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.
model regression linear fit data models residuals function parameters values fitted error coefficients logistic fitting errors estimate response estimates squares
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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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poisson data species count binomial site counts negative weight sites model number plant family water temperature type year fish glm
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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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package data code software packages python sas good matlab program source implementation excel free analysis version learning programming work time
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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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data students people year income country school years level student average survey countries panel person population individual age study effect
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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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data missing values column function package columns row code table rows object error imputation run dataset file output class set
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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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prior bayesian posterior distribution model parameters likelihood priors frequentist data parameter mcmc tau bayes beta sigma sampling probability theta jags
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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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distribution data median outliers normal values transformation distributions scale log robust fit skewed skewness gamma outlier shape transform kurtosis quantile
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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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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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model data models set selection training validation test performance aic crossvalidation error cross parameters prediction lasso fit fold number accuracy
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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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genes gene graph nodes network expression node edges path protein networks genetic number parent list connected edge directed biological graphs
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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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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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theta likelihood estimator parameter function maximum parameters estimate mle log estimators distribution loglikelihood sample estimation unbiased statistic information sigma estimates
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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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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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book paper amp analysis methods statistics statistical models good reference references page journal article chapter books found introduction read approach
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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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hypothesis test null power pvalue testing significance tests alpha error type level significant reject true pvalues alternative rate difference hypotheses
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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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standard variance error deviation sum errors estimate square formula calculate average means squared sigma deviations values difference squares variances root
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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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plot line points data point plots graph values red curve image lines area axis blue show histogram bin color chart
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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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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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probability number probabilities times chance coin event events distribution binomial success heads trials total random expected balls outcomes numbers successes
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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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time survival patients risk disease hazard patient study cox model analysis age data event rate cancer covariates status outcome ratio
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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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model random effects fixed data effect mixed models intercept lme subject level fit variance time linear structure package lmer correlation
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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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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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class classification features feature classifier data training set classes svm learning accuracy problem machine dataset classifiers label vector algorithm roc
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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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pca analysis factor data variables components component principal factors variance matrix loadings scores original reduction correlation dimensions linear rotation dimension
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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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tree neural network trees input output random forest networks training layer function hidden learning decision node inputs train split importance
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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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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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variables correlation variable regression coefficient model effect significant coefficients interaction correlated relationship independent correlations linear predictors effects dependent term predictor
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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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ratio false log positive odds true negative ratios change rate values increase positives relative exp sensitivity unit percent calculate return
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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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