When using nutrient accounting methodologies to estimate nutrient balances to watersheds, nations and other regions, it is necessary to estimate the nutrient contents of food and feed commodities, either using commodity trade statistics or making assumptions about the balance between local nutrient food & feed supply and demand. Here, we have assembled several datasets from various American, European, and Asian sources to aid the researcher in estimating nutrient fluxes associated with fluxes of food and livestock feed within a region.
The eyeball method is great if you only have a few forecasts, or youhave lots of time, or you're not interested in quantitative verificationstatistics. Even when you do want statistics, it is a very good idea tolook at the data from time to time!
Basic Statistics By Nagar And Das Pdf
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The following sections give fairly brief descriptions of thestandard verification methods and scores for dichotomous, multi-category,continuous, and probabilistic forecasts. For greater detail and discussion of thestandard methods see Stanski et al. (1989) orone of the excellentbooks on forecast verification and statistics.
A large variety of categorical statistics are computed from theelements in the contingency table to describe particular aspects of forecastperformance. We will illustrate these statistics using a (made-up) example.Suppose a year's worth of official daily rain forecasts andobservations produced the following contingency table:
The advantage of the distributions approach is that the nature ofthe forecast errors can more easily be diagnosed. The disadvantage is thatit is more difficult to condense the results into a single number.There are fewer statistics that summarize the performance of multi-categoryforecasts. However, any multi-category forecast verification can be converted to aseries of K-1 yes/no-type verifications by defining "yes" to be"in category i" or "in category i or higher", and "no"to be "not in categoryi" or "below category i".
Verifying forecasts of continuous variables measures how thevaluesof the forecasts differ from the values of the observations. Thecontinuousverification methods and statistics will be demonstrated on a sampledataset of 10 temperature forecasts taken from Stanskiet al. (1989):
The RMSF is the exponent of the root mean square error ofthelogarithm of the data. The logarithmic transformation is performed tosmooth thedata, reduce the discontinuities, and make the data more robust.Whereas the RMS error can be interpreted as giving a scale to theadditiveerror, i.e., f =o RMS, the RMSFcan be interpreted as giving a scale to the multiplicative error, i.e.,f =o / RMSF (read: "multiplied or dividedby"), which is a more appropriate measure of accuracy for somevariablesand more intuitively meaningful than the RMS log error. In order toavoid assigning skill to trivial forecasts, statistics are onlyaccumulatedwhere either the forecast or observations are within specified limits.Forexample, for visibility verification, the lower and upper limits usedby Golding(1998) were 1 m and 5000 m. When either the forecast or the observationlies within the range but the other is outside the range, then limitsof half the lower limit or double the upper limit are prescribed on theother.
RThe R Project for StatisticalComputing has free software for statistical computing and graphics,including some packages for forecast verification. In particular, the"verification" package provides basic verification functions includingROC plots,attributes (reliability) diagrams,contingency table scores, and more, depending on the type of forecast and observation. It verifiesbinary forecasts versus binary observations,
probabilistic forecasts versus binary observations,
continous forecasts versus continuous observations,
ensemble forecasts versus continuous observations,
spatial forecasts versus spatial observations usingfractions skill score and the intensity-scale method.
Click hereto find out how to get the R forecast verification routines.
StatisticsA New View ofStatistics- Will Hopkins' statistical primer for the health sciencesEngineeringStatistics Handbook - NIST / SEMATECH summaries of statisticalmethodsWeb Interface forStatistics Education (WISE) - teaching resources offered throughIntroductory Statistics courses, especially in the social sciencesDr. Arsham's WebPage - zillions of links to web-based statistics resources
Tutors: Section A1: Dr. Apurba Das, apurba@iitk.ac.in Section A2: Dr. Md. Ramiz Reza, ramiz@iitk.ac.in Section A3: Dr. Vijay Kumar Patel,vkpatel@iitk.ac.in Section B1: 4. Dr. Mukesh Kumar Nagar,mknagar@iitk.ac.in Section B2: Dr. Soumitra Ghara,sghara@iitk.ac.in Section B3: Dr. Kalyan Manna,kalyanm@iitk.ac.in Section C1: Dr. Choiti Bandyopadhyay, choitib@iitk.ac.in Section C2: Dr. Ravitheja Vangala, rvangala@iitk.ac.in Section C3: Dr. Shailesh Trivedi, shailtr@iitk.ac.in Section D1: Dr. Sameer Chavan, chavan@iitk.ac.in Section D2: Dr. Rama Rawat, rrawat@iitk.ac.in Section D3: Dr. G. Santhanam, santhana@iitk.ac.in
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