Topic Coherence
Each list of terms represents a topic. Evaluate each topic's coherence on a scale from 1 to 5. Does a topic contain terms that you would expect to see together on a page? Does it contain terms that would work together as search queries? Could you easily think of a short descriptive label? A Coherent topic (5) should be clear, consistent, and readily interpretable. A Problematic topic (3) should have some related words but might merge two unrelated concepts or contain several offtopic words. A Useless topic (1) should have no obvious connection between more than two or three words.
series model time forecast arima process y_t stationary x_t y_{t lag x_{t function forecasting unit order models forecasts arma trend
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theta likelihood estimator parameter function maximum parameters estimate mle log estimators distribution loglikelihood sample estimation unbiased statistic information sigma estimates
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variables correlation variable regression coefficient model effect significant coefficients interaction correlated relationship independent correlations linear predictors effects dependent term predictor
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distribution data median outliers normal values transformation distributions scale log robust fit skewed skewness gamma outlier shape transform kurtosis quantile
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model regression linear fit data models residuals function parameters values fitted error coefficients logistic fitting errors estimate response estimates squares
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model random effects fixed data effect mixed models intercept lme subject level fit variance time linear structure package lmer correlation
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items score scale scores item questions analysis factor measure likert latent reliability rating responses survey response scales questionnaire question ratings
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book paper amp analysis methods statistics statistical models good reference references page journal article chapter books found introduction read approach
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probability number probabilities times chance coin event events distribution binomial success heads trials total random expected balls outcomes numbers successes
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game player team players win car games winning cars wins number play teams home probability points time chance average bet
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prior bayesian posterior distribution model parameters likelihood priors frequentist data parameter mcmc tau bayes beta sigma sampling probability theta jags
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poisson data species count binomial site counts negative weight sites model number plant family water temperature type year fish glm
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standard variance error deviation sum errors estimate square formula calculate average means squared sigma deviations values difference squares variances root
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group groups anova treatment effect control design test factor data condition subjects measures significant repeated subject experiment difference levels time
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pca analysis factor data variables components component principal factors variance matrix loadings scores original reduction correlation dimensions linear rotation dimension
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sample size population samples sampling estimate small large random sizes number weights variance proportion bias estimates larger observations effect smaller
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day time data days year month price number week sales years period rate product daily average customer months predict date
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intercept model error coefficients std data freedom degrees deviance estimate family residual rsquared pr(&gt max min residuals codes regression signif
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ratio false log positive odds true negative ratios change rate values increase positives relative exp sensitivity unit percent calculate return
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hypothesis test null power pvalue testing significance tests alpha error type level significant reject true pvalues alternative rate difference hypotheses
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tree neural network trees input output random forest networks training layer function hidden learning decision node inputs train split importance
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data statistics statistical analysis methods good theory research learning question results field techniques knowledge important study people inference questions science
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confidence interval intervals bootstrap estimate true upper lower distribution sample estimates parameter alpha calculate bound prediction method standard level bootstrapping
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lambda frac x_i leq x_n sum_{i ldots infty cdot left geq random dots mathbb expectation rightarrow sum cdots bar function
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distribution random normal distributions variables variance independent variable distributed sigma probability gaussian poisson case uniform process theorem function mixture sample
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beta alpha x_i y_i delta gamma sigma hat sum_{i epsilon frac mathbf sim hat{eta model estimate varepsilon x_{i term equation
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matrix covariance vector vectors matrices column columns diagonal elements mathbf{x row positive multivariate times rows values decomposition sigma linear compute
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variable variables regression model logistic dependent continuous categorical independent response binary predictor outcome dummy data predictors categories levels linear ordinal
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age gender sex male female women height males females men weight group income num person children race exp people sig
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data set values number points times random time sets method observations average results dataset measurements numbers generate samples run simulation
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genes gene graph nodes network expression node edges path protein networks genetic number parent list connected edge directed biological graphs
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plot line points data point plots graph values red curve image lines area axis blue show histogram bin color chart
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function density distribution pdf f(x cdf probability frac int integral infty phi random sigma functions normal kernel sqrt variable continuous
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time series data model trend noise signal period change seasonal autocorrelation level arima structure analysis process spatial trends frequency lag
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class classification features feature classifier data training set classes svm learning accuracy problem machine dataset classifiers label vector algorithm roc
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amp sigma frac end{align rho begin{align left end{array boldsymbol vdots end{bmatrix cdots quad sigma_y begin{pmatrix prime end{pmatrix begin{bmatrix mu_x ldots
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data students people year income country school years level student average survey countries panel person population individual age study effect
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var data pred code import return true dat newdata double print size replace def function int nan library(ggplot gt;&gt;&gt aes(x
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it's case don't make answer question problem good you're sense information doesn't point small approach work give that's makes things
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time survival patients risk disease hazard patient study cox model analysis age data event rate cancer covariates status outcome ratio
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distance cluster clustering clusters data kmeans similarity points distances algorithm measure metric number euclidean algorithms matrix point space method based
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package data code software packages python sas good matlab program source implementation excel free analysis version learning programming work time
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data missing values column function package columns row code table rows object error imputation run dataset file output class set
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model data models set selection training validation test performance aic crossvalidation error cross parameters prediction lasso fit fold number accuracy
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function algorithm state problem kernel solution space markov parameters states loss optimization step gradient transition find weights method hidden sequence
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probability p(x entropy null p(a omega conditional mid p(y probabilities p_i p(b information frac cap bayes a_i cdot cust p(c
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col code rnorm true set.seed rep plot type data function function(x lwd main ylab xlab ncol red false pch seq
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i'm question i've find problem don't understand data answer found correct questions read i'd it's calculate can't appreciated results method
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words word user users number text document documents sequence set category list frequency topic sequences length corpus count topics votes
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test tests data statistic difference ttest pvalue sample distribution chi hypothesis groups compare chisquare normality means table samples null testing
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