scholarly journals On The Experience Of The Arab Open University With The Standard Deviation Method

2008 ◽  
Vol 4 (3) ◽  
pp. 61-72
Author(s):  
Abdulkarim S. Al-Eisa ◽  
Abdulla M. Alhemoud

The Arab Open University (AOU) has adopted the standard deviation method (SDM) as a grading system in replacement of a fixed scale. Adopting SDM was intended to remedy a problem that has resulted from discrepancies between AOU's graduation requirements and those of its partner, UK Open University. This paper aims mainly at investigating whether SDM has served the purpose for which it was selected. A data set of the final letter grades of students enrolled in 18 licensed courses from UK-OU at Kuwait branch was used. These letter grades were analyzed in comparison with the letter grades that would have been assigned to students had the fixed grading scale been utilized. The results of the comparative analysis revealed that SDM resulted in benefiting 39.4% of all students and lowering the letter grades of 10.4% of the students. Despite its positive results, SDM has not contributed significantly to remedying the problem in question. Thus, a case for withholding the D grade while continuing with using SDM was presented.

Author(s):  
Sean Maw ◽  
Indy Lagu

This paper presents methods, pros and consof using a letter grading system versus a percentagegrading system, in engineering course componentevaluations. In making evaluation criteria acrossdeliverables qualitatively similar, letter grading showsperformance equivalency across courses and subjectareas, as well as departments, faculties and universities.What is worth an ‘A’ is not always a trivial discussion.But it is an easier discussion than what is worth 83%versus 85%. How to letter grade various types ofdeliverables in a valid and equivalent fashion can still bechallenging. But with thoughtful marking rubrics, manydeliverables can be evaluated using letter grades. Thesecan be combined to produce valid final letter grades.Overall, there are advantages to taking such an approachto evaluation, and these are discussed in the paper.


2020 ◽  
Vol 12 (17) ◽  
pp. 2731
Author(s):  
Xuan-Hien Le ◽  
Giha Lee ◽  
Kwansue Jung ◽  
Hyun-uk An ◽  
Seungsoo Lee ◽  
...  

Spatiotemporal precipitation data is one of the essential components in modeling hydrological problems. Although the estimation of these data has achieved remarkable accuracy owning to the recent advances in remote-sensing technology, gaps remain between satellite-based precipitation and observed data due to the dependence of precipitation on the spatiotemporal distribution and the specific characteristics of the area. This paper presents an efficient approach based on a combination of the convolutional neural network and the autoencoder architecture, called the convolutional autoencoder (ConvAE) neural network, to correct the pixel-by-pixel bias for satellite-based products. The two daily gridded precipitation datasets with a spatial resolution of 0.25° employed are Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation (APHRODITE) as the observed data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) as the satellite-based data. Furthermore, the Mekong River basin was selected as a case study, because it is one of the largest river basins, spanning six countries, most of which are developing countries. In addition to the ConvAE model, another bias correction method based on the standard deviation method was also introduced. The performance of the bias correction methods was evaluated in terms of the probability distribution, temporal correlation, and spatial correlation of precipitation. Compared with the standard deviation method, the ConvAE model demonstrated superior and stable performance in most comparisons conducted. Additionally, the ConvAE model also exhibited impressive performance in capturing extreme rainfall events, distribution trends, and described spatial relationships between adjacent grid cells well. The findings of this study highlight the potential of the ConvAE model to resolve the precipitation bias correction problem. Thus, the ConvAE model could be applied to other satellite-based products, higher-resolution precipitation data, or other issues related to gridded data.


2015 ◽  
Vol 8 (2) ◽  
pp. 941-963 ◽  
Author(s):  
T. Vlemmix ◽  
F. Hendrick ◽  
G. Pinardi ◽  
I. De Smedt ◽  
C. Fayt ◽  
...  

Abstract. A 4-year data set of MAX-DOAS observations in the Beijing area (2008–2012) is analysed with a focus on NO2, HCHO and aerosols. Two very different retrieval methods are applied. Method A describes the tropospheric profile with 13 layers and makes use of the optimal estimation method. Method B uses 2–4 parameters to describe the tropospheric profile and an inversion based on a least-squares fit. For each constituent (NO2, HCHO and aerosols) the retrieval outcomes are compared in terms of tropospheric column densities, surface concentrations and "characteristic profile heights" (i.e. the height below which 75% of the vertically integrated tropospheric column density resides). We find best agreement between the two methods for tropospheric NO2 column densities, with a standard deviation of relative differences below 10%, a correlation of 0.99 and a linear regression with a slope of 1.03. For tropospheric HCHO column densities we find a similar slope, but also a systematic bias of almost 10% which is likely related to differences in profile height. Aerosol optical depths (AODs) retrieved with method B are 20% high compared to method A. They are more in agreement with AERONET measurements, which are on average only 5% lower, however with considerable relative differences (standard deviation ~ 25%). With respect to near-surface volume mixing ratios and aerosol extinction we find considerably larger relative differences: 10 ± 30, −23 ± 28 and −8 ± 33% for aerosols, HCHO and NO2 respectively. The frequency distributions of these near-surface concentrations show however a quite good agreement, and this indicates that near-surface concentrations derived from MAX-DOAS are certainly useful in a climatological sense. A major difference between the two methods is the dynamic range of retrieved characteristic profile heights which is larger for method B than for method A. This effect is most pronounced for HCHO, where retrieved profile shapes with method A are very close to the a priori, and moderate for NO2 and aerosol extinction which on average show quite good agreement for characteristic profile heights below 1.5 km. One of the main advantages of method A is the stability, even under suboptimal conditions (e.g. in the presence of clouds). Method B is generally more unstable and this explains probably a substantial part of the quite large relative differences between the two methods. However, despite a relatively low precision for individual profile retrievals it appears as if seasonally averaged profile heights retrieved with method B are less biased towards a priori assumptions than those retrieved with method A. This gives confidence in the result obtained with method B, namely that aerosol extinction profiles tend on average to be higher than NO2 profiles in spring and summer, whereas they seem on average to be of the same height in winter, a result which is especially relevant in relation to the validation of satellite retrievals.


Author(s):  
B Xiong ◽  
Z-G Wang ◽  
X-Q Fan ◽  
Y Wang

In order to make the shock train leading edge detection method more possible for operational application, a new detection method based on differential pressure signals is introduced in this paper. Firstly, three previous detection methods, including the pressure ratio method, the pressure increase method, and the standard deviation method, have been examined whether they are also applicable for shock train moving at different speeds. Accordingly, three experimental cases of back-pressure changing at different rates were conducted in this paper. The results show that the pressure ratio and the pressure increase method both have acceptable detection accuracy for shock train moving rapidly and slowly, and the standard deviation method is not applicable for rapid shock train movement due to its running time window. Considering the operational application, the differential pressure method is raised and tested in this paper. This detection method has sufficient temporal resolution for rapidly and slowly shock train moving, and can make a real-time detection. In the end, the improvements brought by the differential pressure method have been discussed.


Forced Grading Systems are popular and accepted for evaluating students, particularly in business schools. Under such systems students' numerical test scores are converted to letter grades (A, B, etc.) and awarded in mandated percentages. In common practice, schools mandate that 10% to 15% of students in a class receive As, 25% to 35% Bs, and 40% to 50% Cs. However, instructors must identify what numerical cutoffs satisfy mandated grade distributions, and that tedious effort might entail several solutions. This study introduces an Excel-based template with which instructors can establish numerical cutoffs that distribute students' grades in accord with mandated standards. Results indicate that the spreadsheet template is an efficient tool to evaluate students following the Forced Grading System


2018 ◽  
Vol 1 (1) ◽  
pp. 103
Author(s):  
Sasmita R. Setiawan

Currently all companies are looking for opportunities in the openness of international markets or global markets. Companies that can enter this global market are companies that have comparative and competitive advantages. It is necessary to make an important decision concerning the level or quantity of production for the manufacturing company, the determination of the amount or stock of merchandise for the trading company as well as the level of service production for the service company. One important aspect that needs proper management is the supply problem. One of the local companies that became the focus or object of research in this writing is UD. Mirama located in Gorontalo Province, especially the city of Gorontalo which sells AC electronic goods and TV. just like any other trading company the company is also having trouble determining the amount of merchandise inventory. Because often companies have difficulty in the amount of safety stock that must be prepared at the time of booking so that companies can anticipate the number of fluctuating demand. With this problem it is necessary to apply a method used as corporate guidance to overcome the problem of inventory. The method that will try to use is standard deviation method in determining the amount of safety stock. This method is applied to see how the most optimal security inventory, which is the most economical, in the sense that not too much which means waste or additional costs that are not necessary or not too little which means there is still danger of running out of inventory. By using model of qualitative and quantitative analysis, the result of research analysis from the research object is the application of standard deviation method can be determined how much the safety stock will be held by the company and can cover the occurrence of stock out when demand increase or fluctuation in demand.


Author(s):  
Arindam Majumder ◽  
Abhishek Majumder

Multi-objective optimization is one of the most popular research areas in the world of manufacturing. It concerns the manufacturing optimization problems involving more than one optimization simultaneously, but in this present scenario, it is becoming very tough to solve a manufacturing-related multi-objective problem as no logical method has been developed in assignment of response individual weight. Therefore, to tackle this problem, this chapter proposes a new integrated approach by combining Standard Deviation Method with Particle Swarm Optimization. Two examples of optimizing the advanced manufacturing process parameters are performed to test the proposed approach. The examples considered for this approach are also attempted using other established optimization techniques such as Desirability-based RSM and SDM-GA. The results verify the effectiveness of the proposed approach during multi-objective manufacturing process parameter optimization.


2020 ◽  
Vol 27 (2) ◽  
pp. 8-15
Author(s):  
J.A. Oyewole ◽  
F.O. Aweda ◽  
D. Oni

There is a crucial need in Nigeria to enhance the development of wind technology in order to boost our energy supply. Adequate knowledge about the wind speed distribution becomes very essential in the establishment of Wind Energy Conversion Systems (WECS). Weibull Probability Density Function (PDF) with two parameters is widely accepted and is commonly used for modelling, characterizing and predicting wind resource and wind power, as well as assessing optimum performance of WECS. Therefore, it is paramount to precisely estimate the scale and shape parameters for all regions or sites of interest. Here, wind data from year 2000 to 2010 for four different locations (Port Harcourt, Ikeja, Kano and Jos) were analysed and the Weibull parameters was determined. The three methods employed are Mean Standard Deviation Method (MSDM), Energy Pattern Factor Method (EPFM) and Method of Moments (MOM) for estimating Weibull parameters. The method that gave the most accurate estimation of the wind speed was MSDM method, while Energy Pattern Factor Method (EPFM) is the most reliable and consistent method for estimating probability density function of wind. Keywords: Weibull Distribution, Method of Moment, Mean Standard Deviation Method, Energy Pattern Method


1979 ◽  
Vol 25 (3) ◽  
pp. 432-438 ◽  
Author(s):  
P J Cornbleet ◽  
N Gochman

Abstract The least-squares method is frequently used to calculate the slope and intercept of the best line through a set of data points. However, least-squares regression slopes and intercepts may be incorrect if the underlying assumptions of the least-squares model are not met. Two factors in particular that may result in incorrect least-squares regression coefficients are: (a) imprecision in the measurement of the independent (x-axis) variable and (b) inclusion of outliers in the data analysis. We compared the methods of Deming, Mandel, and Bartlett in estimating the known slope of a regression line when the independent variable is measured with imprecision, and found the method of Deming to be the most useful. Significant error in the least-squares slope estimation occurs when the ratio of the standard deviation of measurement of a single x value to the standard deviation of the x-data set exceeds 0.2. Errors in the least-squares coefficients attributable to outliers can be avoided by eliminating data points whose vertical distance from the regression line exceed four times the standard error the estimate.


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