Sentences with phrase «error term»

Figure 1 shows the resulting model with 14 items, and two pairs of related error terms.

The standardized factor loadings, with measurement error terms in parenthesis, are reported.

predictors) variables in the model and between error terms of comparable manifest indicators of

Pitch - level error terms were then aggregated at the player level to identify deviation in performance from the league - level expectation across pitch locations.

Finally, εim is a stochastic error term clustered at the applicant group level to take into account the spatial correlation from students nested within applicant groups.

In these species - level models, the residuals error terms u0j and uqj are distributed normal with mean 0 and variance τ0 and τq respectively.

You can aim for least - squared error, straight error, least - cubed error, etc... Even the base error term is adjustable; Euclidean distance isn't mandatory.

Therefore, error terms of item 1 and 5 were allowed to correlate and CFA was rerun on the 14 - item scale.

The terms αt and βg are year - of - test and grade - of - test effects, while Xi is a vector of demographic controls with coefficient γ, and εigt is an error term that reflects random fluctuation in test scores.

The method used in the paper — clustering - robust linear regressions — may not be well known to the reviewer, but — contrary to the reviewer's claim — it does in fact take care of the hierarchical structure of the error terms.

The intuition behind this equation is that the unemployment rate of a given state can be explained by the amount that it varies in proportion to the unemployment rate for the US as a whole (the beta term), a fixed difference (the alpha term), and the error term.

PE = a / ROE + b + e / ROE, which means my error term could no longer be normally distributed.

Model 4, which does not use look - ahead information in its calibration, reduces the error term by 25 % versus Model 2, roughly two - thirds as good as clairvoyance!

Model 3, a linear model that does not use any look - ahead information in its calibration, reduces the error term by 11 % compared to Model 2 — nice, but not impressive — and its errors are a bit larger than the naïve assumption that all alphas are zero.

The improvement in forecasting error of 39 % for Model 6 compared to Model 2 shows how much, at best, a valuation - based model can reduce the error term.

This is an interesting study, but unfortunately valuation - based forecasts are swamped by the error term over short horizons.

All statistical assumptions regarding linearity, homoscedastic, normality of the residuals, independence of the error terms, and the lack of residual outliers were met.

In other words, if crunch works, it should provide a «lift,» and for projects that involved more crunch, we should see a positive error term (that is, game projects that crunched should have turned out better than the crunch - free model predicts), while for projects that involved little or no crunch, we should see a negative error term.

We then computed the crunch - free model's error term — that is, we compared the actual aggregate outcome score to the predicted outcome score given by the crunch - free model for each response by subtracting the predicted score from the actual aggregate outcome score.

For a lot of folks trained in experimental method, the error term associated with a data point is the measurement error, period.

What I see in common is that in both cases, they completely failed to come to grips with the nature of the error term they were dealing with.

Using models that are optimized for historical accuracy to predict future real states of a chaotic system have error terms that grow as a function of distance from supporting real data.

Nonstationarity of error terms is a serious problem in time - series analysis, but I don't have a good sense of how well this issue has been treated in climate analysis.