Fitting least-squares lines to bivariate data is a topic traditionally discussed in introductory statistics courses, often in a unit of study that includes correlation. Recently, because calculators that graph bivariate data sets and compute regression equations have become widely available, this topic ...
Despite the striking resemblance of the q-statistic in GDM to the R-squared in linear regression models, their explicit connection has not yet been established. This study proves that the q-statistic reduces into the R-squared under a linear regression framework. Under linear regression and ...
24]. However, these datasets are not suitable for multi-target regression settings specific to readers’ emotion detection as they map documents to only a single emotion with corresponding intensity. An available benchmark dataset that suits multi-target regression based...
In the output, we can see that the Electoral College and Dow Jones predictors are all significant, and the R-squared is 56.7%. The adjusted R-squared also increased from Silver’s original model, suggesting that adding the additional predictor is valid. Thecoefficientsare all as expected given...
in line with the suggestions of Shmueli et al. (2019) and Barta et al. (2023). According to the analysis of PLSpredict, theQ2values of the indicators in terms of continuance intention were greater than 0 (see Table7). In addition, the root mean-squared error (RMSE) and mean absolute...
It requires the specification of a smoothing factor which is usually chosen from the data to minimize the average squared residual of previous one-step-ahead forecasts. In this paper we show that exponential smoothing can be put into a nonparametric regression framework and gain some interesting ...
This is equivalent to testing significance of the regression slope, \(\widehat \beta _j\), as both \(\widehat \beta _j\) and rj are assumed to be t-distributed and have the same t-value: \(t_j = \beta _j/{\mathrm{se}}\left( {\beta _j} \right) = r_j/{\mathrm{se}}...
Haider and Chatti also adopted various statistical analyses (ANOVA, logistic regression, discriminant analysis, etc.) to analyze the relative influence of design features and site factors on the fatigue performance of in-service flexible pavements. Among the design factors, the base type was the most...
Table 4:Performancecomparison of each regressor for predicting the number of transit trips in individuals’ daily trips based on 10-fold cross-validation Dependent Variable = The number of transit trips DTRFXGBNNSVMLinRZINBHurdle R-Squared (%)¯X52.3358.2253.9055.3753.0646.7449.8948.11 ...
The associations between exogenous and endogenous variables were examined using standardized regression coefficients (β), 95% confidence intervals, and p-values, with a significance level of p-value < 0.05. Results The proposed model outperformed previous UTAUT models, explaining 84.5% (squared ...