Relevant bibliographies by topics / Statistical analysis

Academic literature on the topic 'Statistical analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Statistical analysis":

1

Begum, Hasina. "Statistical Analysis in GWAS." International Journal of Science and Research (IJSR) 12, no. 12 (December 5, 2023): 1072–78. http://dx.doi.org/10.21275/sr231215085226.

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Zhao, Bin. "Statistical analysis on Alzheimer's disease." Journal of Infectious Diseases & Travel Medicine 7, no. 2 (October 31, 2023): 1–18. http://dx.doi.org/10.23880/jidtm-16000177.

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Alzheimer's disease is a progressive neurodegenerative disease that occurs mostly in the elderly and has memory impairment as the main clinical symptom. There is no ideal treatment for Alzheimer's disease, so early prevention is important. In this paper, we use brain structural information to diagnose Alzheimer's disease features and cognitive-behavioral characteristics, which is important for early and accurate diagnosis of mild cognitive impairment. To investigate the factors influencing Alzheimer's disease, a correlation analysis model was developed after preprocessing the missing values of the data. First, the data features were viewed, the missing values of the data were analyzed, and the useless features were removed and the missing values of the remaining features were filled with the average value. To verify the accuracy of the subsequent intelligent diagnosis model and clustering model, this paper divides the training set and test set according to PTID. Finally, the top ten important features are selected and the Spearman coefficients are chosen according to the distribution of the features for correlation analysis. Machine learning methods were utilized to build an Alzheimer's classification model to solve the problem of intelligent diagnosis of Alzheimer's disease. The pre-processed dataset in the above paper was trained with the model, and five methods of logistic regression, support vector machine, KNN classification, decision tree classification and XGB were utilized to build the classification model, and the accuracy, recall and F1 value of each model were visualized and compared, among which the accuracy of XGB model reached 83%, which is reasonable for the intelligent diagnosis of the disease. A K-Means-based clustering model for disease types was established using the K-Means clustering algorithm, clustering CN, MCI and AD into three major classes, and then refining MCI into three subclasses. The optimal K-values and random seeds were firstly found using the elbow principle, then the cluster analysis was performed using the feature values and data sets selected after preprocessing, and finally the MCI in MCI was extracted and sub-clustered into three subclasses SMC, EMCI and LMCI. In order to investigate the evolution pattern of different categories of diseases over time, patients with 3 categories of diseases are screened separately for analysis in this paper. Firstly, by combining the results above and reviewing the data, the features irrelevant to this task and columns containing a large number of missing values were removed, the remaining features were selected and probability density plots were drawn, and all discrete features and all features that were essentially zero were continued to be screened out. After that, the 15 features of CN, MCI and AD diseases were plotted separately over time to reveal their evolution patterns over time. We reviewed the relevant literature, sorted out and summarized the existing studies at home and abroad, and summarized the criteria for determining the five stages of Alzheimer's disease and the early intervention of the disease.
3

Aziz, Nor Azlina Ab, Marizan Mubin, Zuwairie Ibrahim, and Sophan Wahyudi Nawawi. "Statistical Analysis for Swarm Intelligence — Simplified." International Journal of Future Computer and Communication 4, no. 3 (2015): 193–97. http://dx.doi.org/10.7763/ijfcc.2015.v4.383.

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Anchan, Tanushri. "Crimes Against Women: A Statistical Analysis." Indian Journal of Applied Research 4, no. 2 (October 1, 2011): 8–9. http://dx.doi.org/10.15373/2249555x/feb2014/35.

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Couturier, Raphaël, and Rubén Pazmiño. "Use of Statistical Implicative Analysis in Complement of Item Analysis." International Journal of Information and Education Technology 6, no. 1 (2016): 39–43. http://dx.doi.org/10.7763/ijiet.2016.v6.655.

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Xue, Xuemei, and Jian Tao. "Statistical Order Convergence and Statistically Relatively Uniform Convergence in Riesz Spaces." Journal of Function Spaces 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/9092136.

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A new concept of statistically e-uniform Cauchy sequences is introduced to study statistical order convergence, statistically relatively uniform convergence, and norm statistical convergence in Riesz spaces. We prove that, for statistically e-uniform Cauchy sequences, these three kinds of convergence for sequences coincide. Moreover, we show that the statistical order convergence and the statistically relatively uniform convergence need not be equivalent. Finally, we prove that, for monotone sequences in Banach lattices, the norm statistical convergence coincides with the weak statistical convergence.
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Marcucci, Mark, and Sam Kachigan. "Statistical Analysis." Technometrics 30, no. 2 (May 1988): 235. http://dx.doi.org/10.2307/1270177.

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MUNRO, BARBARA HAZARD. "Statistical Analysis." Clinical Nurse Specialist 3, no. 3 (1989): 113. http://dx.doi.org/10.1097/00002800-198900330-00004.

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Mnrcucci, Mark. "Statistical Analysis." Technometrics 30, no. 2 (May 1988): 235. http://dx.doi.org/10.1080/00401706.1988.10488379.

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Lewis, Roger J. "Statistical analysis." Annals of Emergency Medicine 20, no. 3 (March 1991): 323. http://dx.doi.org/10.1016/s0196-0644(05)80953-9.

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You might also be interested in the extended bibliographies on the topic 'Statistical analysis' for particular source types:
Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Statistical analysis":

1

Wang, Tao. "Statistical design and analysis of microarray experiments." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1117201363.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains ix, 146 p.; also includes graphics (some col.) Includes bibliographical references (p. 145-146). Available online via OhioLINK's ETD Center
2

Whitehead, Andile. "Statistical-thermodynamical analysis, using Tsallis statistics, in high energy physics." Master's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/13391.

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Includes bibliographical references.
Obtained via the maximisation of a modified entropy, the Tsallis distribution has been used to fit the transverse momentum distributions of identified particles from several high energy experiments. We propose a form of the distribution described in Cleymans and Worku, 2012, and show it to be thermodynamically consistent. Transverse momenta distributions and fits from ALICE, ATLAS, and CMS using both Tsallis and Boltzmann distributions are presented.
3

尹再英 and Choi-ying Wan. "Statistical analysis for capture-recapture experiments in discrete time." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31225287.

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Jung, Andreas. "Statistical analysis of biomedical data." [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=970139543.

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Fabini, Claudia. "Statistical Analysis of Commodity Prices." St. Gallen, 2009. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/04602710001/$FILE/04602710001.pdf.

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Perez, Melo Sergio. "Statistical Analysis of Meteorological Data." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1527.

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Some of the more significant effects of global warming are manifested in the rise of temperatures and the increased intensity of hurricanes. This study analyzed data on Annual, January and July temperatures in Miami in the period spanning from 1949 to 2011; as well as data on central pressure and radii of maximum winds of hurricanes from 1944 to present. Annual Average, Maximum and Minimum Temperatures were found to be increasing with time. Also July Average, Maximum and Minimum Temperatures were found to be increasing with time. On the other hand, no significant trend could be detected for January Average, Maximum and Minimum Temperatures. No significant trend was detected in the central pressures and radii of maximum winds of hurricanes, while the radii of maximum winds for the largest hurricane of the year showed an increasing trend.
7

Zaykin, Dmitri V. "STATISTICAL ANALYSIS OF GENETIC ASSOCIATIONS." NCSU, 1999. http://www.lib.ncsu.edu/theses/available/etd-19990914-043001.

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Zaykin, Dmitri V. Statistical Analysis of Genetic Associations.Advisor: Bruce S. Weir.There is an increasing need for a statistical treatment of geneticdata prompted by recent advances in molecular genetics and moleculartechnology. Study of associations between genes is one of the mostimportant aspects in applications of population genetics theory andstatistical methodology to genetic data. Developments of these methodsare important for conservation biology, experimental populationgenetics, forensic science, and for mapping human disease genes. Overthe next several years, genotypic data will be collected to attemptlocating positions of multiple genes affecting disease phenotype.Adequate statistical methodology is required to analyze thesedata. Special attention should be paid to multiple testing issuesresulting from searching through many genetic markers and high risk offalse associations. In this research we develop theory and methodsneeded to treat some of these problems. We introduce exact conditionaltests for analyzing associations within and between genes in samplesof multilocus genotypes and efficient algorithms to perform them.These tests are formulated for the general case of multiple alleles atarbitrary numbers of loci and lead to multiple testing adjustmentsbased on the closing testing principle, thus providing strongprotection of the family-wise error rate. We discuss an applicationof the closing method to the testing for Hardy-Weinberg equilibriumand computationally efficient shortcuts arising from methods forcombining p-values that allow to deal with large numbers of loci. Wealso discuss efficient Bayesian tests for heterozygote excess anddeficiency, as a special case of testing for Hardy-Weinbergequilibrium, and the frequentist properties of a p-value type ofquantity resulting from them. We further develop new methods forvalidation of experiments and for combining and adjusting independentand correlated p-values and apply them to simulated as well as toactual gene expression data sets. These methods prove to be especiallyuseful in situations with large numbers of statistical tests, such asin whole-genome screens for associations of genetic markers withdisease phenotypes and in analyzing gene expression data obtained fromDNA microarrays.

8

Mihailovici, Manuela. "Statistical analysis of electrocardiogram data." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=22860.

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An overview of the statistical procedures used in the analysis of electrocardiogram traces is presented in this thesis.
The purpose of these procedures is twofold: (i) they may suggest underlying mechanisms that influence heart rate (ii) they may be used as a means of classifying one or more patients into disease categories, by using objective criteria rather than the subjective approaches prevalent in current practice.
In an attempt to apply the methods discussed in this thesis, a selected group of patients was analyzed using spectral analysis.
Lack of information and of control of the patients' activities while they were being monitored precluded the possibility of obtaining definitive results.
9

McClelland, Robyn L. (Robyn Leagh). "Statistical analysis of DNA profiles." Thesis, McGill University, 1994. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=68215.

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DNA profiles have become an extremely important tool in forensic investigations, and a match between a suspect and a crime scene specimen is highly incriminating. Presentation of this evidence in court, however, requires a statistical interpretation, one which reflects the uncertainty in the results due to measurement imprecision and sampling variability. No consensus has been reached about how to quantify this uncertainty, and the literature to date is lacking an objective review of possible methods.
This thesis provides a survey of approaches to statistical analysis of DNA profile data currently in use, as well as proposed methods which seem promising. A comparison of frequentist and Bayesian approaches is made, as well as a careful examination of the assumptions required for each method.
10

Sneddon, Duncan J. M. "Statistical analysis of crystallographic data." Thesis, University of Glasgow, 2010. http://theses.gla.ac.uk/1683/.

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The Cambridge structural database (CSD) is a vast resource for crystallographic information. As of 1st January 2009 there are more than 469,611 crystal structures available in the CSD. This work is centred on a program dSNAP which has been developed at the University of Glasgow. dSNAP is a program that uses statistical methods to group fragments of molecules into groups that have a similar conformation. This work is aimed at applying methods to reduce the number of variables required to describe the geometry of the fragments mined from the CSD. To this end, the geometric definition employed by dSNAP was investigated. The default definition is total geometries which are made up of all angles and all distances, including all non-bonded distances and angles. This geometric definition was investigated in a comparative manner with four other definitions. There were all angles, all distances, bonded angles and distances and bonded angles, distances and torsion angles. These comparisons show that non-bonded information is critical to the formation of groups of fragments with similar conformations. The remainder of this work was focused in reducing the number of variables required to group fragments having similar conformations into distinct groups. Initially a method was developed to calculate the area of triangles between three atoms making up the fragment. This was employed systematically as a means of reducing the total number of variables required to describe the geometry of the fragments. Multivariate statistical methods were also applied with the aim of reducing the number of variables required to describe the geometry of the fragment in a systematic manner. The methods employed were factor analysis and sparse principal components analysis. Both of these methods were used to extract important variables from the original default geometric definition, total geometries. The extracted variables were then used as input for dSNAP and were compared with the original output. Biplots were used to visualise the variables describing the fragments. Biplots are multivariate analogues to scatter plots and are used to visualise how the fragments are related to the variables describing them. Owing to the large number of variables that make up the definition factor analysis was applied to extract the important variables before the biplot was calculated. The biplots give an overview of the correlation matrix and using these plots it is possible to select variables that are influencing the formation of clusters in dSNAP .
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Books on the topic "Statistical analysis":

1

Dairy & Food Industries Supply Association., ed. Statistical analysis. Rockville, Md. (6245 Executive Blvd., Rockville 20852): Dairy & Food Industries Supply Association, 1991.

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Giri, Narayan C. Multivariate statistical analysis. New York: M. Dekker, 1996.

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Johnson, Richard Arnold. Applied multivariate statistical analysis. 4th ed. Upper Saddle River, N.J: Prentice Hall, 1998.

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Johnson, Richard Arnold. Applied multivariate statistical analysis. 2nd ed. Englewood Cliffs, N.J: Prentice-Hall, 1988.

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Johnson, Richard Arnold. Applied multivariate statistical analysis. 5th ed. Upper Saddle River, N.J: Prentice Hall, 2002.

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Giri, Narayan C. Multivariate statistical analysis. 2nd ed. New York: Marcel Dekker, 2004.

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Johnson, Richard Arnold. Applied multivariate statistical analysis. 3rd ed. Englewood Cliffs, N.J: Prentice Hall, 1992.

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Alwan, Layth C. Statistical process analysis. Boston: Irwin/McGraw-Hill, 2000.

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Gras, Régis, Einoshin Suzuki, Fabrice Guillet, and Filippo Spagnolo, eds. Statistical Implicative Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78983-3.

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Serdobolskii, V. Multivariate Statistical Analysis. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-015-9468-4.

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Book chapters on the topic "Statistical analysis":

1

Montejo Berlingen, Javier. "Statistical Analysis." In Springer Theses, 127–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41051-7_6.

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Skiena, Steven S. "Statistical Analysis." In Texts in Computer Science, 121–54. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55444-0_5.

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McCalla, William J. "Statistical Analysis." In The Kluwer International Series in Engineering and Computer Science, 149–60. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-2011-1_8.

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Turner, J. Rick. "Statistical Analysis." In New Drug Development, 99–114. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6418-2_8.

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Peters, B. Guy. "Statistical Analysis." In Comparative Politics, 191–211. London: Macmillan Education UK, 1998. http://dx.doi.org/10.1007/978-1-349-26926-6_9.

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Huang, Gan. "Statistical Analysis." In EEG Signal Processing and Feature Extraction, 335–75. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2_17.

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Davis, Peter, and Roy Lay-Yee. "Statistical Analysis." In Computational Social Sciences, 67–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04786-3_6.

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Yamaguchi, Daiki. "Statistical Analysis." In Search for New Phenomena in Top-Antitop Quarks Final States with Additional Heavy-Flavour Jets with the ATLAS Detector, 209–15. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0932-2_9.

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Lambach, Daniel, Markus Bayer, Felix S. Bethke, Matteo Dressler, and Véronique Dudouet. "Statistical Analysis." In Nonviolent Resistance and Democratic Consolidation, 41–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39371-7_3.

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Minnebo, Jurgen, and Bart Cambré. "Statistical Analysis." In The Palgrave Handbook of Methods for Media Policy Research, 329–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16065-4_19.

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Conference papers on the topic "Statistical analysis":

1

Hahn, A. A. "Statistical data analysis." In The 6th workshop on beam instrumentation. AIP, 1995. http://dx.doi.org/10.1063/1.48038.

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Lee, Seongmin, and Marcel Böhme. "Statistical Reachability Analysis." In ESEC/FSE '23: 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3611643.3616268.

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Swendsen, Robert H. "Acceleration and analysis methods in statistical physics." In Computer-aided statistical physics. AIP, 1992. http://dx.doi.org/10.1063/1.41937.

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Oliveira, Teresa A., Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Symposium: Statistical Modeling and Data Analysis." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637922.

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Kelton, W. David. "Statistical design and analysis." In the 18th conference. New York, New York, USA: ACM Press, 1986. http://dx.doi.org/10.1145/318242.318259.

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Anastassopoulos, Vassilis, and George A. Lampropoulos. "Statistical infrared image analysis." In SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Marija Strojnik and Bjorn F. Andresen. SPIE, 1995. http://dx.doi.org/10.1117/12.221354.

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Fang, Qiang, Jiabi Chen, and Yushan Tan. "Statistical Analysis Of Shearography." In 31st Annual Technical Symposium, edited by Fu-Pen Chiang. SPIE, 1987. http://dx.doi.org/10.1117/12.941678.

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Amin, Chirayu S., Noel Menezes, Kip Killpack, Florentin Dartu, Umakanta Choudhury, Nagib Hakim, and Yehea I. Ismail. "Statistical static timing analysis." In the 42nd annual conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1065579.1065751.

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Visweswariah, Chandu. "Statistical analysis and design." In the 17th symposium. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1016568.1016576.

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Khalaji, Iman, Kaamran Rahemifar, and Abbas Samani. "Statistical finite element analysis." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4650478.

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Reports on the topic "Statistical analysis":

1

Hahn, A. A. Statistical data analysis. Office of Scientific and Technical Information (OSTI), November 1994. http://dx.doi.org/10.2172/10119859.

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Parzen, Emanuel. Quantile Statistical Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, February 1987. http://dx.doi.org/10.21236/ada185181.

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Gruber, Peter. Using ChatGPT for Advanced Data Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/pmgm4wmm7ffer469.

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This workshop teaches researchers, from PhD students to professors, how to use ChatGPT and its Advanced Data Analytics tool for statistical analysis without writing a line of code or even knowing how to use a statistics program. The seminar covers a range of topics from data preparation and descriptive statistics to regression analysis, advanced statistical tests and visualisation. Special emphasis is placed on understanding the workings and limits of AI models such as ChatGPT and reflecting on its implications for data analysis.
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Gruber, Peter H. Using ChatGPT for Advanced Data Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/zqir2dzchct5b469.

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This 2-day workshop teaches researchers, from PhD students to professors, how to use ChatGPT and its Advanced Data Analytics tool for statistical analysis without writing a line of code or even knowing how to use a statistics program. The seminar covers a range of topics from data preparation and descriptive statistics to regression analysis, advanced statistical tests and visualisation. Special emphasis is placed on understanding the workings and limits of AI models such as ChatGPT and reflecting on its implications for data analysis.
5

Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada202273.

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Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada161375.

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Zoltowski, Michael D. Analysis of Statistical Performance Measures. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada414936.

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Zoltowski, Michael D. Analysis of Statistical Performance Measures. Fort Belvoir, VA: Defense Technical Information Center, May 2004. http://dx.doi.org/10.21236/ada422819.

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Cutlip, L. B. Statistical analysis of fluorimeter operation. Office of Scientific and Technical Information (OSTI), January 1991. http://dx.doi.org/10.2172/6256080.

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Manata, Jack P. Tool Life Analysis and Forecasting: 1. Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada266918.

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