Academic literature on the topic 'Multivariat analys'
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Journal articles on the topic "Multivariat analys"
Manawan, Fridly, Gunawan Pamudji Widodo, and Tri Murti Andayani. "COST OF ILLNESS PASIEN KANKER PAYUDARA DI RUMAH SAKIT UMUM PUSAT PROF DR R.D KANDOU MANADO." Jurnal Farmasi Medica/Pharmacy Medical Journal (PMJ) 2, no. 2 (December 2, 2019): 86. http://dx.doi.org/10.35799/pmj.2.2.2019.26532.
Full textFischer, Hagen S. "Multivariate analysis of phenological data." Phytocoenologia 30, no. 3-4 (November 24, 2000): 477–89. http://dx.doi.org/10.1127/phyto/30/2000/477.
Full textBeyreuther, Christoph, Heinz Wohlrabe, Klaus-Jürgen Wolter, and André Köhler. "Multivariate Bohrbildkontrolle und -analyse." ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 103, no. 7-8 (August 18, 2008): 526–30. http://dx.doi.org/10.3139/104.101319.
Full textÁlvarez-Olguín, Gabriela, Natalia Hotait-Salas, and Fidencio Sustaita-Rivera. "Identificación de regiones hidrológicas homogéneas mediante análisis multivariado." Ingeniería, investigación y tecnología 12, no. 3 (July 1, 2011): 277–84. http://dx.doi.org/10.22201/fi.25940732e.2011.12n3.027.
Full textSteinmetz, Holger, Michael Bosnjak, and Rodrigo Isidor. "Meta-analytische Strukturgleichungsmodelle." Psychologische Rundschau 71, no. 2 (April 2020): 111–18. http://dx.doi.org/10.1026/0033-3042/a000483.
Full textEl Wahidi, G. F., M. M. Al Awadi, H. A. Saker, A. El Shaat, and A. Horwich. "Prognostic factors in low grade NHL (A multivariate analys)." European Journal of Cancer 33 (September 1997): S269. http://dx.doi.org/10.1016/s0959-8049(97)86125-4.
Full textKhodasevich, M. A., and D. A. Borisevich. "Identification of Flax Oil by Linear Multivariate Spectral Analys." Journal of Applied Spectroscopy 86, no. 6 (January 2020): 996–99. http://dx.doi.org/10.1007/s10812-020-00929-z.
Full textAhmed, Wondimu. "Motivation and Self-Regulated Learning: A Multivariate Multilevel Analysis." International Journal of Psychology and Educational Studies 4, no. 3 (September 1, 2017): 1–11. http://dx.doi.org/10.17220/ijpes.2017.03.001.
Full textThames, Howard D. "Multivariate Analyse — Verstehen wir, was wir tun?" Medizinische Klinik 94, S2 (April 1999): 8–9. http://dx.doi.org/10.1007/bf03042018.
Full textThames, Howard D. "Multivariate Analyse – Verstehen wir, was wir tun?" coloproctology 22, no. 6 (December 2000): 236–38. http://dx.doi.org/10.1007/pl00001872.
Full textDissertations / Theses on the topic "Multivariat analys"
Nyström, Josefina. "Multivariate non-invasive measurements of skin disorders /." Umeå : Department of Chemistry, Analytical Chemistry, Umeå University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-865.
Full textMagnusson, Anna. "MANOVA I PRAKTIKEN : Studie med multivariat analys i fokus och praktisk tillämpning." Thesis, Karlstads universitet, Handelshögskolan (from 2013), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-79109.
Full textMado, George. "Överföring av NIR metod för kvantitativ analys mellan olika instrument." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255613.
Full textThis diploma work treats the transferring of a method from an Antaris NIR-instrument at AstraZeneca Macclesfield to a Bomem 3600 NIR-instrument at AstraZeneca Gärtuna. The method is used to ensure that by-product form L is below the specified limit value for the product. Quantitative NIR methods are not absolute. They are predictive methods that are based on the inclusion of spectra for several samples that are also analyzed with a reference method. Then, a multivariate computer program is used that relates each spectra to its value with linear regression. To transfer a method between two different NIR-instruments, you need to mathematically transfer the spectra on which the multivariate model is based on, so that they are mathematically analyzed on the instrument that the method is transferred to. The raw data used to build the model is data from the NIR-instrument in Macclesfield and the NIR instrumentin Gärtuna. The transformation of data is performed in Matlab with local centering. The transformed data is then used to build the model in SIMCA 14 which is a software for managing multivariate data models. When the model's correctness is verified, a method is created in SenISS that uses the newly built model to predict Form L in the product. The model is a 2-component PLS model that has the SNV and second derivative spectral filters. These filters contribute to signal correction and reduce the individual differences in spectrum. To verify the correctness of the model, several parameters have been examined, these parameters are: linearity, accuracy, repeatability and selectivity. The linearity is verified by means of a validation set where the model can predict the content of Form L which is then compared with the Form L value determined with reference method. Regression statistics are calculated to verify that the slope includes the value 1 and that the intercept includes the value 0 in a 95% confidence interval. The accuracy is verified by examining how much the predictions deviates from the reference method. The accepted value is 20% RSD. The model has a maximum RSD value of 11.7%. The repeatability is verified by analyzing 2 capsules from 2 different batches 10 times each. Then the pooled standard deviation and a 95% confidence interval is calculated. The selectivity of the model is verified by analyzing capsules with other contents, 5 capsules with different contents is analyzed. The model gives high values of DModX (the distance for each observation to the model) and/or Hotelling's T2 (the distance from the observation to the center of the model) which means that the analyzed capsules with different content deviate from the model.
Holmberg, Pia. "Realtidsanalys av kiselsol." Thesis, KTH, Skolan för kemivetenskap (CHE), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146193.
Full textTraditional measurement techniques are often conducted by mounting instruments inside pipes or are in other ways introducing them to the process. This can be problematic when handling process fluids that are dangerous, cause precipitations or are in other ways hard to handle and might give misleading results. One way to avoid these problems are using the measuring technique Active Acoustic Spectroscopy. Active Acoustic Spectroscopy is conducted by measuring sound signals attenuation through process fluids. The instrument for this is called Acospector and is produced and marketed by the company Acosense. The Acospector is noninvasive and can be installed and maintained without needing to stop the manufacturing process. Through multivariate data analysis of the spectrums from the instrument, vital information is revealed about the process. The project “Real Time Analysis of Silica Sol” was conducted during 10 weeks as a part of Acosense development plan for new application areas. The project evaluated the possibility of real time analysis of specific surface of silica sol at a new position at Akzo Nobel Pulp & Performance Chemicals (former known as Eka Chemicals). The objective of the project was to validate Acosense technique for the new position and specific surface. This was to be done by collecting as good calibration data as possible and create a model which could predict properties of a validation data set. Another objective was to evaluate Akzo Nobel’s lab methods for analyzing specific surface. A large part of the work was to collect calibration samples from the process and analyze them for specific surface, pH, Na2O-content, turbidity, density, viscosity and conductivity. A spectrum was also collected for each calibration sample with the Acospector. These spectrums were then treated with Direct Fourier Transform and analyzed and correlated with lab results by multivariate data analysis. Important parameters for comparable results are carefully and consistent conducted analysis, the pH-electrodes sensitivity and the time that passes between the out take of the sample and analysis. Evaluation of Akzo Nobel’s lab methods for specific surface gave no suggestions to changes in the lab instruction even though the projects results and factory operators’ results deviated. The results from the multivariate analysis of the spectrums and lab results showed after an initial validation with collected data to give a good model for prediction of specific surface. The work to develop the model was done by identifying some observations that deviated from the others. These could after further review be excluded from the data set. It is recommended to conduct further validation of the model with a new data set that hasn’t been used to build the model. When this is done, it would be proved how useful the model is for the application of real time analysis of specific surface at Akzo Nobel. For new installations of the Acospector similar projects, with targeted operations for collecting large amounts of lab samples conducted in the same way, seems to be a good way to ensure good quality of the calibration data. Thoroughly and consistent conducted analyzing methods are crucial to limit the error in the calibration data and produce good results during a short period of time.
Hagqvist, Petter. "Analys av ljudspektroskopisignaler med artificiella neurala eller bayesiska nätverk." Thesis, Linköping University, Department of Physics, Chemistry and Biology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-56429.
Full textVid analys av fluider med akustisk spektroskopi finns ett behov av att finna multivariata metoder för att utifrån akustiska spektra prediktera storheter såsom viskositet och densitet. Användning av artificiella neurala nätverk och bayesiska nätverk för detta syfte utreds genom teoretiska och praktiska undersökningar. Förbehandling och uppdelning av data samt en handfull linjära och olinjära multivariata analysmetoder beskrivs och implementeras. Prediktionsfelen för de olika metoderna jämförs och PLS (Partial Least Squares) framstår som den starkaste kandidaten för att prediktera de sökta storheterna.
When analyzing fluids using acoustic spectrometry there is a need of finding multivariate methods for predicting properties such as viscosity and density from acoustic spectra. The utilization of artificial neural networks and Bayesian networks for this purpose is analyzed through theoretical and practical investigations. Preprocessing and division of data along with a handful of linear and non-linear multivariate methods of analysis are described and implemented. The errors of prediction for the different methods are compared and PLS (Partial Least Squares) appear to be the strongest candidate for predicting the sought-after properties.
Törner, Felix. "Analys av Svartlut med Aktiv Akustisk Spektrometri : Analysis of Black Liquor with Active Acoustic Spectrometry." Thesis, Linköpings universitet, Teknisk biologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-69549.
Full textThere is a need for additional process monitoring techniques in the production of chemical pulp. The possibility of analyzing black liquor by active acoustic spectrometry was investigated by constructing an on-line instrument and installing it in a chemical pulp mill. The results were then analyzed with multivariate methods. Due to unforeseen delays a sufficient amount of data could not be collected, and therefore a definitive conclusion could not be reached. Further work is suggested.
Colliander, Cristian. "Utvärdering av klusteranalytiska metoder i kombination med bibliografisk koppling : en bibliometrisk kartläggning av aktuella forskningsteman inom informationsvetenskap." Thesis, Högskolan i Borås, Institutionen Biblioteks- och informationsvetenskap / Bibliotekshögskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-18588.
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Olsson, Ing-Marie. "Experimental Designs at the Crossroads of Drug Discovery." Doctoral thesis, Umeå : Department of Chemistry, Umeå University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-693.
Full textLovell, Jessica. "Long term organic carbon dynamics in 17 Swedish lakes : The impact of acid deposition and climate change." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-264516.
Full textUnder de senaste tre årtiondena har ett flertal studier baserade på data från nationella miljöövervakningsprogram rapporterat ökande koncentrationer av organiskt kol (TOC) i ytvatten på norra halvklotet inklusive Sverige. Det finns många hypoteser om vad som ligger bakom trenden, till exempel förändringar i markanvändning, minskad atmosfärisk deposition av försurande ämnen och klimatförändringar. Olika förklaringar till vad som ligger bakom den ökande trenden ger konsekvenser vid kvantifiering av förindustriella nivåer och för förutsägelser om framtida koncentrationer, vilket i sin tur ger konsekvenser för vattenklassificering enligt Ramvattendirektivet, vattenförvaltning och dricksvattenberedning. För att kunna analysera de långsiktiga effekterna av industrialisering och klimatförändringar på TOC i ytvatten behövs långa tidsserier av data. Då den svenska miljöövervakningen endast sträcker sig tillbaka till mitten av 1980-talet måste andra tekniker användas för att rekonstruera data. I den här studien har sedimentproppar från 17 sjöar längs en klimat- och depositionsgradient analyserats med visible near infrared spektroskopi (VNIRS), en analysteknik som gör det möjligt att rekonstruera TOC-koncentrationer i ytvatten till förindustriell tid. En tidigare studie med VNIRS visade att TOC-koncentrationer sjönk till följd av försurande nedfall fram till 1980 då nedfallet kraftigt minskade, varefter koncentrationer av TOC började öka. Det noterades i studien att ökningen av TOC efter 1980 varit snabbare än vad minskningen var före 1980 på grund av försurande nedfall. Syftet med den här studien var därför att undersöka hypotesen att den senaste tidens ökning av TOC inte bara berott på minskat nedfall av försurande ämnen, utan även på grund av klimatförändringar. Det var möjligt att undersöka de långsiktiga effekterna av industrialisering och klimatförändringar på TOC i ytvatten genom att analysera rekonstruerad TOC data, klimatdata från början av 1900-talet, modellerad sulfatdepositionsdata och miljöövervakningsdata med uni- och multivariata analysmetoder. Resultaten visade att den senaste tidens ökning av TOC kunde förklaras med både en minskande deposition av försurande ämnen och en ökad nederbörd, medan ökande temperaturer kan ha haft en minskande effekt på TOC. Resultaten visade även att förändringshastigheten av TOC-koncentrationer varit snabbare i de norra, kalla delarna av Sverige och långsammare i de varmare södra. Resultaten indikerar att koncentrationer av TOC kommer att öka till nivåer som aldrig tidigare skådats, vilket är något vattenreningsverk kommer att behöva anpassa sina reningsmetoder till i framtiden.
Abrahamsson, Sandra. "Utformning av mjukvarusensorer för avloppsvatten med multivariata analysmetoder." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-207863.
Full textStudies of real processes are based on measured data. In the past, the amount of available data was very limited. However, with modern technology, the information which is possible to obtain from measurements is more available, which considerably alters the possibility to understand and describe processes. Multivariate analysis is often used when large datasets which contains many variables are evaluated. In this thesis, the multivariate analysis methods PCA (principal component analysis) and PLS (partial least squares projection to latent structures) has been applied to wastewater data collected at Hammarby Sjöstadsverk WWTP (wastewater treatment plant). Wastewater treatment plants are required to monitor and control their systems in order to reduce their environmental impact. With improved knowledge of the processes involved, the impact can be significantly decreased without affecting the plant efficiency. Several variables are easy to measure directly in the water, while other require extensive laboratory analysis. Some of the parameters from the latter category are the contents of phosphorus and nitrogen in the water, both of which are important for the wastewater treatment results. The concentrations of these substances in the inlet water vary during the day and are difficult to monitor properly. The purpose of this study was to investigate whether it is possible, from the more easily measured variables, to obtain information on those which require more extensive analysis. This was done by using multivariate analysis to create models attempting to explain the variation in these variables. The models are commonly referred to as soft sensors, since they don’t actually make use of any physical sensors to measure the relevant variable. Data were collected during the period of March 11 to March 15, 2013 in the wastewater at different stages of the treatment process and a number of multivariate models were created. The result shows that it is possible to obtain information about the variables with PLS models based on easy-to-measure variables. The best created model was the one explaining the concentration of nitrogen in the inlet water.
Books on the topic "Multivariat analys"
E, Anderson Rolph, and Tatham Ronald L, eds. Multivariate data analysiswith readings. 2nd ed. New York: Macmillan, 1987.
Find full textHair, Joseph F. Multivariate data analysis with readings. 2nd ed. New York: Macmillan, 1987.
Find full textTabachnick, Barbara G. Using multivariate statistics. 4th ed. Boston, Mass: Allyn and Bacon, 2001.
Find full textTabachnick, Barbara G. Using multivariate statistics. 2nd ed. New York: HarperCollins, 1989.
Find full textS, Fidell Linda, ed. Using multivariate statistics. 4th ed. Boston MA: Allyn and Bacon, 2001.
Find full textW, Wichern Dean, ed. Applied multivariate statistical analysis. 3rd ed. Englewood Cliffs, N.J: Prentice Hall, 1992.
Find full textW, Wichern Dean, ed. Applied multivariate statistical analysis. 4th ed. Upper Saddle River, N.J: Prentice Hall, 1998.
Find full textW, Wichern Dean, ed. Applied multivariate statistical analysis. 5th ed. Upper Saddle River, N.J: Prentice Hall, 2002.
Find full textJohnson, Richard Arnold. Applied multivariate statistical analysis. 2nd ed. Englewood Cliffs, N.J: Prentice-Hall, 1988.
Find full textBook chapters on the topic "Multivariat analys"
Backhaus, Klaus, Bernd Erichson, Rolf Weiber, and Wulff Plinke. "Conjoint-Analyse." In Multivariate Analysemethoden, 517–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46076-4_10.
Full textHandl, Andreas. "Procrustes-Analyse." In Multivariate Analysemethoden, 175–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-08887-6_7.
Full textBackhaus, Klaus, Bernd Erichson, Wulff Plinke, Christiane Schuchard-Ficher, and Rolf Weiber. "Conjoint-Analyse." In Multivariate Analysemethoden, 345–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-662-08890-6_8.
Full textBackhaus, Klaus, Bernd Erichson, Wulff Plinke, Christiane Schuchard-Ficher, and Rolf Weiber. "Conjoint-Analyse." In Multivariate Analysemethoden, 345–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-662-08891-3_8.
Full textHandl, Andreas. "Procrustes-Analyse." In Multivariate Analysemethoden, 185–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14987-0_7.
Full textBackhaus, Klaus, Bernd Erichson, Wulff Plinke, and Rolf Weiber. "Conjoint-Analyse." In Multivariate Analysemethoden, 497–545. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56655-8_10.
Full textKuhlenkasper, Torben, and Andreas Handl. "Procrustes-Analyse." In Multivariate Analysemethoden, 199–216. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54754-0_7.
Full textBackhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Conjoint-Analyse." In Multivariate Analysemethoden, 577–653. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32425-4_9.
Full textHandl, Andreas, and Torben Kuhlenkasper. "Multivariate Analyse." In Einführung in die Statistik, 129–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56440-0_4.
Full textBackhaus, Klaus, Bernd Erichson, Rolf Weiber, and Wulff Plinke. "Auswahlbasierte Conjoint-Analyse." In Multivariate Analysemethoden, 597–601. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46076-4_14.
Full textConference papers on the topic "Multivariat analys"
Filus, Jerzy K., and Lidia Z. Filus. "Multivariate “pseudodistributions” pattern - applications." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756154.
Full textFilus, Jerzy K., and Lidia Z. Filus. "Multivariate "pseudodistributions" as natural extensions of the multivariate normal density pattern - theory." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756153.
Full textKouvaras, George, and George Kokolakis. "Random Multivariate Multimodal Distributions." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0009.
Full textMiyagawa, Naoki, Hiroshi Teramoto, Chun-Biu Li, Tamiki Komatsuzaki, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Spatial Heterogeneity of Multivariate Dependence." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637776.
Full textNam Anh, Dao. "Multivariate Filter for Saliency." In 2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR). IEEE, 2018. http://dx.doi.org/10.1109/mapr.2018.8337522.
Full textCarnicer, J. M., M. Gasca, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Some Recent Advances in Multivariate Polynomial Interpolation." In Numerical Analysis and Applied Mathematics. AIP, 2007. http://dx.doi.org/10.1063/1.2790271.
Full textGarcía, Jesús E. "Combining multivariate Markov chains." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4912373.
Full textHoffmann, Jan, Klaus Aehlig, and Martin Hofmann. "Multivariate amortized resource analysis." In the 38th annual ACM SIGPLAN-SIGACT symposium. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1926385.1926427.
Full textMexia, João T., Célia Nunes, Manuela M. Oliveira, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Multivariate Application Domains for the Delta Method." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637906.
Full textAkal, Cevdet, and Alexey Lukashov. "Mean value multipoint multivariate Padé approximations." In INTERNATIONAL CONFERENCE ON ANALYSIS AND APPLIED MATHEMATICS (ICAAM 2014). AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4893827.
Full textReports on the topic "Multivariat analys"
Madych, W. R. Multivariate Multiscale Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada229502.
Full textRao, C. R. Applications of Multivariate Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada265250.
Full textDzhangarov, A. I. Multivariate analysis of variance analysis software. Engineering Herald of Don, 2019. http://dx.doi.org/10.18411/0236-8898-1123.
Full textDzhangarov, A. I. Multivariate analysis of variance analysis software. Engineering Herald of Don, 2019. http://dx.doi.org/10.18411/0236-8898-1123-2020.
Full textKrishnaiah, P. R., and C. R. Rao. Multivariate Analysis and Its Application. Fort Belvoir, VA: Defense Technical Information Center, September 1987. http://dx.doi.org/10.21236/ada189983.
Full textRao, C. R. Multivariate Analysis and Its Applications. Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada205585.
Full textAnderson, 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.
Full textAnderson, 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.
Full textAlam, M. Kathleen. Multivariate Analysis of Seismic Field Data. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/8993.
Full textMathew, Thomas. Statistical Inference Problems in Some Multivariate Linear Models with Applications to Multivariate Calibration and Meta-Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1994. http://dx.doi.org/10.21236/ada291125.
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