curriculum Efficiency Through Monte Carlo Simulation

2018 ◽  
Vol 22 (4) ◽  
pp. 597-610
Author(s):  
David Torres ◽  
Jorge Crichigno ◽  
Carmella Sanchez
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Frequency Distribution ◽  
Relative Frequency ◽  
Monte Carlo Algorithm ◽  
Pass Rate ◽  
Random Numbers ◽  
Pass Rates ◽  
Low Pass ◽  
Relative Frequency Distribution

A Monte Carlo algorithm is designed to predict the average time to graduate by enrolling virtual students in a degree plan. The algorithm can be used to improve graduation rates by identifying bottlenecks in a degree plan (e.g., low pass rate courses and prerequisites). Random numbers are used to determine whether students pass or fail classes by comparing them to institutional pass rates. Courses cannot be taken unless prerequisites and corequisites are satisfied. The output of the algorithm generates a relative frequency distribution which plots the number of students who graduate by semester. Pass rates of courses can be changed to determine the courses that have the greatest impact on the time to graduate. Prerequisites can also be removed to determine whether certain prerequisites significantly affect the time to graduate.

scholarly journals Analisis Risiko Finansial Dengan Metode Simulasi Monte Carlo (Studi Kasus: Pt. Phase Delta Control)

2016 ◽  
Vol 8 (1) ◽  
pp. 62
Author(s):  
Atikah Aghdhi Pratiwi ◽  
Rosa Rilantiana
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Historical Data ◽  
Uncertain Variable ◽  
Random Numbers ◽  
Selling Price ◽  
Sales Volume ◽  
Simulation Research ◽  
Cost Of Production ◽  
A Company

AbstractBasically, the purpose of a company is make a profit and enrich the owners of the company. This is manifested by development and achievement of good performance, both in financial and operational perspective. But in reality, not all of companies can achieve good performance. One of them is because exposure of risk. This could threaten achievement of the objectives and existence of the company. Therefore, companies need to have an idea related to possible condition and financial projection in future periods that are affected by risk. One of the possible method is Monte Carlo Simulation. Research will be conducted at PT. Phase Delta Control with historical data related to production/sales volume, cost of production and selling price. Historical data will be used as Monte Carlo Simulation with random numbers that describe probability of each risk variables describing reality. The main result is estimated profitability of PT. Phase Delta Control in given period. Profit estimation will be uncertain variable due to some uncertainty

Investigation of contribution of number of trials in Monte Carlo simulation for uncertainty estimation for a pressure balance

2021 ◽  
pp. 014233122110390
Author(s):  
Jasveer Singh ◽  
Neha Bura ◽  
Kapil Kaushik ◽  
Lakshmi Annamalai Kumaraswamidhas ◽  
Nita Dilawar Sharma
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Measurement Uncertainty ◽  
Critical Factor ◽  
Random Number Generation ◽  
Effective Area ◽  
National Physical Laboratory ◽  
Conventional Technique ◽  
Random Numbers ◽  
Pressure Balance

It is well established that the estimation of measurement uncertainty is vital for the validation of any measurement and is an essential parameter of quality assurance. Apart from the conventional technique of law of propagation of uncertainty (LPU), which has many limitations, Monte Carlo simulation (MCS) technique has become an essential tool for the estimation of measurement uncertainty in various fields of metrology. The most critical factor in MCS is the generation of random numbers of the input quantities according to their probability distributions. The number of Monte Carlo trials to generate these random numbers significantly affects the results. In particular, the required number of trials is also affected by the parameter for which the uncertainty is to be estimated. Hence, in the current paper, the effect of selection of the number of trials on the random number generation and the resulting output in terms of standard deviation (SD) is investigated for the uncertainty in the effective area of a pneumatic reference pressure standard (NPLI-4) at the CSIR-National Physical Laboratory of India. The simulation results thus obtained are compared amongst themselves, with an adaptive approach as well as with the experimental results. The outcomes are analyzed and discussed in detail.

scholarly journals Fragmented Rockfall Volume Distribution from Photogrammetry-Based Structural Mapping and Discrete Fracture Networks

2020 ◽  
Vol 10 (19) ◽  
pp. 6977
Author(s):  
Renato Macciotta ◽  
Chris Gräpel ◽  
Roger Skirrow
Keyword(s):  
Frequency Distribution ◽  
Relative Frequency ◽  
Fracture Network ◽  
Discrete Fracture Network ◽  
Volume Distribution ◽  
Structural Mapping ◽  
Discrete Fracture ◽  
Relative Frequency Distribution ◽  
Rockfall Protection Structures ◽  
Rockfall Protection

The design of rockfall protection structures requires information about the falling block volumes. Computational tools for rockfall trajectory simulation are now capable of modeling block fragmentation, requiring the fragmented volume-relative frequency distribution of rockfalls as input. This can be challenging at locations with scarce or nonexistent rockfall records and where block surveys are not feasible. The work in this paper shows that simple discrete fracture network realizations from structural mapping based on photogrammetric techniques can be used to reliably estimate rock fall block volumes. These estimates can be used for dimensioning rockfall protection structures in cases where data is scarce or not available. The methodology is tested at two sites in the Canadian Cordillera where limestone outcrops have been the source of recurrent rockfalls. The results suggest that fragmentation will largely tend to occur through weak planes and expansion of non-persistent discontinuities, while other block breakage mechanisms exert less influence in the fragmented volume-relative frequency distribution of rockfalls. Therefore, block volume distribution can be estimated using a simple discrete fracture network (DFN) with fully persistent discontinuities. Limitations of the methods are also discussed, as well as potential future research to address such limitations.

Bootstraps, Een Nieuw Statistisch Alternatief

1988 ◽  
Vol 31 ◽  
pp. 45-56 ◽  
Author(s):  
E.D.J. Schils
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Relative Frequency ◽  
Sampling Error ◽  
Bootstrap Method ◽  
Sampling Theory ◽  
Arithmetic Mean ◽  
Language Loss ◽  
Relative Frequency Distribution ◽  
The Bootstrap Method

The bootstrap is a Monte Carlo method for the approximation of the sampling error of a statistic. The bootstrap method estimates this standard error on the basis of the repeated calculation of the statistic at hand in each of a great number of so-called bootstrap samples, i.e. samples with replacement from a probability distribution which exactly mirrors the empirical relative frequency distribution. The method is useful when there is no analytical sampling theory available for the statistic at hand, or when violation of underlying assumptions precludes the application of an available sampling theory. This paper uses an analytically well-known problem as the context for the presentation of the method, viz. the sampling distribution of the arithmetic mean. The method is then applied to an investigation of language loss using an unfamiliar research design.

Comments on Bartolino et al. (2011): limits of cumulative relative frequency distribution curves for hotspot identification

2011 ◽  
Vol 53 (4) ◽  
pp. 597-601 ◽  
Author(s):  
Luis Cayuela ◽  
Lucía Gálvez-Bravo ◽  
Luis María Carrascal ◽  
Fábio S. de Albuquerque
Keyword(s):  
Frequency Distribution ◽  
Relative Frequency ◽  
Relative Frequency Distribution

Suggestions and Criteria for More Effective Regression Sampling

1974 ◽  
Vol 4 (3) ◽  
pp. 341-348 ◽  
Author(s):  
J. P. Demaerschalk ◽  
A. Kozak
Keyword(s):  
Linear Regression ◽  
Frequency Distribution ◽  
Relative Frequency ◽  
Sampling Design ◽  
Simple Linear Regression ◽  
Relative Frequency Distribution ◽  
Independent Variable

Techniques are developed for more effective sampling for simple linear regression, making use of the relative frequency distribution of the observations over the range of the independent variable. A basis for testing the efficiency of a given sampling design is also provided.

scholarly journals Relative Frequency Distribution of D125 C Values for Spore Isolates from the Mariner-Mars 1969 Spacecraft

1971 ◽  
Vol 21 (5) ◽  
pp. 832-836
Author(s):  
W. W. Bond ◽  
M. S. Favero ◽  
N. J. Petersen ◽  
J. H. Marshall
Keyword(s):  
Frequency Distribution ◽  
Relative Frequency ◽  
Relative Frequency Distribution
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