Two independent variables both correlate with the dependent variable, but none are significant in a regression analysis I recently encountered a problem in relation to my study of conflicts and performance. The two types of conflict in the study, task and relationship conflict, both correlated with performance $(-.39, P &lt; .01)$ and $(-.37, P &lt; .01)$. When I put these in a regression, the regression model came out significant, and it explained $22.1\%$ of the variance $(F\ (3, 82) = 7.76,\ P &lt; .01)$. None of the two IVs were, however, significant coefficients! There were no other variables in the regression analysis. How could this be?
How can a regression be significant yet all predictors be non-significant? My multiple regression analysis model has a statistically significant F value however all beta values are statistically non-significant.   All the regression assumptions are met. No multicollinearity was found. Correlations among all predictors are all less than 0.60.   What else might be the cause of the insignificant predictors?
Regression for an outcome (ratio or fraction) between 0 and 1 I am thinking of building a model predicting a ratio $a/b$, where $a \le b$ and $a &gt; 0$ and $b &gt; 0$. So, the ratio would be between $0$ and $1$.  I could use linear regression, although it doesn't naturally limit to 0..1.  I have no reason to believe the relationship is linear, but of course it is often used anyway, as a simple first model.  I could use a logistic regression, although it is normally used to predict the probability of a two-state outcome, not to predict a continuous value from the range 0..1.  Knowing nothing more, would you use linear regression, logistic regression, or hidden option c?