| Statistics Terms | <stu ’tis tiks tRmz> | |
| absolute error | <ab su ‘LOOt ‘eR R> | |
| algebra | <‘aL ju bru> | |
| algorithm | <‘aL gu Ri THum> | |
| alternative | <oL ‘tR nu div> | |
| analytical | <an u ‘Li di kuL> | |
| approach | <u ‘pROch> | |
| approximation | <u pRok su ‘mA shun> | |
| association | <u sO sE ‘A shun> | |
| assumption | <u ‘sump shun> | |
| asymptotic | <a sim ‘tO dik> | |
| auto-correlation | <‘o dO kOR u ‘LA shun> | |
| Bayesian | <‘bA zE un> | |
| bias (unbias) | <‘bI us> <‘un bI us> | |
| binary | <‘bI ne RE> | |
| biostatistics | <bI O stu ’tis tiks> | |
| bootstrap | <‘bOOt stRap> | |
| calculus | <‘kaL kEu Lus> | |
| casualty | <‘ka zhOOuL tE> | |
| coefficient | <kO u ‘fi shunt> | |
| complete | <kum ‘pLEt> | |
| computing | <kum ‘pEOO ding> | |
| conditional | <kun ‘di shu nuL> | |
| confident interval | <‘Kon fu dunt ‘in tR vuL> | |
| constant | <‘kon stunt> | |
| converge | <kun ‘vRj> | |
| convolution | <kon vu ‘LOO shun> | |
| corollary | <‘kOR u LeR E> | |
| correlation | <KOR u ‘LA shun> | |
| covariance | <kO ‘veR E uns> | |
| covariate | <kO ‘veR E ut> | |
| cross validation | <kRos va Lu ‘dA shun> | |
| data | <‘dA du> < ‘da du> | |
| deep learning | <dEEp ‘LR ning> | |
| define | <di ‘fIn> | |
| definition | <de fu ‘ni shun> | |
| dependency | <di ‘pen dun sE> | |
| derivation | <deR u ‘vA shun> | |
| derivative | <dR ‘i vu tiv> | |
| derive | <di ‘RIv> | |
| differential | <di fR ‘en shuL> | |
| dimension | <du ‘men shun> | |
| diverge | <du ‘vRj> | |
| efficiency | <i ‘fi shun sE> | |
| equal | <‘E kOOuL> | |
| estimate | <‘e stu mAt> <‘e stu mut> | |
| estimation | <e stu ‘mA shun> | |
| expectation | <eks pek ‘tA shun> | |
| experimental design | <ek spi Ru ‘men tL di ‘zIn> | |
| exponential | <eks pu ‘nen shuL> | |
| expression | <ek ‘spRe shun> | |
| fitting | <‘fi ding> | |
| fixed effect | <fikst i ‘fekt> | |
| forecast | <‘fOR kast> | |
| formula | <‘fOR mEu Lu> | |
| functional data | <‘fungk shu nL ‘dA du> | |
| Gaussian | <‘goOO sE un> | |
| generalized | <‘je nu Ru LIzd> | |
| hypothesis test | <hI ‘po thu sus test> | |
| identical | <I ‘den ti kuL> | |
| imputation | <im pEu ‘tA shun> | |
| independent | <in du ‘pen dunt> | |
| inequality | <in i ‘KOOo Lu dE> | |
| inference | <‘in fR uns> | |
| infimum | <in ‘fu mum> | |
| infinity | <in ‘fi nu dE> | |
| integral | <‘in ti gRuL> | |
| inverse | <‘in vRs> | |
| kernel | <‘kR nL> | |
| lemma | <‘Le mu> | |
| likelihood | <‘LIk LE hood> | |
| linear regression | <‘Li nE R Ri ‘gRe shun> | |
| linearization | <Li nE R u ‘zA shun> | |
| logistic | <Lu ‘jis tik> | |
| logrithm | <‘Log Ri THum> | |
| machine learning | <mu ‘shEn ‘LR ning> | |
| marginal | <‘moR ju nuL> | |
| matrices | <‘mA tRu sEz> | |
| matrix | <‘mA tRiks> | |
| maximum | <‘mak su mum> | |
| mean | <mEn> | |
| mechanism | <‘me ku ni zum> | |
| median | <‘mE dE un> | |
| minimum | <‘mi nu mum> | |
| misspecification | <mis spe su fu ‘KA shun> | |
| model | <‘mo duL> | |
| moment | <‘mO munt> | |
| Monte Carlo | <‘mon tE ‘koR LO> | |
| multinomial | <muL tE ‘nO mE uL> | |
| multivariate | <muL tE ‘veR E ut> | |
| negative | <‘ne gu tiv> | |
| negligible | <‘ne gLi ju bL> | |
| neural network | <‘nR uL ‘net OORk> | |
| nonparametric | <non peR u ‘me tRik> | |
| normal distribution | <‘nOR muL dis tRu’bEOO shun> | |
| observation | <ob zR ‘vA shun> | |
| optimization | <op tu mu ‘zA shun> | |
| outlier | <‘oOOt LI R> | |
| parameter | <pu ‘Ra mu dR> | |
| partition | <poR ‘ti shun> | |
| percent | <pR ‘sent> | |
| positive | <‘po zu tiv> | |
| Possion | <‘po shun> | |
| posterior | <pO ‘stiR E R> | |
| predict | <pRi ‘dikt> | |
| prediction | <pRi ‘dik shun> | |
| principal component analysis | <‘pRin su pL kum ‘pO nunt u ‘na Lu sus> | |
| prior | <‘pRI R> | |
| probability | <pRo bu ‘bi Lu dE> | |
| procedure | <pru ‘sE jR> | |
| propensity score | <pRu ‘pen su dE skOR> | |
| proportion | <pRu ‘pOR shun> | |
| proof | <pROOf> | |
| prove | <pROOv> | |
| quadratic equation | <KOOo ‘dRa dik i ‘KOOA zhun> | |
| quantile | <‘kOOon tIL> | |
| random effect | <‘Ran dum i ‘fekt> | |
| random forest | <‘Ran dum ‘fOR ust> | |
| random variable | <‘Ran dum ‘veR E u bL> | |
| randomization | <Ran du mu ’zA shun> | |
| range | <RAnj> | |
| rank | <Rangk> | |
| record | <‘Re kRd> <RE ‘kORd> | |
| relationship | <Ri ‘LA shun ship> | |
| repeated measure | <Ri ‘pE dud ‘me zhR> | |
| response | <Ri ‘spons> | |
| robust | <RO ‘bust> | |
| root of mean squared error | <ROOt uv mEn skOOeRD ‘eR R | |
| sampling | <‘sam pLing> | |
| sensitivity | <sen su ‘ti vu dE> | |
| significant | <sig ‘ni fi kunt> | |
| skew | <sKEOO> | |
| sparse | <spoRs> | |
| spatial | <‘spA shuL> | |
| specificity | <spe su ‘fi su dE> | |
| spline | <spLIn> | |
| split plot design | <spLit pLot di ‘zIn> | |
| standard error | <‘stan dRd ‘eR R> | |
| statistics | <stu ’tis tiks> | |
| stochastic | <stO ‘kas tik> | |
| sufficient | <su ‘fi shunt> | |
| supremum | <su ‘pRi mum> | |
| survival analysis | <sR ‘vI vuL u ‘na Lu sus> | |
| symmetric | <su ‘me tRik> | |
| theorem | <‘thER Rum> | |
| theory | <‘thER RE> | |
| time series | <tIm ‘sER Ez> | |
| transformation | <tRans fR ‘mA shun> | |
| treatment | <‘tREt munt> | |
| uniform | <‘U nu fORm> | |
| univariate | <‘U ni veR E ut> | |
| variance | <‘veR E uns> | |
| vectorization | <vek tR i ‘zA shun> | |
| visualization | <vi zhu OOu Lu ‘zA shun> | |
| volatility | <vo Lu ’ti Lu dE> |
