Relevant bibliographies by topics / Learning statistics

Academic literature on the topic 'Learning statistics'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Learning statistics"

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Holt, Lori L., and Andrew J. Lotto. "What are the statistics in statistical learning?" Journal of the Acoustical Society of America 114, no. 4 (October 2003): 2444. http://dx.doi.org/10.1121/1.4779327.

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Hossain, Munier. "Learning statistics online." BMJ 335, no. 7621 (September 29, 2007): s119. http://dx.doi.org/10.1136/bmj.39273.644294.ce.

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Estes, Katharine Graf. "From Tracking Statistics to Learning words: Statistical Learning and Lexical Acquisition." Language and Linguistics Compass 3, no. 6 (September 4, 2009): 1379–89. http://dx.doi.org/10.1111/j.1749-818x.2009.00164.x.

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Nam Hai, Hoang. "ABOUT TEACHING AND LEARNING STATISTICS AT PRIMARY SCHOOLS." Journal of Science, Educational Science 60, no. 8A (2015): 231–35. http://dx.doi.org/10.18173/2354-1075.2015-0289.

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Jatnika, R., M. Haffas, and H. Agustiani. "Learning Statistics Using Universitas Padjadjaran Statistical Analysis Series." Journal of Physics: Conference Series 1179 (July 2019): 012046. http://dx.doi.org/10.1088/1742-6596/1179/1/012046.

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D’Orazio, Marcello. "Statistical learning in official statistics: The case of statistical matching." Statistical Journal of the IAOS 35, no. 3 (August 26, 2019): 435–41. http://dx.doi.org/10.3233/sji-190518.

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Kusumarasdyati. "Statistical reasoning or statistical method: Students’ preferences for learning Statistics." Journal of Physics: Conference Series 1417 (December 2019): 012041. http://dx.doi.org/10.1088/1742-6596/1417/1/012041.

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Zirmansyah, Zirmansyah. "Kualitas Skripsi Mahasiswa Universitas Al Azhar Indonesia: Pengaruh Hasil Belajar Metodologi Penelitian dan Statistik terhadap Kualitas Skripsi." JURNAL Al-AZHAR INDONESIA SERI HUMANIORA 1, no. 1 (April 4, 2011): 19. http://dx.doi.org/10.36722/sh.v1i1.20.

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The objective of the research is to study the relationship between learning outcome on statistic, learning outcome on research methodology and thesis quality. The research was carried out at the student Al Azhar University, with 53 samples of thesis which were selected randomly. The research concludes there is positive correlation between: (1) learning outcome on statistics and thesis quality; (2) knowledge on research methodology and thesis quality; (3) furthermore, there is a positive correlation between learning outcome on statistcs, learning outcome on research methodology, with thesis quality. Therefore thesis quality can be increased by improving learning outcome on statistic, and learning outcome on research metodhology.
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Balabdaoui, Fadoua, Lutz Dümbgen, Klaus-Robert Müller, and Richard Samworth. "Statistics meets Machine Learning." Oberwolfach Reports 17, no. 1 (February 9, 2021): 231–72. http://dx.doi.org/10.4171/owr/2020/4.

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Bzdok, Danilo, Naomi Altman, and Martin Krzywinski. "Statistics versus machine learning." Nature Methods 15, no. 4 (April 2018): 233–34. http://dx.doi.org/10.1038/nmeth.4642.

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Dissertations / Theses on the topic "Learning statistics"

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Zhang, Bo. "Machine Learning on Statistical Manifold." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/hmc_theses/110.

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This senior thesis project explores and generalizes some fundamental machine learning algorithms from the Euclidean space to the statistical manifold, an abstract space in which each point is a probability distribution. In this thesis, we adapt the optimal separating hyperplane, the k-means clustering method, and the hierarchical clustering method for classifying and clustering probability distributions. In these modifications, we use the statistical distances as a measure of the dissimilarity between objects. We describe a situation where the clustering of probability distributions is needed and useful. We present many interesting and promising empirical clustering results, which demonstrate the statistical-distance-based clustering algorithms often outperform the same algorithms with the Euclidean distance in many complex scenarios. In particular, we apply our statistical-distance-based hierarchical and k-means clustering algorithms to the univariate normal distributions with k = 2 and k = 3 clusters, the bivariate normal distributions with diagonal covariance matrix and k = 3 clusters, and the discrete Poisson distributions with k = 3 clusters. Finally, we prove the k-means clustering algorithm applied on the discrete distributions with the Hellinger distance converges not only to the partial optimal solution but also to the local minimum.
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Thayne, Jeffrey L. "Making Statistics Matter: Using Self-data to Improve Statistics Learning." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/5214.

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Research has demonstrated that well into their undergraduate and even graduate education, learners often struggle to understand basic statistical concepts, fail to see their relevance in their personal and professional lives, and often treat them as little more than mere mathematics exercises. This study explored ways help learners in an undergraduate learning context to treat statistical inquiry as mattering in a practical research context, by inviting them to ask questions about and analyze large, real, messy datasets that they have collected about their own personal lives (i.e., self-data). This study examined the conditions under which such an intervention might (and might not) successfully lead to a greater sense of the relevance of statistics to undergraduate learners.
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Choy, Ko-leung Tyrone. "An investigation on the learning of statistics with MINITAB." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2005788X.

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Bonneau, Maxime. "Reinforcement Learning for 5G Handover." Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-140816.

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The development of the 5G network is in progress, and one part of the process that needs to be optimised is the handover. This operation, consisting of changing the base station (BS) providing data to a user equipment (UE), needs to be efficient enough to be a seamless operation. From the BS point of view, this operation should be as economical as possible, while satisfying the UE needs.  In this thesis, the problem of 5G handover has been addressed, and the chosen tool to solve this problem is reinforcement learning. A review of the different methods proposed by reinforcement learning led to the restricted field of model-free, off-policy methods, more specifically the Q-Learning algorithm. On its basic form, and used with simulated data, this method allows to get information on which kind of reward and which kinds of action-space and state-space produce good results. However, despite working on some restricted datasets, this algorithm does not scale well due to lengthy computation times. It means that the agent trained can not use a lot of data for its learning process, and both state-space and action-space can not be extended a lot, restricting the use of the basic Q-Learning algorithm to discrete variables. Since the strength of the signal (RSRP), which is of high interest to match the UE needs, is a continuous variable, a continuous form of the Q-learning needs to be used. A function approximation method is then investigated, namely artificial neural networks. In addition to the lengthy computational time, the results obtained are not convincing yet. Thus, despite some interesting results obtained from the basic form of the Q-Learning algorithm, the extension to the continuous case has not been successful. Moreover, the computation times make the use of reinforcement learning applicable in our domain only for really powerful computers.
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Wong, Sik-kwan Francis. "Outcome of a web-based statistic laboratory for teaching and learning of medical statistics." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43251687.

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Saive, Yannick. "DirCNN: Rotation Invariant Geometric Deep Learning." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252573.

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Recently geometric deep learning introduced a new way for machine learning algorithms to tackle point cloud data in its raw form. Pioneers like PointNet and many architectures building on top of its success realize the importance of invariance to initial data transformations. These include shifting, scaling and rotating the point cloud in 3D space. Similarly to our desire for image classifying machine learning models to classify an upside down dog as a dog, we wish geometric deep learning models to succeed on transformed data. As such, many models employ an initial data transform in their models which is learned as part of a neural network, to transform the point cloud into a global canonical space. I see weaknesses in this approach as they are not guaranteed to perform completely invariant to input data transformations, but rather approximately. To combat this I propose to use local deterministic transformations which do not need to be learned. The novelty layer of this project builds upon Edge Convolutions and is thus dubbed DirEdgeConv, with the directional invariance in mind. This layer is slightly altered to introduce another layer by the name of DirSplineConv. These layers are assembled in a variety of models which are then benchmarked against the same tasks as its predecessor to invite a fair comparison. The results are not quite as good as state of the art results, however are still respectable. It is also my belief that the results can be improved by improving the learning rate and its scheduling. Another experiment in which ablation is performed on the novel layers shows that the layers  main concept indeed improves the overall results.
Nyligen har ämnet geometrisk deep learning presenterat ett nytt sätt för maskininlärningsalgoritmer att arbeta med punktmolnsdata i dess råa form.Banbrytande arkitekturer som PointNet och många andra som byggt på dennes framgång framhåller vikten av invarians under inledande datatransformationer. Sådana transformationer inkluderar skiftning, skalning och rotation av punktmoln i ett tredimensionellt rum. Precis som vi önskar att klassifierande maskininlärningsalgoritmer lyckas identifiera en uppochnedvänd hund som en hund vill vi att våra geometriska deep learning-modeller framgångsrikt ska kunna hantera transformerade punktmoln. Därför använder många modeller en inledande datatransformation som tränas som en del av ett neuralt nätverk för att transformera punktmoln till ett globalt kanoniskt rum. Jag ser tillkortakommanden i detta tillgångavägssätt eftersom invariansen är inte fullständigt garanterad, den är snarare approximativ. För att motverka detta föreslår jag en lokal deterministisk transformation som inte måste läras från datan. Det nya lagret i det här projektet bygger på Edge Convolutions och döps därför till DirEdgeConv, namnet tar den riktningsmässiga invariansen i åtanke. Lagret ändras en aning för att introducera ett nytt lager vid namn DirSplineConv. Dessa lager sätts ihop i olika modeller som sedan jämförs med sina efterföljare på samma uppgifter för att ge en rättvis grund för att jämföra dem. Resultaten är inte lika bra som toppmoderna resultat men de är ändå tillfredsställande. Jag tror även resultaten kan förbättas genom att förbättra inlärningshastigheten och dess schemaläggning. I ett experiment där ablation genomförs på de nya lagren ser vi att lagrens huvudkoncept förbättrar resultaten överlag.
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Sandberg, Martina. "Credit Risk Evaluation using Machine Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138968.

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In this thesis, we examine the machine learning models logistic regression, multilayer perceptron and random forests in the purpose of discriminate between good and bad credit applicants. In addition to these models we address the problem of imbalanced data with the Synthetic Minority Over-Sampling Technique (SMOTE). The data available have 273 286 entries and contains information about the invoice of the applicant and the credit decision process as well as information about the applicant. The data was collected during the period 2015-2017. With AUC-values at about 73%some patterns are found that can discriminate between customers that are likely to pay their invoice and customers that are not. However, the more advanced models only performed slightly better than the logistic regression.
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Vallin, Simon. "Small Cohort Population Forecasting via Bayesian Learning." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209274.

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A set of distributional assumptions regarding the demographic processes of birth, death, emigration and immigration have been assembled to form a probabilistic model framework of population dynamics. This framework was summarized as a Bayesian network and Bayesian inference techniques are exploited to infer the posterior distributions of the model parameters from observed data. The birth, death and emigration processes are modelled using a hierarchical beta-binomial model from which the inference of the posterior parameter distribution was analytically tractable. The immigration process was modelled with a Poisson type regression model where posterior distribution of the parameters has to be estimated numerically. This thesis suggests an implementation of the Metropolis-Hasting algorithm for this task. Classifi cation of incomings into subpopulations of age and gender is subsequently made using a Dirichlet-multinomial hierarchic model, for which parameter inference is analytically tractable. This model framework is used to generate forecasts of demographic data, which can be validated using the observed outcomes. A key component of the Bayesian model framework used is that is estimates the full posterior distributions of demographic data, which can take into account the full amount of uncertainty when forecasting population growths.
Genom att använda en mängd av distributionella antaganden om de demografiska processerna födsel, dödsfall, utflyttning och inflyttning har vi byggt ett stokastiskt ramverk för att modellera befolkningsförändringar. Ramverket kan sammanfattas som ett Bayesianskt nätverk och för detta nätverk introduceras tekniker för att skatta parametrar i denna uppsats. Födsel, dödsfall och utflyttning modelleras av en hierarkisk beta-binomialmodell där parametrarnas posteriorifördelning kan skattas analytiskt från data. För inflyttning används en regressionsmodell av Poissontyp där parametervärdenas posteriorifördelning måste skattas numeriskt. Vi föreslår en implementation av Metropolis-Hastingsalgoritmen för detta. Klassificering av subpopulationer hos de inflyttande sker via en hierarkisk Dirichlet-multinomialmodell där parameterskattning sker analytiskt. Ramverket användes för att göra prognoser för tidigare demografisk data, vilka validerades med de faktiska utfallen. En av modellens huvudsakliga styrkor är att kunna skatta en prediktiv fördelning för demografisk data, vilket ger en mer nyanserad pronos än en enkel maximum-likelihood-skattning.
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黃式鈞 and Sik-kwan Francis Wong. "Outcome of a web-based statistic laboratory for teaching and learning of medical statistics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43251687.

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RYSZ, TERI. "METACOGNITION IN LEARNING ELEMENTARY PROBABILITY AND STATISTICS." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1099248340.

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Books on the topic "Learning statistics"

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Peck, Roxy. Statistics: Learning from data. Australia: Brooks/Cole, Cengage Learning, 2014.

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Riccia, Giacomo, Hans-Joachim Lenz, and Rudolf Kruse, eds. Learning, Networks and Statistics. Vienna: Springer Vienna, 1997. http://dx.doi.org/10.1007/978-3-7091-2668-4.

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A guide to learning statistics. New York: McGraw-Hill, 1996.

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Learning statistics through playing cards. Thousand Oaks: Sage Publications, 1996.

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Karen, Kampen, and Peter Tracey 1973-, eds. The statistics coach: Learning through practice. Don Mills, Ont: Oxford University Press, 2010.

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H, Robinson David, ed. Understanding and learning statistics by computer. Singapore: World Scientific, 1986.

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DasGupta, Anirban. Probability for Statistics and Machine Learning. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9634-3.

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Ben-Zvi, Dani, and Katie Makar, eds. The Teaching and Learning of Statistics. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23470-0.

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Neufeld, John L. Learning business statistics with Microsoft Excel. Upper Saddle River, N.J: Prentice Hall, 1997.

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Rothman, Stanley. Sandlot stats: Learning statistics with baseball. Baltimore: Johns Hopkins University Press, 2012.

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Book chapters on the topic "Learning statistics"

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Müller, Marlene. "Descriptive Statistics." In XploRe — Learning Guide, 43–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-60232-0_2.

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Arnold, Pip, Jere Confrey, Ryan Seth Jones, Hollylynne S. Lee, and Maxine Pfannkuch. "Statistics Learning Trajectories." In International Handbook of Research in Statistics Education, 295–326. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66195-7_9.

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Unpingco, José. "Statistics." In Python for Probability, Statistics, and Machine Learning, 101–96. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30717-6_3.

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Unpingco, José. "Statistics." In Python for Probability, Statistics, and Machine Learning, 123–236. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18545-9_3.

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Unpingco, José. "Statistics." In Python for Probability, Statistics, and Machine Learning, 135–358. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04648-3_3.

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van Aalst, Jan, Jin Mu, Crina Damşa, and Sydney E. Msonde. "Elementary Statistics." In Learning Sciences Research for Teaching, 41–60. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781315697239-4.

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James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. "Statistical Learning." In Springer Texts in Statistics, 15–57. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7138-7_2.

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James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. "Statistical Learning." In Springer Texts in Statistics, 15–57. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1418-1_2.

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James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. "Unsupervised Learning." In Springer Texts in Statistics, 373–418. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7138-7_10.

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James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. "Deep Learning." In Springer Texts in Statistics, 403–60. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1418-1_10.

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Conference papers on the topic "Learning statistics"

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Nogales Vasconcelos, Ana, Cauan Braga da Silva Cardoso, and Isabella Figueiredo Vieira. "Learning to portray your reality: teaching statistics to high school students." In Advances in Statistics Education: Developments, Experiences, and Assessments. International Association for Statistical Education, 2015. http://dx.doi.org/10.52041/srap.15113.

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The Institutional Scientific Initiation Scholarship Program (PIBIC) “Learning to Portray Your Reality” seeks to motivate female high school students to apply for undergraduate program in statistics at the University of Brasilia (UnB), to divulge the undergraduate program in statistics at UnB and to promote the statistical education. The high school students learned how to have a statistical thinking, what is the job of a statistician and the roll of statistic in science, as well as some basics concepts of statistics. They also learned how to work with the database available online by the Brazilian Institute of Geography and Statistics (IBGE). Furthermore, it was designed an activity to promote the undergraduate program in statistics at UnB and the learning of the statistic in their school.
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Borovcnik, Manfred. "E-learning or blended learning – enriching statistics for business students." In Statistics education for Progress: Youth and Official Statistics. International Association for Statistical Education, 2013. http://dx.doi.org/10.52041/srap.13303.

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There has been much interest within the scientific community to use the potential of new technol- ogies for teaching since they have become widely available. We will deal with two questions: What has emerged out of e-learning endeavours? What are vital issues for their success? The specific situation for learners within an e-learning setting will be analysed in the light of two “events”: First, the general discussion of the impact of technology on teaching at the ISI con- gress in Lisboa in 2007, and second, a series of papers on blended learning in the International Statistical Review published in the same year. Our long-term experience with a blended learning course on introductory statistics for business students will serve as background to respond to key questions for the success of e-learning. As an interactive exchange of feedback between students and staff has proved to be essential in the evaluation of our course, this paper gives support for blended learning enrichment of courses to assist the students in their learning process.
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Schield, Milo. "Statistical literacy: factual assessment to support hypothetical thinking." In Assessing Student Learning in Statistics. International Association for Statistical Education, 2007. http://dx.doi.org/10.52041/srap.07204.

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The GAISE College report suggested that teachers assess statistical literacy by students "inter- preting or critiquing articles in the news." Media stories typically present summary statistics to support non-statistical conclusions. Summary statistics require hypothetical thinking which in turn requires drill in factual exercises involving deductive right-wrong answers. This paper presents a wide range of deductive exercises that may help students develop the hypothetical thinking needed to deal with the fact that all statistics are socially constructed. This paper pre- sents 130 different topics involving fact-based exercises with objective answers. Of these, 50% are numerical, 30% are number-related and 20% are non-numeric. Selected examples are pre- sented. At least half of these exercises have been used by students in a web-based format. These exercises are classified by topics in traditional research statistics and in statistical literacy.
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Ocampo, Shirlee, Rechel Arcilla, Frumencio Co, Ryan Jumangit, and Felipe Diokno. "Enthusing students towards statistical literacy using transformative learning paradigm: implementation and appraisal." In Statistics education for Progress: Youth and Official Statistics. IASE international Association for Statistical Education, 2013. http://dx.doi.org/10.52041/srap.113201.

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and changing requirements of globalizing society. Hence, there is a need to shift from traditional method of teaching statistics to new paradigms. This paper presents the improvements implemented along with its appraisal in teaching general education statistics courses using the traditional transmissive pedagogy and then shifting to transformative learning paradigm. The transmissive pedagogy involves merely lectures and paper-and-pen tests, while the transformative learning paradigm integrates computer-based instructions, Web technologies, authentic assessment, problem-based learning, collaborative inquiry, and use of real-life data. Results showed a significant improvement in understanding statistics for both learning paradigms. However, the data did not provide evidence to indicate differences in the amount of learning between the two paradigms. Classical and Bayesian factor analyses both obtained seven non- intellective factors. The two paradigms differ significantly on five factors indicating that students are enthused towards statistical literacy under the transformative learning framework.
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Galmacci, Gianfranco, and Anna Maria Milito. "Distance learning: new frontiers for solving old problems." In Statistics Education and the Communication of Statistics. International Association for Statistical Education, 2005. http://dx.doi.org/10.52041/srap.05306.

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After a discussion on the use of technology for teaching purposes, the authors present an experiment carried out in Italy and based on the use of e-learning to face the problem of guiding large classes of students for lab activities in basic courses of Statistics. Even though this solution has been adopted to solve a local problem, it seems to have a wide range of potential applications.
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Krishnan, Saras, and Noraini Idris. "The use of a hierarchical construct to investigate students’ learning of inferential statistics." In Statistics education for Progress: Youth and Official Statistics. IASE international Association for Statistical Education, 2013. http://dx.doi.org/10.52041/srap.13202.

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At present, there is still a need for more research in the teaching and learning of inferential statistics because of the limitedness of literature in this area of statistics education. Moreover, there is continuing evidence of students’ partial or unsuccessful learning of many aspects of inferential statistics. This is one of the concerns brought to attention in my postgraduate research whereby part of my work involved the development of a hierarchical construct to identify the different levels of students’ learning of inferential statistics. This paper particularly discusses the use of this hierarchical construct to investigate the learning of inferential statistics among students.
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Petocz, Peter, and Anna Reid. "Learning and assessment in statistics." In Assessing Student Learning in Statistics. International Association for Statistical Education, 2007. http://dx.doi.org/10.52041/srap.070103.

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The main role of assessment is to support learning, and any view of assessment implies a corresponding view of learning. Research on students’ conceptions of statistics, learning in statistics and assessment, suggests that there is a clear variation from narrow to broad views. Another dimension is students’ perceptions of their future professional roles and how that impacts on their present studies. In order to support the learning process, assessment should be structured in such a way as to make apparent to students the full range of variation in conceptions and to encourage them towards the broadest and most inclusive ideas. Further, it is important that the approach to assessment has coherence with the overall pedagogical approach.
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Macgillivray, Helen. "Weaving assessment for student learning in probabilistic reasoning at the introductory tertiary level." In Assessing Student leaning in Statistics. International Association for Statistical Education, 2007. http://dx.doi.org/10.52041/srap.07702.

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For any course in a student’s degree program, the assessment should be part of an integrated assessment and learning package, with the components of the package combining to meet the learning objectives in a steady development of skills and operational knowledge that take account of the students’ various prior and future learnings. This paper considers such a package for an introductory course in probability and distributional modelling, including its objectives with reference to the nature of statistical thinking in probabilistic and distributional modelling, and general assessment principles. A new component of assessment to strengthen the problem-solving environment and to better address some of the objectives is described, together with student and tutor feedback and student data.
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Régnier, Jean-Claude. "Statistical Education and E-Learning." In Statistics and the Internet. International Association for Statistical Education, 2003. http://dx.doi.org/10.52041/srap.03203.

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Since the start of the 2002 academic year, the Department of Sciences of Education of the University of Lyon2, in partnership with the University of Rouen and the CNED (Centre National d'Enseignement à Distance ) of Poitiers (France), has put forward a pedagogical plan of action allowing students to prepare a B.SC degree in Sciences of Education. The goal of this article is the presentation of the problematic of the teaching - learning of the Statistics in this context. Key-words: guidance, e-learning, statistical education, teaching and learning of statistics . A long French version of this article is accessible to URL : ftp://nte.univ-lyon2.fr/users/regnier/public/IASE/BERLIN/.
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Aoyama, Kazuhiro, Michiko Watanabe, and Yoshiyasu Tamura. "Statistics learning environment for students through Japanese censusatschool project." In Statistics education for Progress: Youth and Official Statistics. International Association for Statistical Education, 2013. http://dx.doi.org/10.52041/srap.13502.

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Since the Curriculum revision in 2008 and 2009, statistics education in Japan is improving gradually. Number of teachers who have concerned with statistics education, develop and try new lessons has been increased. But the usage of software in statistics lessons is very limited. Many teachers teach statistics only with papers and pencils in traditional style. In this paper, what kinds of obstacles for teachers in Japan to teach statistics especially focused on software use are reported firstly. Secondly, we note needed supports for them, 1) Statistical software (or function) accessible without install process, 2) GUI which enable for teachers and students to analyze data intuitively, 3) Interesting dataset which can enrich students’ data analysis activities and lessons. Finally, we report construction and new system of Japanese CensusAtSchool website to match those demands.
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Reports on the topic "Learning statistics"

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Cavallo, Alberto, Guillermo Cruces, and Ricardo Perez-Truglia. Learning from Potentially-Biased Statistics: Household Inflation Perceptions and Expectations in Argentina. Cambridge, MA: National Bureau of Economic Research, March 2016. http://dx.doi.org/10.3386/w22103.

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Ilyin, M. E. The distance learning course «Theory of probability, mathematical statistics and random functions». OFERNIO, December 2018. http://dx.doi.org/10.12731/ofernio.2018.23529.

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Bertlin, Julian. Climate & environment assessment: Business case for better education statistics for improved learning (BESt). Evidence on Demand, September 2013. http://dx.doi.org/10.12774/eod_hd086.sept2013.bertlin.

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Peters, Vanessa, Deblina Pakhira, Latia White, Rita Fennelly-Atkinson, and Barbara Means. Designing Gateway Statistics and Chemistry Courses for Today’s Students: Case Studies of Postsecondary Course Innovations. Digital Promise, August 2022. http://dx.doi.org/10.51388/20.500.12265/162.

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Scholars of teaching and learning examine the impacts of pedagogical decisions on students’ learning and course success. In this report, we describes findings from case studies of eight innovative postsecondary introductory statistics and general chemistry courses that have evidence of improving student completion rates for minoritized and low-income students. The goal of the case studies was to identify the course design elements and pedagogical practices that were implemented by faculty. To identify courses, Digital Promise sought nominations from experts in statistics and chemistry education and reviewed National Science Foundation project abstracts in the Improving Undergraduate STEM Education (IUSE) program. The case studies courses were drawn from 2- and 4-year colleges and were implemented at the level of individual instructors or were part of a department or college-wide intervention. Among the selected courses, both introductory statistics (n = 5) and general chemistry (n = 3) involved changes to the curriculum and pedagogy. Curricular changes involved a shift away from teaching formal mathematical and chemical equations towards teaching that emphasizes conceptual understanding and critical thinking. Pedagogical changes included the implementation of peer-based active learning, formative practice, and supports for students’ metacognitive and self-regulation practices.
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Joo, Jenna, and Richard Spies. Aligning Many Campuses and Instructors around a Common Adaptive Learning Courseware in Introductory Statistics: Lessons from a Multi-Year Pilot in Maryland. Ithaka S+R, November 2019. http://dx.doi.org/10.18665/sr.312073.

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Ogenyi, Moses. Looking back on Nigeria’s COVID-19 School Closures: Effects of Parental Investments on Learning Outcomes and Avoidance of Hysteresis in Education. Research on Improving Systems of Education (RISE), March 2022. http://dx.doi.org/10.35489/bsg-rise-ri_2022/040.

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In this Insight Note, we explore how COVID-19 and related school closures impacted Nigerian schools, parents, and students. National data collected by the National Bureau of Statistics in 2020 through a monthly phone survey show that children had extremely limited contact with the education system during this time, and that families preferred low-cost alternatives such as in-home tutoring and increased parental involvement in education to e-learning tools. Additional data collected by the RISE Nigeria Team in a survey of 73 low-cost private schools in Abuja suggest that some schools did maintain contact with students during mandated school closures, that students experienced absolute learning losses equivalent to about 5-6 months of school missed in other contexts (Cooper et al, 1996), despite participation in alternative learning activities, and that the pandemic led to severe financial hardships for schools and teachers.
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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, November 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.
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Altonji, Joseph, and Charles Pierret. Employer Learning and Statistical Discrimination. Cambridge, MA: National Bureau of Economic Research, November 1997. http://dx.doi.org/10.3386/w6279.

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Cohn, David A., Zoubin Ghahramani, and Michael I. Jordan. Active Learning with Statistical Models. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada295617.

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Moody, John. Statistical Learning Theory and Algorithms. Fort Belvoir, VA: Defense Technical Information Center, February 1993. http://dx.doi.org/10.21236/ada270209.

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