Готові списки джерел за темами / Statistics and Computer Science

Добірка наукової літератури з теми "Statistics and Computer Science"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 29 січня 2023

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Статті в журналах з теми "Statistics and Computer Science"

1

Matshonisa Seeletse, Solly, Tsakani Violet Ndobe, Tichavasia Alex Dandadzi, and Taurai Hungwe. "Crowdsourcing benefits in postgraduate project supervision: Sefako Makgatho Health Sciences University statistics and computer science case study." Environmental Economics 7, no. 2 (June 3, 2016): 122–29. http://dx.doi.org/10.21511/ee.07(2).2016.13.

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The paper reports on the findings made on an experiential exercise of Bachelor of Science Honours in Statistics (BSc Hons Stat) in the Department of Statistics and Operations Research (SOR) of the Sefako Makgatho Health Sciences University (SMU) in South Africa. SOR is a small, understaffed department, which offers courses for degrees from Bachelor to Doctoral levels in the subfields of Artificial Intelligence, Data Mining, Operations Research, Statistics and related ones. On SMU campus, expertize in some of these fields is also available in the Department of Computer Science (DCS). In the 2015 academic year SOR admitted 20 BSc Hons Stat students beyond its staffing capacity. Then, SOR invited DCS in a crowdsourcing initiative to jointly supervise student projects in the various subfields mentioned. The challenges include conflict and limited experience. These are managed satisfactorily though, but mainly because they occur at low levels. This crowdsourcing arrangement nevertheless results in timely submissions of final projects, improved quality projects worthy of being published, innovation, quality teamwork, and some synergistic outcomes. Coordinators also learn and/or improved some project management skills
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Cowles, Mary Kathryn. "Probability and Statistics for Computer Science." American Statistician 60, no. 1 (February 2006): 98. http://dx.doi.org/10.1198/tas.2006.s37.

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LaLonde, Steven M. "Probability and Statistics for Computer Science." Technometrics 46, no. 4 (November 2004): 491–92. http://dx.doi.org/10.1198/tech.2004.s235.

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4

Cleveland, William S. "Learning from Data: Unifying Statistics and Computer Science." International Statistical Review 73, no. 2 (January 15, 2007): 217–21. http://dx.doi.org/10.1111/j.1751-5823.2005.tb00276.x.

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Tannehill, Robert. "Computer-Based Statistics." Science & Technology Libraries 6, no. 4 (July 3, 1986): 61–81. http://dx.doi.org/10.1300/j122v06n04_06.

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6

Baxter, Laurence A., and Arnold O. Allen. "Probability, Statistics, and Queueing Theory with Computer Science Applications." Technometrics 34, no. 2 (May 1992): 240. http://dx.doi.org/10.2307/1269262.

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7

Derringer, George C. "Statistics for the Engineering and Computer Sciences." Technometrics 31, no. 3 (August 1989): 387–88. http://dx.doi.org/10.1080/00401706.1989.10488570.

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McCool, John I. "Probability and Statistics With Reliability, Queuing and Computer Science Applications." Technometrics 45, no. 1 (February 2003): 107. http://dx.doi.org/10.1198/tech.2003.s25.

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9

Ratkó, I. "On special mathematical and computer science methods in medical sciences." Journal of Mathematical Sciences 92, no. 3 (November 1998): 3926–29. http://dx.doi.org/10.1007/bf02432365.

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10

Yuan Hwang Li. "Lord_DIF: A Computer Program to Compute Lord's DIF Statistics." Applied Psychological Measurement 19, no. 1 (March 1995): 72. http://dx.doi.org/10.1177/014662169501900108.

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Дисертації з теми "Statistics and Computer Science"

1

Raj, Alvin Andrew. "Ambiguous statistics - how a statistical encoding in the periphery affects perception." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/79214.

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Анотація:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 159-163).
Recent understanding in human vision suggests that the periphery compresses visual information to a set of summary statistics. Some visual information is robust to this lossy compression, but others, like spatial location and phase are not perfectly represented, leading to ambiguous interpretations. Using the statistical encoding, we can visualize the information available in the periphery to gain intuitions about human performance in visual tasks, which have implications for user interface design, or more generally, whether the periphery encodes sufficient information to perform a task without additional eye movements. The periphery is most of the visual field. If it undergoes these losses of information, then our perception and ability to perform tasks efficiently are affected. We show that the statistical encoding explains human performance in classic visual search experiments. Based on the statistical understanding, we also propose a quantitative model that can estimate the average number of fixations humans would need to find a target in a search display. Further, we show that the ambiguities in the peripheral representation predict many aspects of some illusions. In particular, the model correctly predicts how polarity and width affects the Pinna-Gregory illusion. Visualizing the statistical representation of the illusion shows that many qualitative aspects of the illusion are captured by the statistical ambiguities. We also investigate a phenomena known as Object Substitution Masking (OSM), where the identity of an object is impaired when a sparse, non-overlapping, and temporally trailing mask surrounds that object. We find that different types of grouping of object and mask produce different levels of impairment. This contradicts a theory about OSM which predicts that grouping should always increase masking strength. We speculate some reasons for why the statistical model of the periphery may explain OSM.
by Alvin Andrew Raj.
Ph.D.
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2

Goudie, Robert J. B. "Bayesian structural inference with applications in social science." Thesis, University of Warwick, 2011. http://wrap.warwick.ac.uk/78778/.

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Structural inference for Bayesian networks is useful in situations where the underlying relationship between the variables under study is not well understood. This is often the case in social science settings in which, whilst there are numerous theories about interdependence between factors, there is rarely a consensus view that would form a solid base upon which inference could be performed. However, there are now many social science datasets available with sample sizes large enough to allow a more exploratory structural approach, and this is the approach we investigate in this thesis. In the first part of the thesis, we apply Bayesian model selection to address a key question in empirical economics: why do some people take unnecessary risks with their lives? We investigate this question in the setting of road safety, and demonstrate that less satisfied individuals wear seatbelts less frequently. Bayesian model selection over restricted structures is a useful tool for exploratory analysis, but fuller structural inference is more appealing, especially when there is a considerable quantity of data available, but scant prior information. However, robust structural inference remains an open problem. Surprisingly, it is especially challenging for large n problems, which are sometimes encountered in social science. In the second part of this thesis we develop a new approach that addresses this problem|a Gibbs sampler for structural inference, which we show gives robust results in many settings in which existing methods do not. In the final part of the thesis we use the sampler to investigate depression in adolescents in the US, using data from the Add Health survey. The result stresses the importance of adolescents not getting medical help even when they feel they should, an aspect that has been discussed previously, but not emphasised.
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Meintjes, M. M. (Maria Magdalena). "Evaluating the properties of sensory tests using computer intensive and biplot methodologies." Thesis, Stellenbosch : Stellenbosch University, 2007. http://hdl.handle.net/10019.1/20881.

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Анотація:
Assignment (MComm)--University of Stellenbosch, 2007.
ENGLISH ABSTRACT: This study is the result of part-time work done at a product development centre. The organisation extensively makes use of trained panels in sensory trials designed to asses the quality of its product. Although standard statistical procedures are used for analysing the results arising from these trials, circumstances necessitate deviations from the prescribed protocols. Therefore the validity of conclusions drawn as a result of these testing procedures might be questionable. This assignment deals with these questions. Sensory trials are vital in the development of new products, control of quality levels and the exploration of improvement in current products. Standard test procedures used to explore such questions exist but are in practice often implemented by investigators who have little or no statistical background. Thus test methods are implemented as black boxes and procedures are used blindly without checking all the appropriate assumptions and other statistical requirements. The specific product under consideration often warrants certain modifications to the standard methodology. These changes may have some unknown effect on the obtained results and therefore should be scrutinized to ensure that the results remain valid. The aim of this study is to investigate the distribution and other characteristics of sensory data, comparing the hypothesised, observed and bootstrap distributions. Furthermore, the standard testing methods used to analyse sensory data sets will be evaluated. After comparing these methods, alternative testing methods may be introduced and then tested using newly generated data sets. Graphical displays are also useful to get an overall impression of the data under consideration. Biplots are especially useful in the investigation of multivariate sensory data. The underlying relationships among attributes and their combined effect on the panellists’ decisions can be visually investigated by constructing a biplot. Results obtained by implementing biplot methods are compared to those of sensory tests, i.e. whether a significant difference between objects will correspond to large distances between the points representing objects in the display. In conclusion some recommendations are made as to how the organisation under consideration should implement sensory procedures in future trials. However, these proposals are preliminary and further research is necessary before final adoption. Some issues for further investigation are suggested.
AFRIKAANSE OPSOMMING: Hierdie studie spruit uit deeltydse werk by ’n produk-ontwikkeling-sentrum. Die organisasie maak in al hul sensoriese proewe rakende die kwaliteit van hul produkte op groot skaal gebruik van opgeleide panele. Alhoewel standaard prosedures ingespan word om die resultate te analiseer, noodsaak sekere omstandighede dat die voorgeskrewe protokol in ’n aangepaste vorm geïmplementeer word. Dié aanpassings mag meebring dat gevolgtrekkings gebaseer op resultate ongeldig is. Hierdie werkstuk ondersoek bogenoemde probleem. Sensoriese proewe is noodsaaklik in kwaliteitbeheer, die verbetering van bestaande produkte, asook die ontwikkeling van nuwe produkte. Daar bestaan standaard toets- prosedures om vraagstukke te verken, maar dié word dikwels toegepas deur navorsers met min of geen statistiese kennis. Dit lei daartoe dat toetsprosedures blindelings geïmplementeer en resultate geïnterpreteer word sonder om die nodige aannames en ander statistiese vereistes na te gaan. Alhoewel ’n spesifieke produk die wysiging van die standaard metode kan regverdig, kan hierdie veranderinge ’n groot invloed op die resultate hê. Dus moet die geldigheid van die resultate noukeurig ondersoek word. Die doel van hierdie studie is om die verdeling sowel as ander eienskappe van sensoriese data te bestudeer, deur die verdeling onder die nulhipotese sowel as die waargenome- en skoenlusverdelings te beskou. Verder geniet die standaard toetsprosedure, tans in gebruik om sensoriese data te analiseer, ook aandag. Na afloop hiervan word alternatiewe toetsprosedures voorgestel en dié geëvalueer op nuut gegenereerde datastelle. Grafiese voorstellings is ook nuttig om ’n geheelbeeld te kry van die data onder bespreking. Bistippings is veral handig om meerdimensionele sensoriese data te bestudeer. Die onderliggende verband tussen die kenmerke van ’n produk sowel as hul gekombineerde effek op ’n paneel se besluit, kan hierdeur visueel ondersoek word. Resultate verkry in die voorstellings word vergelyk met dié van sensoriese toetsprosedures om vas te stel of statisties betekenisvolle verskille in ’n produk korrespondeer met groot afstande tussen die relevante punte in die bistippingsvoorstelling. Ten slotte word sekere aanbevelings rakende die implementering van sensoriese proewe in die toekoms aan die betrokke organisasie gemaak. Hierdie aanbevelings word gemaak op grond van die voorafgaande ondersoeke, maar verdere navorsing is nodig voor die finale aanvaarding daarvan. Waar moontlik, word voorstelle vir verdere ondersoeke gedoen.
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4

Billups, Robert Brent. "COMPUTER ASSISTED TREATMENT EVALUATION." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin997908439.

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5

Sjöbergh, Jonas. "Language Technology for the Lazy : Avoiding Work by Using Statistics and Machine Learning." Doctoral thesis, KTH, Numerisk Analys och Datalogi, NADA, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4023.

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Анотація:
Language technology is when a computer processes human languages in some way. Since human languages are irregular and hard to define in detail, this is often difficult. Despite this, good results can many times be achieved. Often a lot of manual work is used in creating these systems though. While this usually gives good results, it is not always desirable. For smaller languages the resources for manual work might not be available, since it is usually time consuming and expensive. This thesis discusses methods for language processing where manual work is kept to a minimum. Instead, the computer does most of the work. This usually means basing the language processing methods on statistical information. These kinds of methods can normally be applied to other languages than they were originally developed for, without requiring much manual work for the language transition. The first half of the thesis mainly deals with methods that are useful as tools for other language processing methods. Ways to improve part of speech tagging, which is an important part in many language processing systems, without using manual work, are examined. Statistical methods for analysis of compound words, also useful in language processing, is also discussed. The first part is rounded off by a presentation of methods for evaluation of language processing systems. As languages are not very clearly defined, it is hard to prove that a system does anything useful. Thus it is very important to evaluate systems, to see if they are useful. Evaluation usually entails manual work, but in this thesis two methods with minimal manual work are presented. One uses a manually developed resource for evaluating other properties than originally intended with no extra work. The other method shows how to calculate an estimate of the system performance without using any manual work at all. In the second half of the thesis, language technology tools that are in themselves useful for a human user are presented. This includes statistical methods for detecting errors in texts. These methods complement traditional methods, based on manually written error detection rules, for instance by being able to detect errors that the rule writer could not imagine that writers could make. Two methods for automatic summarization are also presented. One is based on comparing the overall impression of the summary to that of the original text. This is based on statistical methods for measuring the contents of a text. The second method tries to mitigate the common problem of very sudden topic shifts in automatically generated summaries. After this, a modified method for automatically creating a lexicon between two languages by using lexicons to a common intermediary language is presented. This type of method is useful since there are many language pairs in the world lacking a lexicon, but many languages have lexicons available with translations to one of the larger languages of the world, for instance English. The modifications were intended to improve the coverage of the lexicon, possibly at the cost of lower translation quality. Finally a program for generating puns in Japanese is presented. The generated puns are not very funny, the main purpose of the program is to test the hypothesis that by using "bad word" things become a little bit more funny.
QC 20100920
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6

Kress, Linda. "Analysis of computer science curriculum through development of an online crime reporting system." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4601.

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Анотація:
Thesis (M.S.)--West Virginia University, 2006.
Title from document title page. Document formatted into pages; contains vii, 189 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 175-189).
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7

Xiang, Gang. "Fast algorithms for computing statistics under interval uncertainty with applications to computer science and to electrical and computer engineering /." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2007. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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8

Clough, Andrew Lawrence. "Increasing adder efficiency by exploiting input statistics." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42424.

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Анотація:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2008.
Includes bibliographical references (p. 49-50).
Current techniques for characterizing the power consumption of adders rely on assuming that the inputs are completely random. However, the inputs generated by realistic applications are not random, and in fact include a great deal of structure. Input bits are more likely to remain in the same logical states from addition to addition than would be expected by chance and bits, especially the most significant bits, are very likely to be in the same state as their neighbors. Taking this data, I look at ways that it can be used to improve the design of adders. The first method I look at involves looking at how different adder architectures respond to the different characteristics of input data from the more significant and less significant bits of the adder, and trying to use these responses to create a hybrid adder. Unfortunately the differences are not sufficient for this approach to be effective. I next look at the implications of the data I collected for the optimization of Kogge- Stone adder trees, and find that in certain circumstances the use of experimentally derived activity maps rather than ones based on simple assumptions can increase adder performance by as much as 30%.
by Andrew Lawrence Clough.
M.Eng.
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9

Tikekar, Mehul (Mehul Deepak). "Energy-efficient video decoding using data statistics." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113990.

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Анотація:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 103-108).
Video traffic over the Internet is growing rapidly and is projected to be about 82% of the total consumer Internet traffic by 2020. To address this, new video coding standards such as H.265/HEVC (High Efficiency Video Coding) provide better compression especially at Full HD and higher video resolutions. HEVC achieves this through a variety of algorithmic techniques such as larger transform sizes and more accurate inter-frame prediction. However, these techniques increase the complexity of software and hardware-based video decoders. In this thesis, we design a hardware-based video decoder chip that exploits the statistics of the video to reduce the energy/pixel cost in several ways. For example, we exploit the sparsity in transform coefficients to reduce the energy/pixel cost of inverse transform by 29%. With the proposed architecture, larger transforms have the same energy/pixel cost as smaller transforms owing to their higher sparsity thus addressing the increased complexity of HEVC's larger transform sizes. As a second example, the energy/pixel cost of inter-prediction is dominated by off-chip memory access. We eliminate off-chip memory access by using on-chip embedded DRAM (eDRAM). However, eDRAM banks spend 80% of their energy on frequent refresh operations to retain stored data retention. To reduce refresh energy, we compress the video data stored in the eDRAM by exploiting spatial correlation among pixels. Thus, unused eDRAM banks can be turned off to reduce refresh energy by 55%. This thesis presents measured results for a 40 nm CMOS test chip that can decode Full HD video at 20 - 50 frames per second while consuming only 25 - 31 mW of system power. The system power is 6 times lower than the state-of-the-art and can enable even extremely energy-constrained wearable devices to decode video without exceeding their power budgets. The inverse transform result can enable future coding standards to use even larger transform sizes to improve compression without sacrificing energy efficiency.
by Mehul Tikekar.
Ph. D.
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10

Sharan, Lavanya. "Image statistics and the perception of surface reflectance." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/34356.

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Анотація:
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
MIT Institute Archives copy: p. 223 (last page) bound in reverse order.
Includes bibliographical references (p. 217-223).
Humans are surprisingly good at judging the reflectance of complex surfaces even when the surfaces are viewed in isolation, contrary to the Gelb effect. We argue that textural cues are important for this task. Traditional machine vision systems, on the other hand, are incapable of recognizing reflectance properties. Estimating the reflectance of a complex surface under unknown illumination from a single image is a hard problem. Recent work in reflectance recognition has shown that certain statistics measured o an image of a surface are diagnostic of reflectance. We consider opaque surfaces with medium scale structure and spatially homogeneous reflectance properties. For such surfaces, we find that statistics of intensity histograms and histograms of filtered outputs are indicative of the diffuse surface reflectance. We compare the performance of a learning algorithm that employs these image statistics to human performance in two psychophysical experiments. In the first experiment, observers classify images of complex surfaces according to the perceived reflectance. We find that the learning algorithm rivals human performance at the classification task. In the second experiment, we manipulate the statistics of images and ask observers to provide reflectance ratings. In this case, the learning algorithm performs similarly to human observers. These findings lead us to conclude that the image statistics capture perceptually relevant information.
by Lavanya Sharan.
S.M.
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Книги з теми "Statistics and Computer Science"

1

Probability and statistics for computer science. Hoboken, NJ: Wiley Interscience, 2008.

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2

Probability and statistics for computer science. Hoboken, NJ: Wiley-Interscience, 2002.

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3

Forsyth, David. Probability and Statistics for Computer Science. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-64410-3.

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4

Johnson, James L. Probability and Statistics for Computer Science. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1997. http://dx.doi.org/10.1002/9781118165836.

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5

Ross, Sheldon M. Probability models for computer science. San Diego: Harcourt Academic Press, 2002.

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6

J, Hand D., ed. Artificial intelligence frontiers in statistics: AI and statistics III. London: Chapman & Hall, 1993.

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7

Mari, Jean-François. Probabilistic and statistical methods in computer science. Boston: Kluwer Academic Publishers, 2001.

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8

Allen, Arnold O. Probability, statistics, and queueing theory: With computer science applications. 2nd ed. Boston: Academic Press, 1990.

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9

Mari, Jean-François. Probabilistic and Statistical Methods in Computer Science. Boston, MA: Springer US, 2001.

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10

K, Ford Richard, ed. Basic statistics using SAS. 2nd ed. St. Paul, MN: West Pub. Co., 1987.

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Частини книг з теми "Statistics and Computer Science"

1

O’Regan, Gerard. "Statistics." In Texts in Computer Science, 363–81. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81588-2_22.

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2

Rosamond, Frances. "Statistics of the Field." In Computer Science, 421–66. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1168-0_17.

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3

Wang, Liang, Jianxin Zhao, and Richard Mortier. "Statistics." In Undergraduate Topics in Computer Science, 31–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97645-3_3.

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4

Igual, Laura, and Santi Seguí. "Descriptive Statistics." In Undergraduate Topics in Computer Science, 29–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50017-1_3.

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5

Kaptein, Maurits, and Edwin van den Heuvel. "Bayesian Statistics." In Undergraduate Topics in Computer Science, 287–321. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-10531-0_8.

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O’Regan, Gerard. "Probability, Statistics and Applications." In Texts in Computer Science, 335–60. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44561-8_20.

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Burger, Wilhelm, and Mark J. Burge. "Histograms and Image Statistics." In Texts in Computer Science, 37–56. London: Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-6684-9_3.

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Burger, Wilhelm, and Mark J. Burge. "Histograms and Image Statistics." In Texts in Computer Science, 29–48. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05744-1_2.

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9

Hill, Richard, and Stuart Berry. "Data, Analysis and Statistics." In Texts in Computer Science, 21–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79104-9_2.

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10

Weik, Martin H. "statistics on request." In Computer Science and Communications Dictionary, 1663. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_18215.

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Тези доповідей конференцій з теми "Statistics and Computer Science"

1

Hall-Holt, Olaf A., and Kevin R. Sanft. "Statistics-infused Introduction to Computer Science." In SIGCSE '15: The 46th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2676723.2677218.

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2

Kyng, Timothy, Ayse Bilgin, and Busayasachee Puang-Ngern. "Data science: is it statistics or computer science? Statistics education in the age of big data." In Advances in Statistics Education: Developments, Experiences, and Assessments. International Association for Statistical Education, 2015. http://dx.doi.org/10.52041/srap.15502.

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Big Data / Data Science is a very important emerging area for statisticians. Software skills are increasingly important for statistical practitioners. Data science may be regarded by statisticians as a new name for statistical science but in industry and government the perception may be different. Recent advances in IT have enabled us to collect, store and easily access large amounts of data with modest cost. The capacity to analyse the data and use it for decision making has lagged behind. Software has been developed to filter, access and analyse data. Computer scientists and statisticians have been working separately, not jointly on this. This paper explores the implications of Big Data for statisticians’ education and aims to identify what skills are needed and software packages to use as well as the gaps between the perceptions of practitioners and academics about these issues.
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3

Clark, Megan. "The Effect of Context on the Teaching of Statistics ar First Year University Level." In Proceedings of the First Scientific Meeting of the IASE. International Association for Statistical Education, 1993. http://dx.doi.org/10.52041/srap.93204.

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Clack (1993b) has described how to Victoria University in New Zealand there are two different but essentially equivalent (with respect to level of difficulty and statistical content) first year statistics courses. These courses are: STAT131 Data and Probability- the course recommended for students majoring in mathematics, physics, chemistry, computer science and engineering; and STAT193 Statistics for the Natural and Social Sciences- suggested for those majoring in biological sciences, social sciences and medicine.
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4

Capilla, Carmen. "Assessing undergraduate students of a statistics course in environmental science." In Assessing Student leaning in Statistics. International Association for Statistical Education, 2007. http://dx.doi.org/10.52041/srap.07902.

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This paper presents a case study of assessing an applied statistics course. The assessment includes 10 computer lab activities, that students may voluntary complete during the fall semester. An individual voluntary project is also incorporated as part of the assessment. At the end of the semester there is a final examination about the course contents. These assessment methods and the difficulties to implement them are discussed. Students have heterogeneous backgrounds. Some of them show fear and anxiety towards the subject. This interferes with the implementation of the assessment and implies discipline problems.
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5

Glencross, Michael, and Andile Mji. "The role of a research resource centre in the training of social science researchers." In Training Researchers in the Use if Statistics. International Association for Statistical Education, 2000. http://dx.doi.org/10.52041/srap.00306.

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At the University of Transkei teaching and research are considered to be two sides of the same coin. Research is thus regarded as a fundamental and indispensable activity. With the University facing the challenge of becoming competitive within the new structure of higher education in South Africa, a strategic plan for research in the Humanities and Social Sciences has resulted in a flexible blueprint for research development. The Research Resource Centre supports this initiative by facilitating research capacity development and research excellence within the University. This paper discusses the role of the Research Resource Centre in the provision of research training for social science researchers and provides details of the various workshops and seminars used to develop skills in the use of statistics and computer-based statistical packages.
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6

Judi, Hairulliza Mohamad, Noraidah Sahari @ Ashari, and Zanaton Hj Eksan. "Enhancing interest in statistics among computer science students using computer tool entrepreneur role play." In THE 4TH INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES: Mathematical Sciences: Championing the Way in a Problem Based and Data Driven Society. Author(s), 2017. http://dx.doi.org/10.1063/1.4980944.

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7

N. Morgan, James, and Craig A. VanLengen. "The Digital Divide and K-12 Student Computer Use." In InSITE 2005: Informing Science + IT Education Conference. Informing Science Institute, 2005. http://dx.doi.org/10.28945/2926.

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The divide between those who have computer and Internet access and those who do not appears to be narrowing, however overall statistics may be misleading. Measures of computer availability in schools often include cases where computers are only available for administration or are available only on a very limited basis (Gootman, 2004). Access to a computer and the Internet outside of school helps to reinforce student learning and emphasize the importance of using technology. Recent U.S. statistics indicate that ethnic background and other demographic characteristics still have substantial impact on the availability and use of computers by students outside of the classroom. This paper examines recent census data to determine the impact of the household on student computer use outside of the classroom. Encouragingly, the findings of this study suggest that use of a computer at school substantially increases the chance that a student will use a computer outside of class. Additionally, this study suggests that computer use outside of the classroom is positively and significantly impacted by being in a household with adults who either use a computer at work or work in an industry where computers are extensively used.
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Hungwe, Taurai, E. L. Sesale, G. R. Miyambu, Tsepho Ramarumo, and S. M. Seeletse. "INNOVATIONS FROM INTERSECTIONS AND UNIONS OF COMPUTER SCIENCES AND STATISTICS IN AN EMBRYONIC HEALTH SCIENCE INSTITUTION." In 11th annual International Conference of Education, Research and Innovation. IATED, 2018. http://dx.doi.org/10.21125/iceri.2018.1578.

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9

Ziolko, B., and J. Galka. "Polish phones statistics." In 2010 International Multiconference on Computer Science and Information Technology (IMCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/imcsit.2010.5679868.

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Li, Chen, Wei Huali, Zhang Haoyu, Zeng Fanbo, and Qin Chaoling. "Comparative Study of Sports Statistics Textbooks and Other Disciplines' Statistics Textbooks." In 2011 International Conference on Future Computer Science and Education (ICFCSE). IEEE, 2011. http://dx.doi.org/10.1109/icfcse.2011.11.

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Звіти організацій з теми "Statistics and Computer Science"

1

Olefirenko, Nadiia V., Ilona I. Kostikova, Nataliia O. Ponomarova, Kateryna O. Lebedieva, Vira M. Andriievska, and Andrey V. Pikilnyak. Training elementary school teachers-to-be at Computer Science lessons to evaluate e-tools. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3890.

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Анотація:
The study purpose is to develop methodological support for students’ training for evaluation e-tools for young learners and to check its effectiveness experimentally. The module “Expert evaluation of the quality of e-tools for young learners” is offered for teachers-to-be. The determination of the weighting factor of each criterion by expert evaluations was organized. Educational principles, correlation e-tool content with the curriculum, interactivity, multimedia, assistance system, ergonomic requirements are mentioned. On the basis of the criterion rank, the significance of each criterion was calculated. The indicators to determine the level of preliminary expert evaluations of e-tools are proposed. The results are calculated with nonparametric methods of mathematical statistics, in particular, Pearson’s criterion χ2. The conclusion is the expert evaluation has different activity stages, gradually becoming a common phenomenon. Training teachers-to-be for e-tool expert evaluation at Computer Science, Mathematics, English is a complex process.
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2

McGee, Steven, Randi McGee-Tekula, Jennifer Duck, Lucia Dettori, Don Yanek, Andrew M. Rasmussen, Ronald I. Greenberg, and Dale F, Reed. Does Exploring Computer Science Increase Computer Science Enrollment? The Learning Partnership, April 2018. http://dx.doi.org/10.51420/conf.2018.1.

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This study investigated the impact of the Exploring Computer Science (ECS) program on the likelihood that students of all races and gender would pursue further computer science coursework in high school. ECS is designed to foster deep engagement through equitable inquiry around computer science concepts. The results indicate that students who pursued ECS as their first course were more likely to pursue another course relative to taking a traditional course as the first course.
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3

Lydon, Michael, and Jessie Ford. Computer Science Career Network. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada578200.

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4

Rosenthal, Robert. Computer science and technology :. Gaithersburg, MD: National Bureau of Standards, 1987. http://dx.doi.org/10.6028/nbs.ir.87-3516.

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5

Anderson, Loren James, and Marion Kei Davis. Functional Programming in Computer Science. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1237221.

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6

Ramamritham, Krithi. Computer Science Research in India. Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada300848.

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7

Shafer, S., R. Bryant, J. Wing, B. Myers, and J. Reynolds. Basic Research in Computer Science. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada275184.

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Shafer, S., R. Bryant, J. Wing, B. Myers, J. Reynolds, J. F. Lehman, T. Mitchell, and J. Carbonell. Basic Research in Computer Science. Fort Belvoir, VA: Defense Technical Information Center, October 1993. http://dx.doi.org/10.21236/ada275222.

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9

Blumenthal, M. Computer Science and Technology Board. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/6995568.

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10

Newton, H. J. Computing Science and Statistics. Volume 24. Graphics and Visualization. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada265181.

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