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Full-Text Articles in Statistics and Probability
Estimating Explanatory Power In A Simple Regression Model Via Smoothers, Rand R. Wilcox
Estimating Explanatory Power In A Simple Regression Model Via Smoothers, Rand R. Wilcox
Journal of Modern Applied Statistical Methods
Consider the regression model Y = γ(X) + ε , where γ(X) is some conditional measure of location associated with Y , given X. Let Υ̂ be some estimate of Y, given X, and let τ2 (Y) be some measure of variation. Explanatory power is η2 = τ2 (Υ̂) /τ2(Y) . When γ(X) = β0 + β1X and τ2(Y) is the variance of Y , η2 = ρ2 , …
Confidence Intervals For The Squared Multiple Semipartial Correlation Coefficient, James Algina, H. J. Keselman, Randall D. Penfield
Confidence Intervals For The Squared Multiple Semipartial Correlation Coefficient, James Algina, H. J. Keselman, Randall D. Penfield
Journal of Modern Applied Statistical Methods
The squared multiple semipartial correlation coefficient is the increase in the squared multiple correlation coefficient that occurs when two or more predictors are added to a multiple regression model. Coverage probability was investigated for two variations of each of three methods for setting confidence intervals for the population squared multiple semipartial correlation coefficient. Results indicated that the procedure that provides coverage probability in the [.925, .975] interval for a 95% confidence interval depends primarily on the number of added predictors. Guidelines for selecting a procedure are presented.
Coverage Performance Of The Non-Central F-Based And Percentile Bootstrap Confidence Intervals For Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina
Coverage Performance Of The Non-Central F-Based And Percentile Bootstrap Confidence Intervals For Root Mean Square Standardized Effect Size In One-Way Fixed-Effects Anova, Guili Zhang, James Algina
Journal of Modern Applied Statistical Methods
The coverage performance of the confidence intervals (CIs) for the Root Mean Square Standardized Effect Size (RMSSE) was investigated in a balanced, one-way, fixed-effects, between-subjects ANOVA design. The noncentral F distribution-based and the percentile bootstrap CI construction methods were compared. The results indicated that the coverage probabilities of the CIs for RMSSE were not adequate.
Significance Tests Harm Progress In Forecasting, J. Scott Armstrong
Significance Tests Harm Progress In Forecasting, J. Scott Armstrong
J. Scott Armstrong
Based on a summary of prior literature, I conclude that tests of statistical significance harm scientific progress. Efforts to find exceptions to this conclusion have, to date, turned up none. Even when done correctly, significance tests are dangerous. I show that summaries of scientific research do not require tests of statistical significance. I illustrate the dangers of significance tests by examining an application to the M3-Competition. Although the authors of that reanalysis conducted a proper series of statistical tests, they suggest that the original M3 was not justified in concluding that combined forecasts reduce errors and that the selection of …