Relevant bibliographies by topics / Monthly monthly

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Academic literature on the topic 'Monthly monthly'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Monthly monthly"

1

Baird, T. M. "MONTHLY." Chest 153, no. 1 (January 2018): 280–81. http://dx.doi.org/10.1016/j.chest.2017.07.012.

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Pestell, Katharine. "Monthly pill." Trends in Pharmacological Sciences 22, no. 8 (August 2001): 401. http://dx.doi.org/10.1016/s0165-6147(00)01783-1.

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Tse, Dorothy, and Natascha Bruce. "Monthly Matters." Wasafiri 32, no. 3 (July 3, 2017): 2–17. http://dx.doi.org/10.1080/02690055.2017.1322273.

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Daniel J. Velleman. "Monthly Gems." American Mathematical Monthly 121, no. 2 (2014): 164. http://dx.doi.org/10.4169/amer.math.monthly.121.02.164.

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Daniel J. Velleman. "Monthly Gems." American Mathematical Monthly 123, no. 1 (2016): 77. http://dx.doi.org/10.4169/amer.math.monthly.123.1.77.

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Bax, Martin. "Monthly Publication." Developmental Medicine & Child Neurology 32, no. 1 (November 12, 2008): 1–2. http://dx.doi.org/10.1111/j.1469-8749.1990.tb08459.x.

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Campbell, Stuart. "Going monthly." Ultrasound in Obstetrics and Gynecology 5, no. 1 (January 1, 1995): 1–2. http://dx.doi.org/10.1046/j.1469-0705.1995.05010001.x.

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Baldwin, Olive, and Thelma Wilson. "Monthly Mask." Musical Times 126, no. 1712 (October 1985): 583. http://dx.doi.org/10.2307/964905.

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Dando, Toni M., and Stuart Noble. "Once-Monthly Ibandronate." Treatments in Endocrinology 4, no. 6 (2005): 381–87. http://dx.doi.org/10.2165/00024677-200504060-00009.

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Silverman, Stuart. "Once-Monthly Ibandronate." Treatments in Endocrinology 4, no. 6 (2005): 389–90. http://dx.doi.org/10.2165/00024677-200504060-00010.

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Dissertations / Theses on the topic "Monthly monthly"

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Paulus, Clinton. "The Drivers of Monthly IPO Volume." Scholarship @ Claremont, 2011. http://scholarship.claremont.edu/cmc_theses/244.

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This paper looks at the drivers of monthly IPO volume. Different factors have impacted IPO volume in successive time periods since 1960. By using monthly data, there are sufficient data points to draw conclusions about some of the main factors that have continuously had an effect on IPO volume, as well as some variables that have shifted in importance. This paper shows that trailing monthly S&P returns are a potential predictor of future IPO volume, and emphasizes the seasonal boost found at the end of the calendar year. *page numbers do not line up with removed copyright images.
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Wolf, Linden S. "Statistical forecasting of Florida monthly rainfall." Tallahassee, Florida : Florida State University, 2009. http://etd.lib.fsu.edu/theses/available/etd-08242009-163026/.

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Thesis (M.S.)--Florida State University, 2009.
Advisor: Jon E. Ahlquist, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed on May 11, 2010). Document formatted into pages; contains x, 42 pages. Includes bibliographical references.
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Van, Houten John S. "Forecasting aircraft mishaps using monthly maintenance reports." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA286049.

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Lau, Wai-hin, and 劉偉憲. "Stochastic analysis of monthly rainfall in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31210387.

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Eglise, Matthew. "A monthly temperature series for Durham from 1784." Thesis, Durham University, 2003. http://etheses.dur.ac.uk/4044/.

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The geographer and climatologist Gordon Manley produced a monthly temperature reduction for Durham University Observatory from 1801 to extend the series back from the start of meteorological observations at the Observatory in the 1840s. He produced his extended series shortly before he died in 1980, and left it in a provisional state, with limited notes regarding his construction of the monthly means based on temperature observations from sites around the North East of England. Papers that Manley left have been examined to ascertain how he arrived at his reduction, and his methods have been fully documented and analysed. Errors in the derivation of his monthly means have been corrected, and methods that he used refined to improve their accuracy. New techniques for the reduction of means from archived data have been studied. A selection of these were implemented to improve the accuracy of the new series, and further temperature observations that Manley did not use in his version have been evaluated and introduced. Observations from South Cave, near Hull, from 1794, and from Brandsby, near York, from 1784, provide the extension of the record back from 1801. Substantial sets of monthly means from Braithwaite, near Keighley, and Jesmond, near Newcastle upon Tyne, in addition to shorter sets from other sites around the North East of England and the Borders, have been incorporated into the reduction from 1801 to improve the representativeness of Manley's series. The completed series, from January 1784, has been analysed and compared with other temperature series for the British Isles, and the potential for a daily version of the monthly series has been investigated, based upon the data sources currently available.
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Lau, Wai-hin. "Stochastic analysis of monthly rainfall in Hong Kong /." [Hong Kong : University of Hong Kong], 1991. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13028315.

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Kucukbahar, Duygu. "Modeling Monthly Electricity Demand In Turkey For 1990-2006." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609318/index.pdf.

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Factors such as economical development, rapid increase in population and climate change increased electricity demand in Turkey as well as in other countries. Thus, using the correct methods to estimate short, medium and long term electricity demand forms a basis for the countries to develop their energy strategy. In this study, monthly electricity demand of Turkey is estimated. First, the effect of natural gas price and consumption to electricity demand and elasticities are searched with a simple regression model. Although, natural gas is known as a substitute of electricity, natural gas consumption and natural gas over electricity price ratio are found to be nearly inelastic. Second part includes two models and cointegration relation is investigated in nonstationary industry production index, electricity consumption per capita and electricity prices series in the first one. An error correction model is then formed with an additional average temperature variable and 12 months electricity demand is forecasted. In the second one, heating degree-days and cooling degree-days are used instead of the average temperature variable and a new error correction model is formed. The first model performs better than the second one, indicating the seasonality of electricity consumption during a year. The results of both models are also compared with previous studies to investigate the effect of different weather variables.
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Bejarno, Mercedes Andrade. "Monthly average temperature modelling for Valle del Cauca (Colombia)." Thesis, University of Reading, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.515761.

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Wu, Shiying. "Trend analysis of monthly acid rain data - '80 -'86." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/28357.

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Three-way median polish is used to model the monthly concentrations of three kinds of ions in precipitation, namely sulphate, nitrate and hydrogen ions. In contrast to previous findings that the wet acid deposition had decreased from late 70's to early 80's, the results suggest that there is a V-shaped trend for wet acid deposition during the period of 1980 -1986 with the change point around 1983. Strong seasonality is also discovered by the analysis. Nonparametric monotone trend tests are performed on the data collected from 1980 to 1986 and on the data collected from 1983 to 1986 separately. The results are consistent with the findings from the median polish approach. A nonparametric slope estimate of the trend is obtained for each monitoring station. Based on these estimates, the slope estimate is obtained by Kriging interpolation for each integer degree grid point of longitude and latitude across the 48 conterminous states in the United States. Also, a geographical pattern in the data is suggested by hierarchical clustering and by median polishing.
Science, Faculty of
Statistics, Department of
Graduate
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Van, Brabant John D. "A monthly squadron sortie scheduling model for improved combat readiness." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA274985.

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Books on the topic "Monthly monthly"

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Great Britain. Her Majesty's Stationery Office. Monthly catalogue. London: H.M.S.O., 1986.

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Group, Pollok Local History. Monthly magazine. [Glasgow]: Pollok Local History Group, 1989.

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Bank, European Central. Monthly bulletin. Frankfurt: ECB, 1999.

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Fahmida. Adventure monthly. Karachi: City, 1999.

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Bank, European Central. Monthly bulletin. Frankfurt: ECB, 2002.

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"Yachting Monthly's" Sailpower (Yachting Monthly). Adlard Coles Nautical, 2004.

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Orecchia, Silvia, and Upsidedown Cactus Upsidedown Cactus Publishing. Monthly Planner: 12 Months + Notes Pages. Independently Published, 2020.

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NOWLIN. June Monthly Activities (Monthly Activities Series). Teacher Created Resources, 2006.

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NOWLIN. July Monthly Activities (Monthly Activities Series). Teacher Created Resources, 2005.

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Martin, Robert. Monthly Weekly Planner: 2021-2022 2-Year Monthly Planner, 24-Month Calendar. Independently Published, 2020.

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Book chapters on the topic "Monthly monthly"

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Leimbach, Judy, Kathy Leimbach, and Mary Lou Johnson. "Monthly Calendar." In Math Extension Units, 53. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003236474-51.

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Twort, A. C., and J. Gordon Rees. "Interim monthly payments." In Civil Engineering: Supervision and Management, 182–89. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4613-1153-9_11.

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Mattarocci, Gianluca. "Monthly Calendar Anomalies." In Anomalies in the European REITs Market, 60–70. London: Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137390929_6.

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Serrat, Olivier. "Monthly Progress Notes." In Knowledge Solutions, 1029–34. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-0983-9_116.

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Andrews, D. F., and A. M. Herzberg. "Monthly Mean Sunspot Numbers." In Springer Series in Statistics, 67–74. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-5098-2_12.

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Andrews, Stuart. "Reviewers Reviewed: Monthly and Critical." In The British Periodical Press and the French Revolution, 1789–99, 138–51. London: Palgrave Macmillan UK, 2000. http://dx.doi.org/10.1057/9781403932716_11.

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Devonshire-Ellis, Chris, Andy Scott, and Sam Woollard. "Managing the Monthly Payroll Process." In Human Resources in China, 63–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18209-9_3.

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Pruscha, Helmut. "Model and Prediction: Monthly Data." In Statistical Analysis of Climate Series, 67–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32084-2_5.

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"Monthly Charts." In Charting the Major Forex Pairs, 99–103. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119198161.ch13.

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"Monthly Charts." In Charting the Major Forex Pairs, 131–36. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119198161.ch17.

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Conference papers on the topic "Monthly monthly"

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Guwaeder, Abdulmunim, Salah Eltief, Ibrahim Aldaouab, and Ali Madi. "Modeling monthly insulation data." In Nonimaging Optics: Efficient Design for Illumination and Solar Concentration XVII, edited by Roland Winston and Eli Yablonovitch. SPIE, 2020. http://dx.doi.org/10.1117/12.2571736.

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"Evaluation of downscaled POAMA M24 for monthly and 3-monthly streamflow forecasts." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.l11.zheng.

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Ravikumar, P. "Generation of Monthly Average Hourly Ambient Temperatures From Monthly Average Daily Temperature." In ASME 2004 International Solar Energy Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/isec2004-65137.

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A method to generate monthly average hourly ambient temperature values, Tah,n, in terms of the monthly average daily ambient temperature, Ta and latitude, φ is presented here. The present correlations do not require the additional information needed in applying the correlations available in [1,2,3]. The predicted monthly average hourly ambient temperature, Tah,n values have been found to be in agreement with data values within a % rms difference of 0.43 (when Tah,n is in K) and the standard deviation has been found to be 1.243 K, for 56 primary locations of TMY2, comparable to the prediction of Erbs, Klein and Beckman [1].
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Hin, Pooi Ah, Soo Huei Ching, and Pan Wei Yeing. "Prediction of Malaysian monthly GDP." In INNOVATION AND ANALYTICS CONFERENCE AND EXHIBITION (IACE 2015): Proceedings of the 2nd Innovation and Analytics Conference & Exhibition. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4937102.

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Wang, Hsiao-Fan, and Chia-Liang Lai. "Monthly electricity demand forecasting by GANN." In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2016. http://dx.doi.org/10.1109/ieem.2016.7798210.

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Guo, Huifang, Zengchuan Dong, Xin Chen, Xixia Ma, and Peiyan Zhang. "WANN Model for Monthly Runoff Forecast." In 2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop (KAM 2008 Workshop). IEEE, 2008. http://dx.doi.org/10.1109/kamw.2008.4810682.

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"Optimizing Theta Model for Monthly Data." In International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004220501900195.

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Hanson, A., and P. Ghera. "Chief Complaint: Coughing Up Blood Monthly!" In American Thoracic Society 2019 International Conference, May 17-22, 2019 - Dallas, TX. American Thoracic Society, 2019. http://dx.doi.org/10.1164/ajrccm-conference.2019.199.1_meetingabstracts.a4927.

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Venema, V. K. C., O. Mestre, E. Aguilar, I. Auer, J. A. Guijarro, P. Domonkos, G. Vertacnik, et al. "Benchmarking homogenization algorithms for monthly data." In TEMPERATURE: ITS MEASUREMENT AND CONTROL IN SCIENCE AND INDUSTRY, VOLUME 8: Proceedings of the Ninth International Temperature Symposium. AIP, 2013. http://dx.doi.org/10.1063/1.4819690.

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Crivova, Olga. "Mean monthly precipitation of the driest month in the context of climatic changes." In Starea actuală a componentelor de mediu. Institute of Ecology and Geography, Republic of Moldova, 2019. http://dx.doi.org/10.53380/9789975315593.09.

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În articol este descrisă analiza temporală și spațială a cantităților de precipitații din cea mai uscată lună. Se efectuează o clasificare parțială a climei în conformitate cu sistemul Koppen-Trewartha. Schimbările climatice duc la scăderea cantităților de precipitații ale a celei mai uscate luni pentru majoritatea stațiilor meteorologice din Republica Moldova, în timp ce subtipul climatic s-a schimbat de la Ds (clima moderată, vara uscată) la Dw (clima moderată, iarna uscată).
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Reports on the topic "Monthly monthly"

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Smith, Sandra R., Melvin Johnson, Kenneth McClevey, Stephen Calopedis, and Deborah Bolden. Electric power monthly. Office of Scientific and Technical Information (OSTI), May 1992. http://dx.doi.org/10.2172/10156959.

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Author, Not Given. Natual gas monthly. Office of Scientific and Technical Information (OSTI), January 1989. http://dx.doi.org/10.2172/6173664.

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Sparks, Valerie, Frederick M. Helsel, Daniel A. Lucero, and Darielle Dexheimer. ARM/NSA Monthly Report. Office of Scientific and Technical Information (OSTI), November 2016. http://dx.doi.org/10.2172/1331868.

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Author, Not Given. Electric Power Monthly, November 1989. Office of Scientific and Technical Information (OSTI), February 1990. http://dx.doi.org/10.2172/7169049.

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Bailey, David H., and Jonathan M. Borwein. Solution to Monthly Problem 11277. Office of Scientific and Technical Information (OSTI), March 2007. http://dx.doi.org/10.2172/983320.

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Author, Not Given. NTRD Technical Monthly March FY17. Office of Scientific and Technical Information (OSTI), May 2017. http://dx.doi.org/10.2172/1364777.

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Author, Not Given. Electric power monthly, May 1996. Office of Scientific and Technical Information (OSTI), May 1996. http://dx.doi.org/10.2172/549316.

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Author, Not Given. Petroleum marketing monthly, January 1989. Office of Scientific and Technical Information (OSTI), April 1989. http://dx.doi.org/10.2172/6439204.

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Author, Not Given. Petroleum supply monthly, January 1989. Office of Scientific and Technical Information (OSTI), March 1989. http://dx.doi.org/10.2172/6439436.

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Ferrell, J. M. SRTC monthly report, May 1995. Office of Scientific and Technical Information (OSTI), May 1995. http://dx.doi.org/10.2172/211581.

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