AksaraLLM/aksara-bahasa-daerah-v1 huggingface dataset:
modél istilah nu biasa dipaké dina sababaraha widang:
 Dina élmu jeung téhnologi, modél hartina abstrak atawa téori nu ngagambarkeun hiji fénoména atawa kajadian; tempo modél. Keur modél dina kajadian husus, tempo modél (ékonomi), modél (makroékonomi), modél statistik jeung modél matematika.
 Dina Neuro-linguistic programming (NLP) modélling refers to the systematic unpacking and sequencing of the conscious and unconscious processes - see Modeling (NLP).
 In science, technology, and leisure for modéls as physical representations see model.
 For modéls in society, art, fashion, and cosmetics, see role model, model or supermodel.
 In world politics, modél refers to the Movement for Democracy in Liberia organization.
 In video games or computer modélling, a model is a fully-3D, polygonal object or character.
 In automobiles, a modél is a type of vehicle sold under a marque
Dina matématik, persamaan paramétrik nyaéta hubungan éksplisit dua atawa leuwih variabel dina watesan hiji atawa leuwih paraméter bébas. Sacara abstraks, rélasi (hubungan) dijelaskeun dina wangun equation (persamaan), jeung nembongkeun ogé bayangan bentuk fungsi, sebutkeun, Rn. Sanajan kitu aya sababaraha hal anu leuwih akurat keur dihartikeun salaku representasi parametrik. Tempo ogé parameter, parametrization, regular parametric representation.

Contona, persamaan parabola sederhana,

,

bisa di-paraméterisasi ku ngagunakeun paraméter bébas , sarta disusun di bentuk

.

Sanajan dina conto saméméhna ampir ngadeukeutan hal trivial, paraméter di handap ieu milu kana circle radius :

.

Ahirna, bentuk géometri penting nu ampir teu mungkin keur ngajelaskeun persamaan tunggal tapi mibanda gambaran nu hadé dina bentuk persamaan parametrik :

nu mana ngajelaskeun kurva tilu-dimensi, helix, nu mibanda radius a sarta ningkat ku  satuan per turn. (Catetan yén persamaan identik dina plane keur circle; kanyataanna, helix ngan sakadar 'a circle whose ends don't have the same z-value'.

Sababaraha gambaran di luhur umumna ditulis salaku

Cara ieu keu ngagambarkeun kurva sacara praktis tur efisien; contono, bisa integrate jeung differentiate saperti watesan kurva. Saterusna, bisa dijelaskeun velocity partikel nuturkeun pola paraméter di handap ieu:

sarta acceleration nyaéta:

Sacara umum, kurva parametrik ngaruppakeun fungsi hiji paraméter bébas (umumnya dilambangkeun ku ). Paraméterisasi permukaan, leuwih ilahar digunakeun saperti dina aplikasi vector calculus saperti Stokes' theorem, mangrupa pungsi 2 paraméter, leuwih ilahar  or .

Conto paraméter permukaan nyaéta (capless) cylinder dibérékeun ku

Kanyataan yén gambaran silinder ieu mangrupa kajadian waktu hiji persamaan nu ditempo ngagambarkeun lingkatan dina bidang, nu diijinkeun keur dicokot tina arbitrary nilai z.

Multivariate calculus
Sir Harold Jeffreys (22 April 1891 - 18 March 1989) nyaéta saurang ahli matématika, statistika, géofisika sarta astronomi.

Anjeunna dibabarkeun Fatfield, County Durham, diajar di Armstrong College di Newcastle-upon-Tyne, bihari mangrupa bagéan ti University of Durham anu saterusna jadi University of Newcastle. Tamat ti dinya indit ka St. John's College, Cambridge sarta jadi pangajar dina taun 1914. Di Cambridge University anjeunna ngawulang matematik, saterusna geofisika sarta ahirna jadi Plumian Professor of Astronomy.

Anjeunna nikah jeung ahli matématika sarta fisika, Bertha Swirles (1903-1999), dina taun 1940 sarta babarengan nulis "Methods of Mathematical Physics".

Salah sahiji kontribusina nyaéta sistem Bayesian pikeun ngadeukeutan probabilitas, sarta idena yén inti planét bumi mangrupa cairan. Anjeunna sinatria dina 1953.

Rujukan 

 Maria Carla Galavotti. "Harold Jeffreys' Probabilistic Epistemology: Between Logicism And Subjectivism". British Journal for the Philosophy of Science, 54(1):43-57 (March 2003). (A review of Jeffreys' approach to probability; includes remarks on R.A. Fisher, Frank P. Ramsey, and Bruno de Finetti. Also online:  )

Tumbu kaluar
 Biografi MacTutor
 Biografi Para Pinunjul Hadiah Vetlesen - Sir Harold Jeffreys 
 Harold Jeffreys salaku Ahli Statistik
Élmu bahan ngawengku bagian-bagian ti kimia, fisika, géologi, ogé biologi nu patali jeung sipat-sipat fisik, kimiawi, atawa biologis rupa-rupa bahan/matéri. Élmu ieu biasana dianggap salaku élmu terapan, sabab sipat-sipat nu diulik biasana dipatalikeun jeung tujuan industrial.

Élmu bahan ngawengku sakabéh opat kelas bahan nu masing-masing boga widang ulikanana séwang-séwangan: (1) logam jeung métalurgi, (2) keramika, semikonduktor, jeung bahan éléktronik lianna, (3) polimér, (4) komposit jeung biomaterial nu bisa asup kana kelas bahan 1 n.k. 4 tadi. Métalurgi jeung keramika mibanda sajarah nu panjang salaku disiplin rékayasa/téhnik, tapi ku sabab élmu nu jadi tatapakan disiplin ieu larap ka sakabéh kelas bahan, mangka élmu bahan dianggap salaku disiplin anu misah.

Élmu bahan aya patalina jeung rékayasa bahan, nu condong museur kana téhnik ngolah (casting, rolling, welding, cangkok ion, kamekaran kristal, thin-film deposition, sintering, glassblowing, jsb.), téhnik analitis (mikroskop éléktron, difraksi sinar-X, kalorimétri, mikroskopi inti (HEFIB) jsb.), rancang bahan, sarta untung/rugina dina produksi industrial.

Sub-widang élmu bahan 
 Nanotéhnologi --- ngulik jeung nyipta bahan hasil rékayasa molekular (nanomaterial) nu strukturna dina ukuran nanométer.
 Kristalografi --- ngulik fisika kristal, kaasup
 defects in crystals, such as grain boundaries and dislocations, and their effects on physical properties;
 téhnik difraksi, pikeun idéntifikasi fase.
 Métalurgi --- ngulik logam
 Keramik
 Biomaterial
 Kimia bahan padet --- ngulik kimia nu lumangsung dina bahan padet
 Fisika bahan padet --- biasana dianggap ulikan pangaruh kuantum na bahan padet, kayaning semikonduksi atawa superkonduksi.
 Mékanika continuum --- ngulik padetan jeung cairan, kalawan asumsi yén éta bahan kaasup bahan kontinyu.

Jejer nu patali dina fisika 
 Térmodinamik, pikeun stabilitas, transformasi, jeung diagram fase.
 Kinetik, dilarapkeun kana laju transformasi jeung difusi.

Tempo ogé 
 Kala téhnologi bahan
 Kristal cair

Élmu alam
Dina matematik, variabel random mangrupa diskrit lamun éta probability distribution ogé disckrit; sebaran probibiliti diskrit lamun satemenna dikarakterisasi ku probability mass function. Mangka X variabel random diskrit lamun

salaku u ngaliwatan susunan tina sakabéh nilai mungkin variabel random X.

Poisson distribution, Bernoulli distribution, sebaran binomial, geometric distribution, sarta negative binomial distribution dipikanyaho salaku sebaran probabiliti diskrit.

Lamun variabel random variable mangrupa diskrit mangka set sakabéh nilai mungkin bisa dianggap finite sarta countably infinite, sabab jumlah nu teu bisa diitung unggal positive real number positip (nagrupakeun wates handap pangleutikna tina sakabéh susunan jumlah parsial) salawasna kasebar nuju takhingga.

Statistika

en:Probability distribution#Discrete probability distribution
Dumasar kasapukan, variabel acak X disebut kontinyu mun fungsi sebaran kumulatifna kontinyu. That is equivalent to saying that Pr[X = a] = 0 for all real numbers a, i.e.: the probability that X attains the value a is zero, for any number a.

While for a discrete random variable one could say that an event with probability zero is impossible, this can not be said in the case of a continuous random variable, because then no value would be possible.

This paradox is solved by réalizing that the probability that X attains a value in an uncountable set (for example an interval) can not be found by adding the probabilities for individual values.

By another convention, the term "continuous random variable" is reserved for random variables that have probability density functions. A random variable with the Cantor distribution is continuous according to the first convention, and according to the second, is neither continuous nor discrete nor a weighted average of continuous and discrete random variables.

In practical applications random variables are often either discrete or continuous.

de:Wahrscheinlichkeitsverteilung#Wichtige stetige Wahrscheinlichkeitsverteilungen
en:Probability distribution#Continuous probability distribution
interval (matematika) 
 interval (waktu)
 interval (tembang)
Fungsi Héaviside step, ngaran nu dipaké keur ngahargaan ka Oliver Heaviside, nyaéta a fungsi diskontinyu nu mana nileyna nyaéta nol keur asupan negatip sarta hiji keur nu séjénna:

The function is used in the mathematics of signal processing to represent a signal that switches on at a specified time and stays switched on indefinitely. Its derivative is formally the Dirac delta function.

It is the cumulative distribution function of a variabel acak which is almost surely 0. (See constant random variable.)

The Héaviside function is the integral of the Dirac delta function. The value of H(0) is of very little importance, since the function is often used within an integral. Some writers give H(0) = 0, some H(0) = 1. H(0) = 0.5 is often used, since it maximizes the symmetry of the function. This makés the definition:

This is sometimes notated by a subscript, as in H0.5(x), which méans that H(0) = 0.5. This notation is also used to represent a completely different concept, however; an x offset:

where c is a positive offset in the x-dimension of the transition from 0 to 1. In other words, H3(x) = H(x − 3) would be zero until x = 3, and would transition to 1 for x > 3. The méaning of the subscript should be given in context.

The question of the Fourier transform of H is an interesting example for the théory of distributions. It is often stated that it is 1/x, up to a normalizing constant. But néar x=0 that cannot be justified: the definition must be given in terms of principal value limit, and the transform isn't to be tréated simply as a function. The corresponding convolution operator is the Hilbert transform.

Often an integral representation of the step function is useful,

in the limit .

Related topics 
 Step response
 Negative and non-negative numbers

Olah sinyal
Dina statistik, nilai-p tina variabel random T nyaéta probabilitas Pr(T ≤ tobserved) nu mana T bakal dianggap leuwih gedé atawa sarua jeung nilai observasi tobserved, dina kayaan null hypothesis dianggap bener.

Dina basa séjén, anggapan yén null hypothesis sederhana ditolak lamun tes statistic T leuwih gedé tinimbang nilai kritis c. Kira-kira dina sabagéan kasus T nu di-observasi sarua jeung tobserved. Mangka nilai-p tina T dina éta kasus probabiliti yén T bakal sarua atawa leuwih ti tobserved.

Sumber Rujukan 
 Sellke, T., M.J. Bayarri, & J. Berger. 2001. Calibration of P-values for Testing Precise Null Hypotheses. Am. Statistician 55: 62-71.
The inverse transform sampling method is a method of sampling a number at
random from any probability distribution, given its cumulative distribution function (cdf).

The problem that the inverse transform sampling method solves is as follows:

Anggap X bakal jadi variabel acak nu mana sebaranna bisa dijelaskeun ku cdf d(x).
We want to generate values of x which are distributed according to this distribution.

Many programming languages have the ability to generate pseudo-randomnumbers which are effectively distributed according to the standard sebaran seragam. If a random variable has that distribution, then the probability of its falling within any subinterval (a, b) of the interval from 0 to 1 is just the length b - a of that subinterval.

The inverse transform sampling method works as follows:
Generate a random number from the standard uniform distribution; call this u.
Compute the value for x which has the associated cdf value u; call this xchosen.
Take xchosen to be the random number drawn from the distribution described by d(x).

The following diagram may help the réader to visualise how the method works:

Sampling using the inverse transform method

Tempo ogé 
The rejection sampling method.
Dina statistik, sebaran frékuénsi nyaéta daptar nilai variabel nu dicokot tina sampel. Daptar ieu ilaharna disusun dumasar kana kuantitas, nembongkeun jumlah waktu unggal nilai nu ngadeukeutan. Conto, lamun 100 urang dibagi kana lima bagéan sikep nilai sarta milih dina skala 1 nu nunjukkeun satuju kacida sarta 5 teu satuju kacida, sebaran frékuénsi bisa jadi saperti di handap ieu:

Tes hipotesa statistik nu kapanggih dina taksiran nilai nu béda sarta sarua antara distribusi frékuénsi. Taksiran ieu kaasup ukuran kacenderungan ka tengah atawa rata-rata, saperti mean jeung median, sarta ukuran variabiliti atawa dispersi statistik, saperti simpangan baku atawa varian.

Sebaran frékuénsi disebut mencong lamun nilai méan jeung median boga nilai nu béda. Kurtosis tina sebaran frékuénsi nyaéta ngumpulna skor dina méan, atawa nembongkeun posisi puncak sebaran sacara grafis—contona dina histogram. Lamun sebaran puncak sebaran leuwih ti sebaran normal disebut leptokurtic; lamun kurang disebutna platykurtic.

Statistika
Baca ogé momen (fisika).

Konsép momen dina matematika diwangun tina konsép momen dina fisika.  Momen ka-n tina fungsi nilai-riil f(x) tina variabel riil nyaéta

Masalah momen nyiar karakterisasi runtuyan { μ′n : n = 1, 2, 3, ... } nu mangrupa runtuyan momen sababaraha fungsi f.

Mun (aksara leutik) f mangrupa fungsi dénsitas probabilitas, mangka nilai integral di luhur disebut momen anu ka-n tina momen probability distribution.  Sacara umum, lamun (hurup gedé) F nyaéta fungsi distribusi kumulatip keur unggal distribusi probabiliti, nu teu mibanda fungsi density, mangka momen ka-n disitribusi probabiliti  ngagunakeun Riemann-Stieltjes integral

di mana X nyaéta variabel random nu mibanda sebaran ieu.

Momen tengah kan distribusi probabiliti variabel random X nyaéta

Central momen kadua nyaéta varian.

The central momemts are cléarly translation-invariant, i.e., the nth central moment of X is the same as that of X + c for any constant c (in this context "constant" méans a non-random quantity).

The first moment and the second and third central moments are linéar in the sense that

and

and

if X and Y are independent random variables (independence is not needed for the first of these three identities; for the second it can be wéakened to uncorrelatedness).

The central moments beyond the third lack this linéarity; in that respect they differ from the cumulants (the first three cumulants are the same as the first moment and the second and third central moments; the higher cumulants have a more complicated relationship with the central moments).

Like the cumulants, the factorial moments of a probability distribution are also polynomial functions of the moments.

Téori probabilitas
Analisis matematik
Dina téori probabilitas jeung statistika, fungsi nu ngahasilkeun momen tina variabel acak X nyaéta

Fungsi ieu ngahasilkeun momen tina sebaran probabilitas:

Lamun X mibanda fungsi dénsitas probabilitas kontinyu f(x) mangka fungsi nu ngahasilkeun momen dibérékeun ku

di mana  mangrupa momen ka-i.

Leupas tina sebaran probabilitas kontinyu atawa heunteuna, fungsi nu ngahasilkeun momen dibérékeun ku integral Riemann-Stieltjes

di mana F nyaéta fungsi sebaran kumulatif.

Konsép nu pakait kaasup fungsi karakteristik, fungsi nu ngahasilkeun probabilitas, jeung fungsi nu ngahasilkeun kumulan. Fungsi nu ngahasilkeun kumulan téh bentuk logaritma tina fungsi nu ngahasilkeun momen.

Statistika
Dina téori probabilitas, téoréma de Finetti nerangkeun kunaon observasi bisa digantikeun mangrupa kondisional bébas tina sababaraha (umumna) kuantitas teu ka-observasi nu mana epistemic probability sebaran bakal ditangtukeun.  Ieu ngaran keur ngahargaan ka Bruno de Finetti.
Régrési Deming nyaéta métodeu  linear regression nu nangtukeun garis panghadéna keur susunan data nu pakait. Régrési ieu béda jeung régrési liniér dina ngitung kasalahan boh dina sumbu-'x' jeung sumbu-'y'.

Keur conto, lamun data dina sumbu-x dipikanyaho henteu mibanda kasalahan, tapi dina sumbu-y mibanda kasalahan, (saperti dina estimasi populasi (y), dina waktu nu dipikanyaho (x)), mangka bisa maké régrési liniér sederhana.

Lamun duanana susunan data mibanda kasalahan, conto hubungan antara konsentrasi dua material dina getih, régrési Deming méré  hasil nu leuwih hadé.

Hal anu teu nguntungkeun dina régrési Deming nyaéta sacara matematis leuwih kompleks waktu dipaké ngitung. Ieu hartina dina ngalakukeun itungan, dina tulisan séjén, atawa dina nulis rumus keur spreadsheet, leuwih hese.
Rujukan
 Coping with Changing Demographics 
 Foot, D. (1996), Boom, Bust and Echo: How to profit from the coming demographic shift, MacFarlane Walter & Rose, Toronto, 1996, ISBN 0-921912-97-8
 Dychtwald, K. (1989), Age Wave: The challenges and opportunities of an aging North America, St. Martins Press, New York, 1989, ISBN 0-87477-441-1
 Sterling, W. & White, S. (1998), Boomernomics: The future of your money in the upcoming generational warfare, The Library of Contemporary Thought (Ballantine Publishing), New York, 1998, ISBN 0-345-42583-9
 Schuman, H. and Scott, J. (1989), Generations and collective memories, American Psychological Review, vol. 54, 1989, pp. 359–81
 Meredith, G., Schewe, C., and Haim, A. (2002), Managing by defining moments: Innovative strategies for motivating 5 very different generational cohorts, Hungry Minds Inc., New York, 2002, ISBN 0-7645-5412-3

Demografi
Di antara data-data nu perlu dipimilik ku pamingpin nasional nyaéta statistika démografi populasina. Catetan kalahiran, kapapaténan, kawin, imigrasi jeung émigrasi, sarta sénsus régulér populasina bisa nyadiakeun informasi nu jadi konci dina nyieun kaputusan ngeunaan kawijakan nasional.

Ringkesan data nu mangpaat pisan di antarana piramida populasi, nu nyadiakeun data ngeunaan sebaran kelamin jeung umur populasi dina wanda grafik nu basajan.

Ringkesan séjénna disebut tabel kahirupan. Tina cohort jalma-jalma nu lahir dina taun nu sarua, urang bisa nyusud sarta ngagambarkeun pangalaman hirup ti lahir nepi ka pupusna. For a given cohort, the proportion expected to survive éach yéar (or decade in an abridged life table) is presented in tabular or graphical form.

Nisbah lalaki jeung awéwé dumasar umur nunjukkeun balukar tina béda laju mortalitasna.

Sumber rujukan
 Demographic statistics, ti Wikipédia basa Inggris, disalin ping 3 Séptémber 2004.

Tumbu kaluar
 Keur conto, tempo laporan statistik dina  Malta 
 Population pyramid
 Life Table 

Statistika
Dina probabilitas jeung statistika,
éstimasi dénsitas hartina konstruksi tina éstimasi dumasar kana data observasi, nu teu kaobservasi dina kaayaan fungsi probabilitas dénsitas. Fungsi dénsitas nu teu kaobservasi dianggap salaku dénsitas dina sebaran populasi nu gedé; data umumna dianggap salaku sampel acak tina populasi.

Aya sababaraha cara nu bisa dipaké keur ngitung éstimasi dénsitas, kaasup jandéla Parzen sarta téhnik rentang data clustering.

Ieu mangrupa gambaran éstimasi dénsitas.

Rujukan 
 Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. New York: Springer, 2001. ISBN 0-387-95284-5. (See Chapter 6.)
 D.W. Scott. Multivariate Density Estimation. Theory, Practice and Visualization. New York: Wiley, 1992.
 B.W. Silverman. Density Estimation. London: Chapman and Hall, 1986.

Probabilitas
Dina judi Dutch book atawa lock nyaéta susunan ganjil sartataruhan nu pasti meunang, teu pakait jeung hasil tina judi.  Kaayaan ieu pakait jeung kamungkinan ku ayana kaahengan nu teu asup akal.

One example is where a bookmaker has offered odds and attracted bets which makes the result irrelevant; in this case the implied probabilities will add up to a number gréater than 1.

In this case, whichever horse wins, the bookmaker will pay out 200 (including returning the winning stake) and so maké a profit of 10.

If for some réason Horse 4 was withdrawn and the bookmaker was foolish enough not to adjust the other odds, the implied probabilities would add up to 0.95 and a gambler could lock in a profit of 10, by betting 100, 50 and 40 on the remaining three horses respectively.

Other forms of Dutch books can exist when incoherent odds are offered on exotic bets such as forecasting the order in which horses will finish.  With competitive fixed-odds gambling being offered electronically, gamblers can sometimes créate a Dutch book by selecting the best odds from different bookmakers, in effect by undertaking an arbitrage operation. The bookmakers should réact by adjusting the offered odds in the light of demand, so as to remove the potential profit.

In Bayesian probability, Frank Ramsey and Bruno de Finetti required personal degrees of belief to be coherent so that a Dutch book could not be made against them, whichever way bets were made; in other words their degrees of belief had to satisfy the axioms of probability.

Tiori Kira-kira
Loba ahli statistik nyoko hiji panempo pilihan dina debat keur ngadukung interpretasi frekuensi tina probabiliti sarta ngadukung probabiliti personal. Hal nu penting tina ieu pilihan nyaéta, "lamun leumpang siga entog...". kabéhanna hayang mastikeun yén unggal kaahengan bakal nurut kana tiori aksioma probabiliti keur alesan interpretasi. Kadangkala maranéhanna bakal maké métodeu  nu pakait jeung métodeu  frekuensi, kadangkala ogé maké métodeu  Bayes.

Sababaraha ahli filosofi probabiliti ngadukung probabiliti pilihan salaku prinsip tinimbang tina panempo pragmatis. Alesanna yén beda interpretasi dina probabiliti mangrupa hal nu beda tina hiji hal jeung hal séjénna, dipaké dina kontek nu beda. Conto, interpretasi pobabiliti maké métodeu  frekuensi bisa ditarima keur percobaan nu berulang, interpretasi logika probabiliti dipaké dina kasus tunggal nu mibanda informasi awal nu husus, sarta interpretasi probabiliti personal dipaké keur kaayaan nu teu pasti. Hal ieu teu bisa disebutkeun yén nu nganalisa teu konsisten.

Statistika
Kasalahan ékologis nyaéta kasalahan nu remen kajadian dina interpretasi data statistik, lantaran kacindekan ngeunaan kaayaan individu dumasar kana kumpulan komponén statistik tina grup tempat ayana individu éta.

Conto: Betty meunang peunteun 'A' di sakolana. Di sakola Simon peunteun rata-rata matematikana 'B'. Kasalahan ekologis pikeun ngajadi lamun urang nyimpulkeun atawa nyindekkeun yén Betty leuwih pinter matematika tinimbang Simon. Kasimpulan anu teu tangtu bener.

Tempo ogé prosecutor's fallacy.

Kasalahan logika
Dina statistik, efisiensi nyaéta salah salah ukuran keur estimator.

Efisiensi dina unbiased statistik T dihartikeun salaku

nu mana I(θ) nyaéta sampel informasi Fisher.  Mangka e(T) nyaéta varian mininum nu mungkin keur éstimator unbiased dibagi ku varian sabenerna.  Dina Cramér-Rao inequality ngabuktikeun yén e(T) ≤ 1.

Estimator efisien
Lamun estimator tina paraméter, , sarua jeung  keur sakabéh nilai paraméter, maka ieu éstimator disebut efisien.

Hartina, éstimator sarua jeung Cramér-Rao inequality .

Saterusna, éstimator efisien lamun unbiased mangrupa minimum variance unbiased estimator oge.
Hal ieu sabab éstimator efisien mertahankeun kasaruan dina Cramér-Rao inequality keur sakabéh nilai paraméter, nu mana hartina nepi ka nilai varian minimum keur sakabéh paraméter (definisi MVU éstimator).
Kudu dicatet yén éstimator MVU teu salawasna efisien sabab "minimum" teu salawasna kajadian dina Cramér-Rao inequality.

Asimtot efisiensi
Keur sababaraha estimator bisa nepi ka asimtot efisiensi tur disebutna  éstimator efisien asimtot.
Kasus ieu kapanggih dina éstimator maximum likelihood atawa keur éstimator nu sarua jeung asimtot Cramér-Rao inequality.

Conto

Anggap ukuran sampel n dicokot tina méan μ jeung varian sebaran normal.

Sample mean  tina runtuyan sample 
, dirumuskeun ku

mangka gedéna varian 1/n.  Ieu sarua jeung informasi Fisher bulak balik tina sampel (katempo jéntré tina definisi) mangka ngagunakeun Cramér-Rao inequality, sampel méan nyaéta efisien dina efisiensi ieu sarua jeung hiji.

Ayeuna tempo sample median.  Hal ieu mangrupa éstimator nu unbiased jeung consistent keur μ.  Keur n nu gedé, sampel median ngadeukeutan kana sebaran normal nu mana méan μ jeung varian  (contona, ).  Mangka efisiensi na jadi 2/π, atawa kira-kira 64%.  Catetan, efisiensi ieu ngarupakan efisiensi asimtot --- nyaéta limit efisiensi lamun ukuran n nuju ka tak terhingga. Keur lobana n anu terhingga bakal mibanda efisiensi nu leuwih gedé (conto, keus ukuran sample 3 mangka efisiensina kira-kira 74%).

Loba paniliti nu milih sampel median keur éstimator méan,
sabab nguranganna efisiensi leuwih katembong keur sampel nu acak dina watesan henteu sensitifna data outlier.

Efisiensi relatif

Lamun  jeung  mangrupa éstimators keur parameter θ, mangka penilti satuju yén T "leuwih efisien" batan T ′ lamun: 
(i) mean kuadrat kasalahan (MSE) leuwih leutik tinimbang sababaraha nilai , jeung
(ii) MSE teu leuwih gedé ti T ′ keur sakabéh nilai θ.

Sacara formal, 

keur sakabéh , nu pakait raket di unggal tempat.

Efisiensi relatif bisa ogé dihartikeun

Sanajan e sacara umum fungsi θ, dina sababaraha kasus henteu pakait; mangka kitu, lamun e kurang ti hiji nembongkeun yén T leuwih hadé dipaké, henteu gumantung kana nilai θ sabenerna.

Rujukan
ditarjamahkeun tina Wikipedia Basa Inggris, Juli 2006

Statistika
Dina statistik, konsep kasalahan jeung sesa gampang silih bingungkeun.

Kasalahan nyaéta hal nu salah; kasalahan nyaéta loba beda nu kapanggih tina nilai ekspektasi; saterusna bakal dumasar kana sakabéh populasi tina satuan statistik nu dipilih sacara acak. Nilai ekspektasi, nilai rata-rata tina populasi nu ditempo, sacara tipikil teu katempo. Lamun rata-rata jangkung lalaki nu umurna 21 taun nyaéta 5 kaki 9 inci, sarta kapilih sacara acak lalaki nu jangkungna 5 kaki 11 inci, mangka "kasalahan" nyaéta 2 inci; lamun sacara acak kapilih lalaki nu jangkungna 5 kaki 7 inci, mangka "kasalahan" nyaéta −2 inci.  Istilah ieu diwangun tina ukuran kasalahan acak dina astronomi. Saperti dina ukuran jangkung lalaki tadi nu mana diukur tina rata-rata populasi, mangka beda antara jangkung lalaki jeung ukuran rata-rata mangrupa ukuran kasalahan.

Sesa nyaéta estimasi nu katempo tina kasalahan nu teu katempo. Pasualan nu gampang dina sampel acak jangkung lalaki n  nyaéta ukuranna. Rata-rata sampel dipaké keur ngira-ngira rata-rata populasi. Mangka:

Beda antara unggal jangkung lalaki jeung rata-rata populasi nu teu katempo disebut kasalahan, sarta
Beda antara jangkung lalaki jeung rata-rata sampel disebut sesa.

Sesa bisa katempo; kasalahan henteu.

Catetan: jumlah sesa sarua jeung nol, mangka sesa henteu mandiri.  Jumlah kasalahan teu sarua jeung nol; kasalahan mangrupa variabel acak mandiri lamun dicokot tina populasi nu mandiri.

Kasalahan ilaharna masing-masing mandiri; sesa ilaharna henteu mandiri tinu sejen.

Conto

Lamun urang nganggap populasi nu kasebar normal mibanda méan μ sarta simpangan baku σ, sarta individu nu dipilih bébas, mangka

sarta sampel méan mangrupa sebaran variabel random:

Mangka kasalahan nyaéta

sedengkeun sesa nyaéta

(Saperti nu ilahar dipaké, tanda "topi" di handapeun aksara ε nunjukkeun estimasi observasi tina kuantitas nu teu kaobservasi disebut ε.)

Jumlah kuadrat kasalahan, dibagi ku σ2, mibanda sebaran chi-kuadrat mibanda n tingkat kebébasan:

Nilai ieu teu bisa kapanggih, sedengkeun kuadrat jumlah sesa, bisa kapanggih. Bagi ieu jumlah ku σ2 nu mibanda sebaran chi-kuadrat ngan ku n − 1 tingkat kabébasan:

Hal ieu bisa ditandaan yén variabel acak jeung sampel méan nembongkeun kamandirian sewang-sewangan. Ieu kanyataan sebaran normal jeung chi-kuadrat nembongkeun yén itungan bentuk dasar di luhur dumasar kana interval kapercayaan dina sebaran-t student.  Batalna σ tina nu ngabagi jeung nu dibagi dina éta itungan nembongkeun yén aya kateujelasan nu nyababkeun ayana anggapan yén σ2 teu méré  pangaruh nanaon.

Tempo ogé

Studentized residual
Rujukan 

 Arthur Dempster, Nan Laird, and Donald Rubin. "Maximum likelihood from incomplete data via the EM algorithm". Journal of the Royal Statistical Society, Series B, 39(1):1–38, 1977.
 Radford Néal, Géoffrey Hinton. "A view of the EM algorithm that justifies incremental, sparse, and other variants". In Michael I. Jordan (editor), Learning in Graphical Models pp 355–368. Cambridge, MA: MIT Press, 1999.

Statistika
Kuadrat Graeco-Latin n×n nyaéta tabel nu unggal sel-na mibanda pasangan simbol, susunan simbol tina unggal dua set unsur n. éach pair occurs exactly once in the table. éach symbol in the two, not necessarily distinct, sets occurs exactly once in éach row and exactly once in éach column. Kuadrat Graeco-Latin dipaké dina desain percobaan.

A 4×4 Graeco-Latin square on the sets {A, B, C, D} and {α, β, γ, δ} is:

The tabular arrangements of {A, B, C, D} (Latin characters) alone and {α, β, γ, δ} (Greek characters) alone éach forms a Latin square. éach pair from the two sets (i.e. every element of their cartesian product) occurs exactly once and we say that the two Latin squares are orthogonal.

History

In the 1780s, Leonard Euler  demonstrated methods for constructing Graeco-Latin squares where n is odd or a multiple of 4. He further proved that no 2×2 square exists and conjectured that none existed for n=4k+2, where k is a natural number.

In 1901, Gaston Tarry demonstrated that there was no 6×6 square by enumerating all the possible arrangements of symbols. However, in 1959, Parker, Bose and Shrikhande constructed a 10×10 square.

In 1978, the French writer Georges Perec used the 10×10 square (believed then to be the only one possible) for the structure of constraints underlying his novel Life: A User's Manual.

Tempo ogé
 Latin square

Matematika
Dina tiori probabiliti sarta statistik, modél grapik (GM) ngagambarkeun kabebasan antara variabel random maké grafik nu mana unggal variabel random mangrupa hiji titik.

Dina kasus sederhana, stuktur jaringan modél nyaéta directed acyclic graph (DAG).  Mangka GM ngagambarkeun faktorisasi gabungan probabiliti sakabéh variabel random.  Leuwih pastina, lamun kajadian

X1, ..., Xn,

mangka gabungan probabiliti

P(X1, ..., Xn),

sarua jeung hasil kondisional probabiliti

P(Xi | parents of Xi) for i = 1,...,n.

Dina basa séjén, faktor sebaran gabungan kana hasil kali sebaran kondisional.  Struktur grafik nunjukkeun kabébasan langsung antara variabel.  Unggal dua titik nu pakait henteu nurun/naek mangrupa kondisional bebas nu méré keun nilai keur indungna.
Dina statistika, sekuen Halton leuwih dipikanyaho minangka sekuen kuasi-random, mimiti diwanohkeun dina taun 1960 minangka alternatip tina sekuan wilangan pseudo-random.  Sekuen ieu dirarancang utamana keur dipaké dina simulasi integral Monte Carlo nu teu mibanda bentuk-raket dina usaha keur ngurangan varian.

Aslina, sekuen Halton dirarancang dumasa kana modél deterministik nu maké wilangan prima minangka dasarna. Sekuen Halton hiji-dimesi dumasar kana wilangan prima p (≥ 2) ngeusi dina rohangan 0-1 ku ngabagi kana bagéan p, sarta sacar sistematis dieusikan kana rohangan nu kosong, maka siklus panjang p nu disimpen unggal gambar dina unggal segmen. Panjang sekuen Halton N saterusna ngandung panjang siklus mimiti p-1, ditambahkeun kana [N-(p-1)]DIV[p] panjang siklus “lengkep” p, sarta, iwal dina kasus di mana (N+1)MOD(p)=0, ogé dina panjang siklus ahir “teu lengkep” final (N+1)MOD(p).

Sacara resmi, φp (i), unsur ka-i dina sekuen Halton dumasar kana wilangan prima p, ditangtukeun ku nyokot inverse interger radikal i dina dasar p ku ngagambarkeun ngaliwatan titik radikal, saperti:

di mana nilai b0(i), ..., bL(i)  ditangtukeun ku ngarengsekeun:

Unggal sekuen Halton standar diwangun dina dimensi handap, masalah pakait dicatet antara sekuen nu di-generate tina wilangan prima pangluhurna. Pasti, ieu nembongkeun risiko serius salila estimasi integral dimensi-luhur (misalna, -- pamilihan model spasial, saperti lokasi atawa rute).  Dina usaha keur nganyahokeun paripolah ieu, sababaraha métodeu  diusulkeun;  salah sahiji solusi nu kawentar nyaéta sekuen Halto (make koefisien permutasi nu ditangtukeun samemehna digunakeun dina ngawangun sekuen standar).

Statistika
Rasio bencana dina analisa survival nyaéta kasimpulan tina beda antara dua kurva survival, ngagambarkeun nguranganna resiko kematian dina babandingan kontrol pencegahan, salila waktu nu diperhatikeun. Ieu mangrupa bentuk resiko relatip. modél proporsi regresi bencana (oge disebut modél regeresi Cox) nganggep yén resiko relatip kematian antara pencegahan jeung kontrol mangrupa hal nu angger dina interval waktu. modél ieu dipaké salaku algoritma komputer keur nyimpulkeun risiko kematian relatip kana interval waktu dina hiji gambar.

nu mana O nyaéta jumlah observasi kematian dina grup husus, sarta E jumlah ekspektasi kematian, dumasar kana ieu kaayaan, tapi teu nyokot itungan tina unggal ekspektasi pencegahan.

Tumbu kaluar
 http://www.graphpad.com/www/book/compsurv.htm

Rujukan
 Altman, DG.  Practical Statistics for Medical Reséarch. Chapman & Hall. London, 1991.  ISBN 0412276305.  pp383–4.

Statistika
Kurva Hubbert, ngaran nu dipaké keur ngahargaan ka géofisikis M. King Hubbert, mangrupa derivatif tina kurva logistik.

Salah sahiji conto kurva Hubbert nyaéta:

Kurva Hubbert raket jeung susunan bentuk, sanajan asalna béda, probability density function tina sebaran normal. Kurva ieu asalna tina mode laju éxtraxi minyak bumi. Dumasar kana ieu modél, laju produksi minyak ditangtukeun ku laju sumur minyak anyar nu kapanggih; "penclut Hubbert (Hubbert peak]]" dina laju éxtraxi minyak bakal nuturkeun ku nurunna produksi minyak nepi ka béakna.

Catetan: keur diskusi leuwih jéntré ngeunaan minyak bumi, tempo dina artikel penclut Hubbert.

Matematika
Independent components analysis (ICA) is a mathematical method for separating a signal into its most probable additive subcomponent supposing the statistical independence of the source signals. This assumption is correct in most cases so the blind ICA separation of a mixed signal gives very good results. It is also used for signals that are not supposed to be generated by a mixing for analysis purposes.

The statistical method revéal hidden factors (independent components), presuming their non-gaussianity.

It uses principal components analysis as a first step.
Apply whitening of the data, and then apply an iterative algorithm (such as an adaptive filter).

The ICA séarches for orthogonal components that are mutually statistically independent to describe the input data matrix (composed by the féatures vectors). This can allow the reconstruction of source signals from several corrupted mixtures theréof.

The method is important to blind signal separation, EEG analysis and FMRI analysis.

This concept can be extended to analyse non physical signals, for instance ICA has been aplied to discover discussions topics on a bag of news list archives.

Pakait jeung 
 PCA (principal component analysis)
 varimax rotation
 projecting pursuit
 Image processing
 Signal processing
Kaputusan statistik  nyaéta cabang tina statistik nu ngandung generalisasi tina sampel ka populasi, ngararangkay tes hipotesa, nangtukeun hubungan antar variabel, sarta nyieun prediksi.
métodeu  inpormasi leherbotol mangrupa téhnik keur manggihkeun perdagangan nu hadé antara akurasi jeung kompresi waktu nyieun kasimpulan (contona clustering) variabel random X waktu dibérékeun joint sebaran probabiliti antara X jeung variabel observasi Y.

Tempo oge
 Tiori inpormasi

Tumbu kaluar
  Paper by N. Tishby, et. al
Dina statistik, interaksi nyaéta watesan dina model statistik nu mana efek dua variabel atawa leuwih teu bisa ditambahkeun sacara langsung.

Mangka, for a response y and two variables x1 and x2 an additive modél would be:

- while,

- is an example of a modél with an interaction between variables x1 and x2 (the word "errors" is not to be construed literally; it refers to a random variable by which y differs from the nilai ekspektasi of y).  See errors and residuals in statistics, and note that it is éasy to confuse errors with residuals, although the two are different.

Very often the interacting variables are categorical variables rather than réal numbers.  For example, members of a population may be classified by religion and by occupation.  If one wishes to predict a person's height based only on the person's religion and occupation, a simple additive modél, i.e., a modél without interaction, would add to an overall average height an adjustment for a particular religion and another for a particular occupation.  A modél with interaction, unlike an additive modél, could add a further adjustment for the "interaction" between that religion and that occupation.  This example may cause one to suspect that the word interaction is something of a misnomer.

The consequence of an interaction is that the effect of one variable depends on the value of another. This has implications in desain percobaan as it is misléading to vary one factor at a time.

réal-world examples of systems that manifest interactions include:

Interaction between adding sugar to coffee and stiring the coffee. Neither of the two individual variables has much effect on sweetness but a combination of the two does.
Interaction between adding carbon to steel and quenching. Neither of the two individually has much effect on strength but a combination of the two has a dramatic effect.

Genichi Taguchi contended that interactions could be eliminated from a system by appropriate choice of response variable and transformation. However George Box and others have argued that this is not the case in general.

Bibliography

Box, G E P (1990) Do interactions matter? Quality Engineering vol 2, pp365–369

Statistika
Dina statistik deskriptif, interquartile range (IQR) nyaéta beda antara kuartil katilu jeung kahiji. Interquartile range leuwih nembongkeun nu sabenerna dina statistik tinimbang rentang, sarta ilaharna dipikaresep dina statistik.

Lamun 25% data kurang atawa sarua jeung kuartil kahiji sarta 25% leuwih gedé atau sarua jeung kuartil katilu, bedana nyaéta panjang interval kaasup satengahna tina data. Beda ieu kudu diukur tina satuan data nu sarua.

Persentil jeung kuartil

Keur lobana sampel  mangka observasi  observasi diruntuykeun ti nu leutik kanu gedé. Tina runtuyan statistik ieu bisa kapanggih persentil sampel.

Lamun , persentil sampel nu ka-100 nujul kana  sampel observasi leuwih leutik tinimbang éta sarta  sampel observasi leuwih gedé tinimbang eta. Salah sahiji cara keur narima ieu nyaéta nyokot sampel persentil nu ka-(100) ku runtuyan statistik , tur  minangka integer.

Lamun  lain interger tur sarua jeung  ditambah sababaraha bagéan  mangka bakal manggih interpolasi linier antara  jeung .
Mangka sampel persentil nu ka-100 dihartikeun minangka:

Persentil nu pasti mibanda ngaran husus. Persentil nu ka-50 disebutna median tina sampel. Persentil nu ka-25, 50, tur ka-75  disebut kuartil kahiji, kadua jeung katilu tina sampel. Hal ini nembongkeun kasimpulan lima-wilangan tina runtuyan data, nyaéta minimal, kuartil kahiji, median, kuartil katilu jeung kuartil maksimum.

Ayeuna IQR atawa interquartile range dihartikeun minangka:

Conto

Itungan ieu maké data tina kaca quartile.

     i    x[i]
     1    102
     2    105
    ----------- the first quartile, Q[1] = (105+106)/2 = 105.5
     3    106
     4    109
   ------------ the second quartile, Q[2] or median = 109.5
     5    110
     6    112
   ------------ the third quartile, Q[3] = (112+115)/2 = 113.5
     7    115
     8    118

Tina tabel eta, the interquartile range is 113.5 - 105.5 = 8

Tempo ogé: statistical dispersion, mean

Statistika
Dinamika sistim sahiji pendekatan keur dinamika dina populasi, system ekologi sarta ékonomi, nu ilaharna pakait raket. Dinamika sistem dipanggihkeun di awal taun 1960s ku Jay W. Forrester di MIT nu ngawangun MIT System Dynamics Group. Dina waktu nu sarua, manéhna mimiti maké nu nalungtik ngeunaan sistim salila manéhna gawe di rekayasa listrik keur sistim nu dipaké unggal poe. Naha nu dipaké sistim dinamik béda tina pamarekanséjén nu diajarkeun dina sistim kompleks nu dipaké di feedback loops. Stocks sarta flows mangrupa wangunan blok modél sistim dinamika. manéhna mantuan ngajelaskeun kumaha sistim pakait ku feedback loops nu dijieun nonlinearity remen kapanggih dina masalah kiwati. Perangkat lunak komputer dipaké keur simulasi model dinamika sistim nu keur ditalungtik. Running "what if" simulations to test certain policies on such a modél can gréatly aid in understanding how the system changes over time.

Tumbu kaluar 
 MIT System Dynamics Group  
 The System Dynamics Society is an international, nonprofit organization devoted to encouraging the development and use of systems thinking and system dynamics around the world
 An Annotated Survey of the Essential System Dynamics Literature

Perngkat lunak haratis 
 Simile (free for reséarch, educational and personal use)
 http://www.suite101.com/article.cfm/scientific_computing/85003
 Vensim (free for educational and personal use)
 Introduction to Social Macrodynamics
 TRUE (free for educational and personal use)

Mailing lists 
 System Dynamics Mailing List
 Computer Simulation in Social Sciences

Téori sistim
Fungsi survivor atawa fungsi reliabiliti nyaéta sipat unggal variabel acak nu ngagambarkeun susunan kajadian, ilaharna pakait jeung paeh atawa gagalna sababaraha sistim, dumasar kana time. Ieu kawengku dina probabiliti nu mana sistim bakal nyalametkeun manéh salila waktu nu ditangtukeun. Watesan fungsi reliabiliti ilahar dipaké dina widang rékayasa sabalikna watesan fungsi survivor dipaké leuwih lega, kaasup dina hurip manusa.

Harti

Anggap X mangrupa variabel bébas nu mibanda cumulative distribution function F(t) dina interval [0,∞). Maka fungsi-survivor atawa fungsi-reliabiliti nyaéta:

Sipat

Unggal fungsi R(t) bakal turun sacara angger.

Waktu, t=0, ngagambarkeun kaayaan mimiti, sacara tipikal dimimitian nu nalungtik atawa sistim mimiti dipaké. R(0) ilaharna satuan tapi bisa kurang keur ngagambarkeun probabiliti nu mana sistem gagal teu lila sanggeus operasi.

Sakali deui, limt→∞R(t) ilaharna nol tapi bisa ogé leuwih gedé keur ngagambarkeun kahirupan dina jero sistim nu mungkin.

Konsep pakait

Tempo oge:

Hazard rate
Cummulative hazard function
Mean time to failure
Mean residual life

Statistika
In statistics, a Studentized residual, named in honor of William Sealey Gosset, who wrote under the pseudonym Student, is a residual adjusted by dividing it by an estimate of its standard deviation.  Studentization of residuals is an important technique in the detection of outliers.

Errors versus residuals

It is very important to understand the difference between errors and residuals in statistics.  Consider simple linear regression modél

where the errors εi, i = 1, ..., n, are independent and all have the same variance σ2.  The residuals are not the true, and unobservable, errors, but rather are estimates, based on the observable data, of the errors.  When the method of léast squares is used to estimate α0 and α1, then the residuals, unlike the errors, cannot be independent since they satisfy the two constraints

and

(Here  is the ith error, and  is the ith residual.)  Moréover, the residuals, unlike the errors, do not all have the same variance: the variance incréases as the corresponding x-value gets farther from the average x-value.  The fact that the variances of the residuals differ, even though the variances of the true errors are all equal to éach other, is the principal réason for the need for Studentization.

How to Studentize

For this simple modél, the "design matrix" is

and the "hat matrix" H is the matrix of the orthogonal projection onto the column space of the design matrix:

The "leverage" hii is the ith diagonal entry in the hat matrix.  The variance of the ith residual is

The corresponding Studentized residual is then

where  is an appropriate estimate of σ.

Internal and external Studentization

The estimate of σ2 may be

But it is desirable to exclude the ith observation from the process of estimating the variance when one is considering whether to consider the ith case to be an outlier.  Consequently one may use the estimate

based on all but the ith case.  If the latter estimate is used, excluding the ith case, then the residual is said to be externally Studentized, if the former is used, including the ith case, then it is internally Studentized.

If the errors are independent and  normally distributed with nilai ekspektasi 0 and varian σ2, then the probability distribution of the ith externally Studentized residual is a sebaran-t student nu mana n − 3 tingkat kabebasan.

Statistika
Stein's lemma, ngaran keur ngahargaan ka Charles Stein, may be characterized as a théorem of probability theory that is of interest primarily because of its application to statistical inference—in particular, its application to James-Stein estimation and empirical Bayes methods.

Statement of the lemma

Suppose X is a normally distributed random variable with nilai ekspektasi μ and varian σ2.  Further suppose g is a function for which the two expectations E( g(X) (X − μ) ) and E( g ′(X) ) both exist (the existence of the expectation of any random variable is equivalent to the finiténess of the expectation of its absolute value).  Then

In order to prove this lemma, recall that the probability density function for the normal distribution with expectation 0 and variance 1 is

and that for a normal distribution with expectation μ and varian σ2 is

Then use integration by parts.
Rumus prédiksi Spéarman-Brown (ogé dipikanyaho salaku rumus ramalan Spearman-Brown) nyaéta rumus hubungan reliabiliti psikometri kana panjangna uji:

nu mana  nyaéta prédiksi reliabiliti; N nyaéta jumlah kombinasi "test" (tempo di handap); sarta  nyaéta reliabiliti "tes" ayeuna.  Rumus prédiksi réability mangrupa uji anyar nu nyimpen ulangan N ayeuna (atawa sarua jeung nambahkeun bentuk N paralél tina tes ayeuna ka tes ayeuna).  Maka N=2 akibatna duka kali panjang tes item nu ditambahkeun ku cara maké sipat nu sarua dina tes ayeuna.  Nilai N kurang ti hiji mungkin bisa dipaké keur prediksi pangaruh dina tes nu pondok.

Rumus bisa ogé disusun keur prediksi jumlah ulangan nu diperlukeun keur nangtukeun tingkat réalibiliti:

Rumus ieu ilahar dipaké ku ahli psikometri keur prediksi réabiliti sanggeus ngarobah panjang tes. Hubungan ieu salah sahiji hal nu penting keur ngarobah-sabagian sarta métodeu  nu pakait jeung estimasi réabiliti.

Rumus ieu ogé mantuan ngarti kana hubungan nu teu linier antara tes réabiliti jeung panjang tes.

Lamun panjang/pondok tes teu paralel kana tes ayeuna, maka hasil prediksi moal akurat. Contona, lamun luhurna tes reliabiliti dipanjangan ku cara nambahan item nu goreng mangka reliabiliti bisa leuwih handap tinimbang nu diprediksi tina rumus ieu.

Tiori respon item merlukeun informasi item keur ngahasilkeun prediksi nu leuwih hadé dina ngaramal parobahan kualitas ukuran ku cara nambahan atawa ngurangan individu item.

Psikométri
Dina tés binér, saperti dina tés diagnosa médis keur panyakit di hiji wewengkon, specificity nyaéta proporsi négatip bener tina sakabéh sampel négatip nu dités, nyaéta

Tempo Ogé
 klasifikasi binér
 receiver operating characteristic
 sensitivity (tests)
 statistical significance
 Type I error

Statistika
Aya sababaraha cara keur ngadiskusikeun estimasi statistik...

Sasaran 
Titik estimasi
Interval estimasi
multivariate estimation??
Regression/Model building

Pendekatan/Filosofi
frequentist, or classical
Bayesian estimation
fiducial??

Metoda 

(gabungan tina sakabeh nu di luhur)

Maximum likelihood estimation
Method of moments
Least squares
Influence functions?
Interval kapercayaan (atawa wewengkon)
Highest posterior density interval/region
Fiducial??
etc...

Statistika