ams-514-lec-10-m.nb 1
|
|
- Albert Brown
- 6 years ago
- Views:
Transcription
1 ams--lec-0-m.nb Set Up << JLink` << Miscellaneous`Calendar` << Statistics`MultiDescriptiveStatistics` Downloading Data from a URL This function is based on the technical note on the Wolfram Research website:elli, Brendan, "Using Java to get Stock Data from the Internet into Mathematica", This function reads data from the specified URL, stores it in a temporary file and returns the name of the file for further processing. xgeturl@surl_stringd := JavaBlock@Module@ 8uBuffer, icount, sotmp, ourl, siurl<, InstallJava@ sotmp = OpenTemporary@DOSTextFormat -> False H* Create a temp file. *L ourl = JavaNew@"java.net.URL", surl H* $Failed if not valid. *L siurl = ourl ü openstream@ H* Create a Java input stream. *L If@siUrl === $Failed, Return@$FailedD ubuffer = JavaNew@"@B", 000 H* 000 is an arbitrary size. *L While@ H* Read data from the stream saving it to the temp file. *L HiCount = siurl ü read@ubufferdl > 0, WriteString@soTmp, FromCharacterCode@Take@Val@uBufferD, icountddd siurl ü close@ H* Close the stream. *L Close@soTmpD H* Close the temp file, which also returns the filename. *L D
2 ams--lec-0-m.nb Utility Functions Regularizing field names We want to be able to regularize field names by squeezing out spaces and extraneous (non-letter and non-digit) characters. xregularizename = StringJoin ü Pick@#, HLetterQ@#D»» DigitQ@#DL & êü #D & ü Characters@#D &; The Yahoo!Finance returns CSV data in which one one of the fields is "Adj. Close*". xregularizename@"adj. Close*"D AdjClose Date formatting The dates are returned as strings, e.g., {, 3, 7} is "7-Mar-06". The following set of functions convert these strings into Mathematica date vectors. The first converts 3-letter month names to integers. xmonthnametointeger@ss_stringd := Which@ ss ã "Jan",, ss ã "Feb",, ss ã "Mar", 3, ss ã "Apr",, ss ã "May",, ss ã "Jun", 6, ss ã "Jul", 7, ss ã "Aug", 8, ss ã "Sep", 9, ss ã "Oct", 0, ss ã "Nov",, ss ã "Dec",, True, $Failed These two functions convert a -digit year into a four digit year. The first makes a guest at what point to switch from 900 to 000. The second lets you specify the dividing line.
3 ams--lec-0-m.nb 3 xcompleteyear@iyear_integerd := Module@ 8iDivide<, idivide = Round@00 FractionalPart@First@Date@DD ê 00.D Which@ iyear 900, iyear, iyear 00, If@iYear idivide, iyear + 000, iyear + 900D, True, $Failed D xcompleteyear@iyear_integer, idivide_integerd := Which@ iyear 900, iyear, iyear 00, If@iYear idivide, iyear + 000, iyear + 900D, True, $Failed xcompleteyear@d 00 xcompleteyear@98d 998 xcompleteyear@30d 930 xcompleteyear@30, 0D 030 This function ties it together. xyahoodatetomathematicadate@ss_stringd := Module@ 8vsS<, vss = StringSplit@sS, "-" 8xCompleteYear@ToExpression@vsSP3TDD, xmonthnametointeger@vssptd, ToExpression@vsSPTD<
4 ams--lec-0-m.nb Here's a demonstration. xyahoodatetomathematicadate@"7-mar-06"d 8, 3, 7< Date@D 8,,,, 3, 7.936< This function converts a Mathematica date into a real number. xmathematicadatetoyear@8iy_integer, im_integer, id_integer<d := NA DaysBetween@8iY -,, 3<, 8iY, im, id<d iy + ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ DaysBetween@8iY -,, 3<, 8iY,, 3<D E; Time expressed as a real number is easier to process and plot, e.g., xmathematicadatetoyear@8, 6, 30<D. Extracting subsets of data matrices These are examples of functions that we've used in earlier classes. xextract@vvudata_list, vsfields_list, sfield_string, xselectq_: HTrue &LD := Module@ 8iPosition<, iposition = Position@vsFields, sfield If@ Length@iPositionD ã 0, Null, Select@vvuDataPAll, ipositionp, TT, xselectqd D
5 ams--lec-0-m.nb vsfields_list, vsfieldsubset_list, xselectq_: HTrue &LD := 8vvuBuffer<, vvubuffer = Transpose@xExtract@vvuData, vsfields, #D & êü vsfieldsubset Select@vvuBuffer, xselectqd History Data from Yahoo!Fianance The example in the Technical Note referenced above uses an MSN site. For various reasons,we have decided to use Yahoo!Finance as our data source primarily because its split and dividend adjusted close is in keeping with CRSP standards. Building a URL This function builds a URL which will download daily data in comma-separated-value (CSV) format. xyahoofinancehistoryurl@ ssymbol_string, 8iStartYear_Integer, istartmonth_integer, istartday_integer<, 8iEndYear_Integer, iendmonth_integer, iendday_integer< D := Module@ 8xF, ss<, xf@ss_d := StringTake@"0" <> ToString@sSD, - StringJoin@ " H* Main call *L "s=" <> ssymbol, H* Symbol string *L "&a=" <> xf@istartmonth - D, H* Start month - *L "&b=" <> ToString@iStartDayD, H* Start day *L "&c=" <> ToString@iStartYearD, H* Start year *L "&d=" <> xf@iendmonth - D, H* End month - *L "&e=" <> ToString@iEndDayD, H* End day *L "&f=" <> ToString@iEndYearD, H* End year *L "&g=d", H* Frequency: daily *L "&ignore=.csv" D For example, to download the history for the 3M Corporation (ticker "MMM") for {970,, } to {, 3, 7} we have the following:
6 ams--lec-0-m.nb 6 xyahoofinancehistoryurl@"mmm", 8970,, <, 8, 3, 7<D MMM&a=00&b=&c=970&d=0&e=7&f=&g=d&ignore=.csv Downloading, reading in and processing the data. Given the data specification, as above, this function passes it to xyahoofinancehistoryurl, uses the URL to download the data, reads the data into a local variable and performs some postprocessing to put it into a convenient form for subsequent processing in Mathematica. The function returns a 3-vector consisting of the original arguments, a list of field names, and a data matrix. xgetyahoofinancehistorydata@ssymbol_string, vistart_, viend_d := Module@ 8vvsBuffer, vvudata, vsfields<, vvsbuffer = Import@ xgeturl@ xyahoofinancehistoryurl@ssymbol, vistart, viendd D, "CSV" vsfields = xregularizename êü First@vvsBuffer vvudata = Rest@vvsBuffer vvudatapall, T = xyahoodatetomathematicadate êü vvudatapall, T; vvudata = Sort@vvuData, HFirst@#D.80000, 00, <L < HFirst@#D.80000, 00, <L & 88sSymbol, vistart, viend<, vsfields, vvudata< We'll provide a detailed example in the next section. Example We download the data for the 3M Corporation, (ticker "MMM") for 970 to. Note that dates not containing data (non-trading days, such as New Years Day, and future days) are not returned.
7 ams--lec-0-m.nb 7 8vuCall, vsfields, vvudata< = xgetyahoofinancehistorydata@"mmm", 8970,, <, 8,, 3< Print@"Arguments = ", vucall Print@"Field Names = ", vsfields Print@ "First & Last 3 Data Rows = \n", Join@Take@vvuData, 3D, 8"..."<, Take@vvuData, -3DD Arguments = 8MMM, 8970,, <, 8,, 3<< Field Names = 8Date, Open, High, Low, Close, Volume, AdjClose< First & Last 3 Data Rows = ,, <, 09.6, 0., 09., 09.6, 7000,.<, 88970,, <, 09.7, 0.37, 09.7, 0., 600,.<, 88970,, 6<, 0.,.37, 0.,.37, 76000,.<,..., 88,, 3<, 7.97, 77.9, 7.76, 7.8, , 7.8<, 88,, <, 76., 77.99, 76., 77., , 77.<, 88,, <, 77.89, 77.9, 76.8, 77.6, 7800, 77.6<< 7.8 ÅÅÅÅÅÅÅÅÅÅÅÅÅÅ This statement extracts the "Date" and "AdjClose" columns, converts the dates to real numbers, takes the log of the prices and plots the result.
8 ams--lec-0-m.nb 8 ListPlot@ 8xMathematicaDateToYear@First@#DD, Log@Last@#DD< & êü xextract@vvudata, vsfields, 8"Date", "AdjClose"<D, PlotJoined Ø True, Frame Ø True, FrameLabel Ø 8"date", "log price", StyleForm@"MMM", FontSize Ø 6D, ""<, ImageSize Ø 00 MMM 3 l og pric e date
9 ams--lec-0-m.nb 9 ListPlot@ 8xMathematicaDateToYear@First@#DD, Last@#D< & êü xextract@vvudata, vsfields, 8"Date", "AdjClose"<D, PlotJoined Ø True, Frame Ø True, FrameLabel Ø 8"date", "price", StyleForm@"MMM", FontSize Ø 6D, ""<, ImageSize Ø 00 MMM price date Assignment The list of tickers of the Dow Jones Industrial Average. vsdowjonessymbol = 8"MMM", "AA", "MO", "AXP", "AIG", "T", "BA", "CAT", "C", "KO", "DD", "XOM", "GE", "GM", "HPQ", "HD", "HON", "INTC", "IBM", "JNJ", "JPM", "MCD", "MRK", "MFST", "PFE", "PG", "UTX", "VZ", "WMT", "DIS"< 8MMM, AA, MO, AXP, AIG, T, BA, CAT, C, KO, DD, XOM, GE, GM, HPQ, HD, HON, INTC, IBM, JNJ, JPM, MCD, MRK, MFST, PFE, PG, UTX, VZ, WMT, DIS< Produce a matrix of monthly rates of return (ROR) based on the adjusted closing price. Monthly RORs are defined from month end to month end. Each row of the matrix represents a given date. Each column represents a given stock. In addition, produce two additional vectors: one containing the dates corresponding to the rows of the data matrix and the other containing the tickers corresponding to the columns.
10 ams--lec-0-m.nb 0 As many have noted by now, the ticker for Microsoft is incorrect. 8vuCall, vsfields, vvudata< = xgetyahoofinancehistorydata@"mfst", 8970,, <, 8,, 3< Java::excptn : A Java exception occurred: java.io.filenotfoundexception: yahoo.comêtable.csv?s=mfst&a=00&b=&c=970&d=&e=3&f=&g=d&ignore=.csv at sun.net. getinputstreamhhttpurlconnection.java:798l at java.net.url.openstreamhurl.java:93l at sun.reflect.nativemethodaccessorimpl.invoke0hnative MethodL at sun.reflect.nativemethodaccessorimpl.invokehnativemethodaccessorimpl.java:39l at sun.reflect.delegatingmethodaccessorimpl. invokehdelegatingmethodaccessorimpl.java:l. Import::chtype : First argument $Failed is not a valid channel type. More Import::guess : Could not guess format from channel CSV. More Part::partd : Part specification Import@CSVDPAll, T is longer than depth of object. More Part::pspec : Part specification xyahoodatetomathematicadate@alld is neither an integer nor a list of integers. More Set::partd : Part specification vvudata$833pall, T is longer than depth of object. More The correct ticker is "MSFT". 8vuCall, vsfields, vvudata< = xgetyahoofinancehistorydata@"msft", 8970,, <, 8,, 3< Print@"Arguments = ", vucall Print@"Field Names = ", vsfields Print@ "First & Last 3 Data Rows = \n", Join@Take@vvuData, 3D, 8"..."<, Take@vvuData, -3DD Arguments = 8MSFT, 8970,, <, 8,, 3<< Field Names = 8Date, Open, High, Low, Close, Volume, AdjClose< First & Last 3 Data Rows = , 3, 3<,., 9.,., 8., , 0.08<, 88986, 3, <, 8., 9., 8., 9., , 0.09<, 88986, 3, 7<, 9., 9.7, 9., 9., 33700, 0.09<,..., 88,, 3<, 7.67, 7.73, 7., 7.6, , 7.6<, 88,, <, 7.6, 7.8, 7.7, 7.6, 70000, 7.6<, 88,, <, 7.88, 7.9, 7.6, 7.7, 39300, 7.7<<
11 ams--lec-0-m.nb & êü vsfields, 8"Date", "AdjClose"<D, PlotJoined Ø True, Frame Ø True, FrameLabel Ø 8"date", "log price", FontSize Ø 6D, ""<, ImageSize Ø 00 MSFT 3 l og pric e This is the th ticker and must be corrected in the vector date Position@vsDowJonesSymbol, "MFST"DP, T vsdowjonessymbolp Position@vsDowJonesSymbol, "MFST"DP, T T = "MSFT"; vsdowjonessymbol 8MMM, AA, MO, AXP, AIG, T, BA, CAT, C, KO, DD, XOM, GE, GM, HPQ, HD, HON, INTC, IBM, JNJ, JPM, MCD, MRK, MSFT, PFE, PG, UTX, VZ, WMT, DIS<
12 ams--lec-0-m.nb We can now download the data for all 30. We can simply Map xgetyahoofinancialhistorydata over the list of tickers. Note that dataabstract is a list each element of which is the return of a single xgetyahoofinancialhistorydata call. dataabstract = xgetyahoofinancehistorydata@#, 8970,, <, 8,, 3<D & êü vsdowjonessymbol; We are not sure at this point that all tickers are defined for all periods. This piece of code prints the firsst and last date for each ticker. TableForm@ Join@8#P, T<, 8First@#D, Last@#D< &@First êü #P3TDD & êü dataabstract, TableHeadings Ø 8None, 8"Ticker", "Start", "End"<< D Ticker Start End MMM 970 AA MO AXP AIG T BA CAT C KO DD XOM
13 GE 970 GM 970 HPQ 970 HD HON 970 INTC IBM 970 JNJ 970 JPM MCD 970 MRK 970 MSFT PFE 98 PG 970 UTX 970 VZ 983 WMT 97 8 DIS 970 ams--lec-0-m.nb 3
14 ams--lec-0-m.nb Ultimately, we want only month ending dates and it appears that we can only as far back as {986, 7, 9}. There also isn't much point including anything past {, 3, 3} since we're still in April. dataabstract = xgetyahoofinancehistorydata@#, 8986, 7, 9<, 8, 3, 3<D & êü vsdowjonessymbol; We only need the Date and AdjClose fields, so we'll restructure dataabstract to that effect. dataabstract = 8#PT, #P, 8, 7<T, #P3, All, 8, 7<T< & êü dataabstract; We only need the month ending dates. The Split function can be used to split a list into sublists of "same" elements. Here we employ Split with a function that defines two entries as the same if the month of their dates are the same. Applied to a single element of the top-level dataabstract list this returns sublists of month data. We then simply pull out the last element of each sublist as the last trading day of the month. For@i =, i Length@dataAbstractD, i ++, dataabstractpi, 3T = Last êü Split@dataAbstractPi, 3T, H#P, T ã #P, TL & We want to be very careful and check that all of the dates across all of the stocks are equal. They are. SameQ üü H#P3, All, T & êü dataabstractl True dataabstractpall,, T 8MMM, AA, MO, AXP, AIG, T, BA, CAT, C, KO, DD, XOM, GE, GM, HPQ, HD, HON, INTC, IBM, JNJ, JPM, MCD, MRK, MSFT, PFE, PG, UTX, VZ, WMT, DIS< Finally, we'll build a returns data structure consisting of three elements, The first is a list of dates as row names. The second is a list of tickers as column nams. The third is a matrix of returns over the dates and tickers. vureturns = 9 Rest@dataAbstractP, 3, All, TD, dataabstractpall,, T, Rest@#D ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ - &@Transpose@#P3, All, T & êü dataabstractdd Most@#D =;
15 ams--lec-0-m.nb At the set up section at the top of the notebook we've added a call to <<Statistics`MultiDescriptiveStatistics` which we allow us to compute mean vectors and covariance matrices. vnmean = Mean@vuReturnsP3TD 80.06, , , , , , 0.006, 0.063, , 0.08, , , 0.078, , , , , 0.07, , , 0.009, , , , , 0.03, 0.038, , 0.06, 0.066< mncovariance = CovarianceMatrix@vuReturnsP3TD , , , , , , , , , , 0.009, , , , , , , , , , , , , 0.009, , 0.003, , , , <, , , , , , , 0.007, 0.009, , , , , , , , , , , , 0.00, , , , , , , , , , <, , , , , , , , 0.003, , , , , , , , , 0.00, , , , , , , , 0.008, 0.006, , , , <, , , , 0.006, , , , , , , , , , , , 0.007, , , , , , , , , , , , , , <, , , , , , , , , , , , , , , , , 0.006, , , , , , , , , , , , , <, , , , , , 0.008, , , 0.007, , , , , , , , , , , 0.007, 0.008, , , , , <
16 ams--lec-0-m.nb , , , , <, , 0.007, , , , , , , , , , , , , , , , , , , , , , , 0.003, , , , , <, , 0.009, 0.003, , , , , , , , , , , , , 0.006, , , , , , 0.003, , , , , , , 0.008, <, , , , , , 0.007, , , , , , , , , , , , , , 0.007, , 0.007, , , , , , , , <, , , , , , , , , , , , , , , 0.009, , , , , 0.003, , , , 0.00, , , , , , <, , , , , , , , , , , , , , , , , , , , , , , , 0.003, , , , , , <, , , , , , , , , , , , , , , , , , , 0.008, , , , , , , , , , , <, , , , , , , , , , , , , , 0.003, , , , , 0.007, , , , , , , , , , , <, , , , , , , , , , , , , 0.003, , , , , , , , , , , , , , , , , <, , , , , , , , , , 0.009, , , , , , , , , , , , , , , <
17 ams--lec-0-m.nb , , , 0.008, , 0.00<, , , , 0.007, , , , 0.006, , , , , , , , , 0.000, , , 0.00, , 0.006, , , , , , 0.000, , <, , , 0.00, , 0.006, , , , , , , , , , , 0.000, , , , , , , , , , , , 0.000, , <, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , <, , , , , , , , , , , , 0.008, 0.007, , , , , , , , , , , , , , , , , <, , 0.00, , , , 0.007, , , 0.007, 0.003, , , , , , 0.00, , , , , , , , , , , , , , <, , , , , , 0.008, , , , , , , , , , , , , , , , , , , , 0.006, , , , <, , , , , , , , 0.003, 0.007, , , , , , , 0.006, , , , , , , , 0.000, , , , , , <, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , <, , , , , , , , , , 0.00, 0.003, , , , , , , , , , , 0.000, , , <
18 ams--lec-0-m.nb , 0.00, 0.009, , , <, , , 0.008, , , , 0.003, , , , , , , , , , , , , , , , , , , , , , , <, , , 0.006, , , , , , , , , , , , , , , , , , 0.006, , , 0.00, , 0.003, 0.009, , , <, , , , , , , , , , , , , , , , , , , , , , , , 0.009, , 0.009, , , , <, , , , , , , , , , , , , , , 0.008, 0.000, 0.000, , , , , , , , , , , , , <, , , , , , , , 0.008, , , , , , , , , , , , , , , , , , , , , 0.008, <, , , , , , , , , , , , , , , 0.00, , , , , , , , , , , , , , , << For example, the spectrum of the covariance matrix is svd = SingularValueDecomposition@mnCovariance
19 ams--lec-0-m.nb 9 ListPlot@ Table@svdP, i, it, 8i,, 30<D, PlotRange Ø All, PlotJoined Ø True, ImageSize Ø
AMS Computational Finance
ams-514-lec-13-m.nb 1 AMS 514 - Computational Finance Workshop Robert J. Frey, Research Professor Stony Brook University, Applied Mathematics and Statistics http://www.ams.sunysb.edu/~frey/ frey@ams.sunysb.edu
More informationDYNAMIC VS STATIC AUTOREGRESSIVE MODELS FOR FORECASTING TIME SERIES
DYNAMIC VS STATIC AUTOREGRESSIVE MODELS FOR FORECASTING TIME SERIES Chris Xie Polytechnic Institute New York University (NYU), NY chris.xie@toprenergy.com Phone: 905-93-0577 June, 008 Electronic copy available
More informationGAMINGRE 8/1/ of 7
FYE 09/30/92 JULY 92 0.00 254,550.00 0.00 0 0 0 0 0 0 0 0 0 254,550.00 0.00 0.00 0.00 0.00 254,550.00 AUG 10,616,710.31 5,299.95 845,656.83 84,565.68 61,084.86 23,480.82 339,734.73 135,893.89 67,946.95
More informationESTIMATION AND FORECASTING OF LARGE REALIZED COVARIANCE MATRICES AND PORTFOLIO CHOICE LAURENT A. F. CALLOT
ESTIMATION AND FORECASTING OF LARGE REALIZED COVARIANCE MATRICES AND PORTFOLIO CHOICE LAURENT A. F. CALLOT VU University Amsterdam, The Netherlands, CREATES, and the Tinbergen Institute. ANDERS B. KOCK
More informationMultivariate elliptically contoured stable distributions: theory and estimation
Multivariate elliptically contoured stable distributions: theory and estimation John P. Nolan American University Revised 31 October 6 Abstract Mulitvariate stable distributions with elliptical contours
More informationEstimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice. Laurent A. F. Callot, Anders B. Kock and Marcelo C.
Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice Laurent A. F. Callot, Anders B. Kock and Marcelo C. Medeiros CREATES Research Paper 2014-42 Department of Economics
More informationBayesian Stochastic Volatility (SV) Model with non-gaussian Errors
rrors Bayesian Stochastic Volatility (SV) Model with non-gaussian Errors Seokwoo Lee 1, Hedibert F. Lopes 2 1 Department of Statistics 2 Graduate School of Business University of Chicago May 19, 2008 rrors
More informationStat 206, Week 6: Factor Analysis
Stat 206, Week 6: Factor Analysis James Johndrow 2016-09-24 Introduction Factor analysis aims to explain correlation between a large set (p) of variables in terms of a smaller number (m) of underlying
More informationClimatography of the United States No
Climate Division: AK 5 NWS Call Sign: ANC Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 90 Number of s (3) Jan 22.2 9.3 15.8
More information2.1 Inductive Reasoning Ojectives: I CAN use patterns to make conjectures. I CAN disprove geometric conjectures using counterexamples.
2.1 Inductive Reasoning Ojectives: I CAN use patterns to make conjectures. I CAN disprove geometric conjectures using counterexamples. 1 Inductive Reasoning Most learning occurs through inductive reasoning,
More informationDEPARTMENT OF THE ARMY MILITARY SURFACE DEPLOYMENT AND DISTRIBUTION COMMAND (SDDC) 1 SOLDIER WAY SCOTT AFB, IL 62225
DEPARTMENT OF THE ARMY MILITARY SURFACE DEPLOYMENT AND DISTRIBUTION COMMAND (SDDC) 1 SOLDIER WAY SCOTT AFB, IL 62225 SDDC Operations Special Requirements Branch 1 Soldier Way Scott AFB, IL 62225 April
More informationAnnual Average NYMEX Strip Comparison 7/03/2017
Annual Average NYMEX Strip Comparison 7/03/2017 To Year to Year Oil Price Deck ($/bbl) change Year change 7/3/2017 6/1/2017 5/1/2017 4/3/2017 3/1/2017 2/1/2017-2.7% 2017 Average -10.4% 47.52 48.84 49.58
More informationTime Series Analysis
Time Series Analysis A time series is a sequence of observations made: 1) over a continuous time interval, 2) of successive measurements across that interval, 3) using equal spacing between consecutive
More informationIn this activity, students will compare weather data from to determine if there is a warming trend in their community.
Overview: In this activity, students will compare weather data from 1910-2000 to determine if there is a warming trend in their community. Objectives: The student will: use the Internet to locate scientific
More informationClimatography of the United States No
Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 63.9 39.3 51.6 86 1976 16 56.6 1986 20 1976 2 47.5 1973
More informationClimatography of the United States No
Temperature ( F) Month (1) Min (2) Month(1) Extremes Lowest (2) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 32.8 21.7 27.3 62 1918 1 35.8 1983-24 1950 29 10.5 1979
More informationReconstructing an economic space from a market metric
arxiv:cond-mat/28v [cond-mat.stat-mech] 6 Nov 22 Reconstructing an economic space from a market metric R. Vilela Mendes Grupo de Física Matemática, Av. Gama Pinto 2, 699 Lisboa Codex, Portugal (vilela@cii.fc.ul.pt)
More informationClimatography of the United States No
Climate Division: CA 5 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 56.6 36.5 46.6 81
More informationClimatography of the United States No
Climate Division: CA 4 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 55.6 39.3 47.5 77
More informationClimatography of the United States No
Climate Division: CA 1 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 57.9 38.9 48.4 85
More informationClimatography of the United States No
Climate Division: CA 5 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 44.8 25.4 35.1 72
More informationClimatography of the United States No
Climate Division: CA 4 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 49.4 37.5 43.5 73
More informationClimatography of the United States No
Climate Division: CA 6 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 69.4 46.6 58.0 92
More informationClimatography of the United States No
Climate Division: CA 6 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 67.5 42. 54.8 92 1971
More informationClimatography of the United States No
Climate Division: CA 4 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 58.5 38.8 48.7 79 1962
More informationClimatography of the United States No
Climate Division: CA 1 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 57.8 39.5 48.7 85 1962
More informationClimatography of the United States No
Climate Division: ND 8 NWS Call Sign: BIS Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 21.1 -.6 10.2
More informationSYSTEM BRIEF DAILY SUMMARY
SYSTEM BRIEF DAILY SUMMARY * ANNUAL MaxTemp NEL (MWH) Hr Ending Hr Ending LOAD (PEAK HOURS 7:00 AM TO 10:00 PM MON-SAT) ENERGY (MWH) INCREMENTAL COST DAY DATE Civic TOTAL MAXIMUM @Max MINIMUM @Min FACTOR
More informationClimatography of the United States No
Climate Division: TN 1 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 47.6 24.9 36.3 81
More informationClimatography of the United States No
Climate Division: CA 5 NWS Call Sign: FAT Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 53.6 38.4 46. 78
More informationClimatography of the United States No
Climate Division: CA 6 NWS Call Sign: 1L2 N Lon: 118 3W Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 63.7
More informationClimatography of the United States No
Climate Division: CA 5 NWS Call Sign: BFL Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 56.3 39.3 47.8
More informationCENTRE FOR ECONOMETRIC ANALYSIS
CENTRE FOR ECONOMETRIC ANALYSIS CEA@Cass http://www.cass.city.ac.uk/cea/index.html Cass Business School Faculty of Finance 106 Bunhill Row London EC1Y 8TZ Co-features in Finance: Co-arrivals and Co-jumps
More informationClimatography of the United States No
Climate Division: TN 3 NWS Call Sign: BNA Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 45.6 27.9 36.8
More informationClimatography of the United States No
Climate Division: CA 5 NWS Call Sign: Elevation: 6 Feet Lat: 37 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3)
More informationClimatography of the United States No
Climate Division: CA 4 NWS Call Sign: Elevation: 2 Feet Lat: 37 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3)
More informationClimatography of the United States No
Climate Division: CA 4 NWS Call Sign: Elevation: 13 Feet Lat: 36 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s
More informationClimatography of the United States No
Climate Division: CA 5 NWS Call Sign: Elevation: 1,14 Feet Lat: 36 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of
More informationSYSTEM BRIEF DAILY SUMMARY
SYSTEM BRIEF DAILY SUMMARY * ANNUAL MaxTemp NEL (MWH) Hr Ending Hr Ending LOAD (PEAK HOURS 7:00 AM TO 10:00 PM MON-SAT) ENERGY (MWH) INCREMENTAL COST DAY DATE Civic TOTAL MAXIMUM @Max MINIMUM @Min FACTOR
More informationClimatography of the United States No
Climate Division: CA 6 NWS Call Sign: LAX Elevation: 1 Feet Lat: 33 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of
More informationClimatography of the United States No
Climate Division: CA 6 NWS Call Sign: TOA Elevation: 11 Feet Lat: 33 2W Temperature ( F) Month (1) Min (2) Month(1) Extremes Lowest (2) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number
More informationClimatography of the United States No
No. 2 1971-2 Asheville, North Carolina 2881 COOP ID: 46646 Climate Division: CA 4 NWS Call Sign: 8W Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp
More informationClimatography of the United States No
No. 2 1971-2 Asheville, North Carolina 2881 COOP ID: 4792 Climate Division: CA 6 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65
More informationMonthly Trading Report July 2018
Monthly Trading Report July 218 Figure 1: July 218 (% change over previous month) % Major Market Indicators 2 2 4 USEP Forecasted Demand CCGT/Cogen/Trigen Supply ST Supply Figure 2: Summary of Trading
More informationClimatography of the United States No
No. 2 1971-2 Asheville, North Carolina 2881 COOP ID: 43417 Climate Division: CA 4 NWS Call Sign: N Lon: 121 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1)
More informationClimatography of the United States No
Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) 42.6 24.2 33.4 79 1950 25 44.2 1974-16 1994 19 18.8 1977 977
More informationClimatography of the United States No
No. 2 1971-2 Asheville, North Carolina 2881 COOP ID: 4795 Climate Division: CA 6 NWS Call Sign: SBA Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp
More informationWHEN IS IT EVER GOING TO RAIN? Table of Average Annual Rainfall and Rainfall For Selected Arizona Cities
WHEN IS IT EVER GOING TO RAIN? Table of Average Annual Rainfall and 2001-2002 Rainfall For Selected Arizona Cities Phoenix Tucson Flagstaff Avg. 2001-2002 Avg. 2001-2002 Avg. 2001-2002 October 0.7 0.0
More informationClimatography of the United States No
Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 54.3 40.1 47.2 75 1998 17 53.0 1995 18 1949 11 41.7 1972
More informationClimatography of the United States No
No. 2 1971-2 Asheville, North Carolina 2881 COOP ID: 46175 Climate Division: CA 6 NWS Call Sign: 3L3 Elevation: 1 Feet Lat: 33 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1)
More informationClimatography of the United States No
No. 2 1971-2 Asheville, North Carolina 2881 COOP ID: 42713 Climate Division: CA 7 NWS Call Sign: Elevation: -3 Feet Lat: 32 Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1)
More informationJackson County 2018 Weather Data 67 Years of Weather Data Recorded at the UF/IFAS Marianna North Florida Research and Education Center
Jackson County 2018 Weather Data 67 Years of Weather Data Recorded at the UF/IFAS Marianna North Florida Research and Education Center Doug Mayo Jackson County Extension Director 1952-2008 Rainfall Data
More informationClimatography of the United States No
Climate Division: CA 7 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) 44.5 29.3 36.9 69 1951
More informationClimatography of the United States No
Climate Division: CA 5 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) 59.3 31.5 45.4 80 1976
More informationClimatography of the United States No
Climate Division: CA 2 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) 53.3 37.1 45.2 77 1962
More informationClimatography of the United States No
Climate Division: CA 2 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) 53.3 31.8 42.6 74+ 1975
More informationClimatography of the United States No
Climate Division: CA 2 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) 53.7 32.7 43.2 79 1962
More informationClimatography of the United States No
Climate Division: CA 6 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) 68.5 45.7 57.1 90 1971
More informationClimatography of the United States No
Climate Division: CA 7 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) 56.0 35.7 45.9 83 1975
More informationClimatography of the United States No
Climate Division: CA 7 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) 64.8 45.4 55.1 85 1971
More informationClimatography of the United States No
Climate Division: CA 7 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) 65.5 38.7 52.1 87 1962
More informationClimatography of the United States No
Climate Division: CA 7 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) 58.8 34.3 46.6 81+ 1948
More informationClimatography of the United States No
Climate Division: CA 7 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) 70.4 44.2 57.3 95 1971
More informationClimatography of the United States No
Climate Division: CA 1 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 52.4 35.4 43.9 69
More informationClimatography of the United States No
Climate Division: CA 4 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 61.9 42.0 52.0 89
More informationClimatography of the United States No
Climate Division: CA 2 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 55.6 38.8 47.2 81
More informationClimatography of the United States No
Climate Division: CA 2 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 53.5 37.6 45.6 78
More informationClimatography of the United States No
Climate Division: CA 6 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 66.1 38.3 52.2 91
More informationClimatography of the United States No
Climate Division: CA 1 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 1 Number of s (3) Jan 56.2 4.7 48.5 79 1962
More informationClimatography of the United States No
Climate Division: CA 1 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 50.2 31.2 40.7 65+
More informationClimatography of the United States No
Climate Division: CA 4 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 61.4 33.1 47.3 82+
More informationClimatography of the United States No
Climate Division: CA 6 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 51.5 35.0 43.3 80
More informationClimatography of the United States No
Climate Division: CA 4 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 56.4 43.6 50.0 77
More informationClimatography of the United States No
Climate Division: CA 7 NWS Call Sign: Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 54.4 36.9 45.7 77+
More informationMr. XYZ. Stock Market Trading and Investment Astrology Report. Report Duration: 12 months. Type: Both Stocks and Option. Date: Apr 12, 2011
Mr. XYZ Stock Market Trading and Investment Astrology Report Report Duration: 12 months Type: Both Stocks and Option Date: Apr 12, 2011 KT Astrologer Website: http://www.softwareandfinance.com/magazine/astrology/kt_astrologer.php
More informationJackson County 2013 Weather Data
Jackson County 2013 Weather Data 61 Years of Weather Data Recorded at the UF/IFAS Marianna North Florida Research and Education Center Doug Mayo Jackson County Extension Director 1952-2008 Rainfall Data
More informationClimatography of the United States No
Climate Division: SC 7 NWS Call Sign: CHS Month (1) Min (2) Month(1) Extremes Lowest (2) Temperature ( F) Lowest Month(1) Degree s (1) Base Temp 65 Heating Cooling 100 Number of s (3) Jan 58.9 36.9 47.9
More informationMonthly Trading Report Trading Date: Dec Monthly Trading Report December 2017
Trading Date: Dec 7 Monthly Trading Report December 7 Trading Date: Dec 7 Figure : December 7 (% change over previous month) % Major Market Indicators 5 4 Figure : Summary of Trading Data USEP () Daily
More informationConcepts of Approximate Error: Approximate Error, Absolute Approximate Error, Relative Approximate Error, and Absolute Relative Approximate Error
Concepts of Approximate Error: Approximate Error, Absolute Approximate Error, Relative Approximate Error, and Absolute Relative Approximate Error Sean Rodby, Luke Snyder, Autar Kaw University of South
More informationSTOCK CYCLES FORECAST
Written and Published by: Michael S. Jenkins STOCK CYCLES FORECAST P.O. Box 652 Cathedral Station PO, New York, N.Y. 10025-9998 WWW.StockCyclesForecast.com Volume 28 Issue 8 October 19, 2012 Dow 13,413
More informationExperimental measurements and analysis of the indoor conditions in Italian museum storerooms: a study case
Experimental measurements and analysis of the indoor conditions in Italian museum storerooms: a study case F. Cappelletti, P. Romagnoni and A. Birra University Iuav of Venezia, Italy Aims and methods Analysis
More informationComputing & Telecommunications Services Monthly Report January CaTS Help Desk. Wright State University (937)
January 215 Monthly Report Computing & Telecommunications Services Monthly Report January 215 CaTS Help Desk (937) 775-4827 1-888-775-4827 25 Library Annex helpdesk@wright.edu www.wright.edu/cats/ Last
More informationModeling by the nonlinear stochastic differential equation of the power-law distribution of extreme events in the financial systems
Modeling by the nonlinear stochastic differential equation of the power-law distribution of extreme events in the financial systems Bronislovas Kaulakys with Miglius Alaburda, Vygintas Gontis, A. Kononovicius
More informationChapter 1 Handout: Descriptive Statistics
Preview Chapter 1 Handout: Descriptive Statistics Describing a Single Data Variable o Introduction to Distributions o Measure of the Distribution Center: Mean (Average) o Measures of the Distribution Spread:
More informationComputing & Telecommunications Services
Computing & Telecommunications Services Monthly Report September 214 CaTS Help Desk (937) 775-4827 1-888-775-4827 25 Library Annex helpdesk@wright.edu www.wright.edu/cats/ Table of Contents HEAT Ticket
More informationLesson 8: Variability in a Data Distribution
Classwork Example 1: Comparing Two Distributions Robert s family is planning to move to either New York City or San Francisco. Robert has a cousin in San Francisco and asked her how she likes living in
More informationInvestigating Factors that Influence Climate
Investigating Factors that Influence Climate Description In this lesson* students investigate the climate of a particular latitude and longitude in North America by collecting real data from My NASA Data
More informationBRADSHAW'S RAILWAY GUIDE : accessible copies
BRADSHAW'S RAILWAY GUIDE : accessible copies Y = copy held; YS = copy held with supplement; R = reprint held; I = incomplete copy held; F = fragile copy (not available for general public - access limited);
More informationENGINE SERIAL NUMBERS
ENGINE SERIAL NUMBERS The engine number was also the serial number of the car. Engines were numbered when they were completed, and for the most part went into a chassis within a day or so. However, some
More informationAverage 175, , , , , , ,046 YTD Total 1,098,649 1,509,593 1,868,795 1,418, ,169 1,977,225 2,065,321
AGRICULTURE 01-Agriculture JUL 2,944-4,465 1,783-146 102 AUG 2,753 6,497 5,321 1,233 1,678 744 1,469 SEP - 4,274 4,183 1,596 - - 238 OCT 2,694 - - 1,032 340-276 NOV 1,979-5,822 637 3,221 1,923 1,532 DEC
More informationAverage 175, , , , , , ,940 YTD Total 944,460 1,284,944 1,635,177 1,183, ,954 1,744,134 1,565,640
AGRICULTURE 01-Agriculture JUL 2,944-4,465 1,783-146 102 AUG 2,753 6,497 5,321 1,233 1,678 744 1,469 SEP - 4,274 4,183 1,596 - - 238 OCT 2,694 - - 1,032 340-276 NOV 1,979-5,822 637 3,221 1,923 1,532 DEC
More informationForecasting the Canadian Dollar Exchange Rate Wissam Saleh & Pablo Navarro
Forecasting the Canadian Dollar Exchange Rate Wissam Saleh & Pablo Navarro Research Question: What variables effect the Canadian/US exchange rate? Do energy prices have an effect on the Canadian/US exchange
More informationHow are adding integers and subtracting integers related? Work with a partner. Use integer counters to find 4 2. Remove 2 positive counters.
. How are adding integers and subtracting integers related? ACTIVITY: Work with a partner. Use integer counters to find 4. Start with 4 positive counters. Remove positive counters. What is the total number
More informationS95 INCOME-TESTED ASSISTANCE RECONCILIATION WORKSHEET (V3.1MF)
Welcome! Here's your reconciliation Quick-Start. Please read all five steps before you get started. 1 2 3 Excel 2003? Are you using software other than Microsoft Excel 2003? Say what? Here are the concepts
More informationRecord date Payment date PID element Non-PID element. 08 Sep Oct p p. 01 Dec Jan p 9.85p
2017/18 Record date Payment date PID element Non-PID element 08 Sep 17 06 Oct 17 9.85p - 9.85p 01 Dec 17 05 Jan 18-9.85p 9.85p 09 Mar 18 06 Apr 18 9.85p - 9.85p Final 22 Jun 18 27 Jul 18 14.65p - 14.65p
More informationJackson County 2014 Weather Data
Jackson County 2014 Weather Data 62 Years of Weather Data Recorded at the UF/IFAS Marianna North Florida Research and Education Center Doug Mayo Jackson County Extension Director 1952-2008 Rainfall Data
More informationJayalath Ekanayake Jonas Tappolet Harald Gall Abraham Bernstein. Time variance and defect prediction in software projects: additional figures
Jayalath Ekanayake Jonas Tappolet Harald Gall Abraham Bernstein TECHNICAL REPORT No. IFI-2.4 Time variance and defect prediction in software projects: additional figures 2 University of Zurich Department
More informationSales Analysis User Manual
Sales Analysis User Manual Confidential Information This document contains proprietary and valuable, confidential trade secret information of APPX Software, Inc., Richmond, Virginia Notice of Authorship
More informationIntroduction to S+FinMetrics
2008 An Introduction to S+FinMetrics This workshop will unleash the power of S+FinMetrics module for the econometric modeling and prediction of economic and financial time series. S+FinMetrics library
More informationProfessor Wiston Adrián RISSO, PhD Institute of Economics (IECON), University of the Republic (Uruguay)
Professor Wiston Adrián RISSO, PhD E-mail: arisso@iecon.ccee.edu.uy Institute of Economics (IECON), University of the Republic (Uruguay) A FIRST APPROACH ON TESTING NON-CAUSALITY WITH SYMBOLIC TIME SERIES
More information