What are you talking about? A correlation coefficient of .5 is in the ballpark of or bigger than the correlation between human height and weight. I wouldn’t be surprised if the bottleneck isn’t in the reliability of the measurement.
Unmodeled interactions here also would only be able to suppress the explained variance - adding them in could only increase the R-squared!
"They produced a regression model and deduced that because the F-test had a low p value that the dark tetrad scores predicted the car score. The F-test, for clarity, determines if a model predicts the response variable better than a model with no explanatory variables. "
Yes, when you wanna know if a variable predicts another, one thing you can do is that you compare how well a model with the predictor included fares compared to a model without the predictor. One way of doing that is by using an F-test.
In case your 101 course hasn’t covered that yet: F-tests are also commonly used when performing an analysis of variance.
“As is it’s impossible to say if the model they found is actually very good.”
You say that after quoting explained variance, which is much more useful (could use confidence intervals… but significance substitutes here a little) in this context for judging how good a model is in absolute terms than some model comparison would be (which could give relative goodness).
Your criticism amounts to “maybe they are understating the evidence”.
What are you talking about? A correlation coefficient of .5 is in the ballpark of or bigger than the correlation between human height and weight. I wouldn’t be surprised if the bottleneck isn’t in the reliability of the measurement.
Unmodeled interactions here also would only be able to suppress the explained variance - adding them in could only increase the R-squared!
"They produced a regression model and deduced that because the F-test had a low p value that the dark tetrad scores predicted the car score. The F-test, for clarity, determines if a model predicts the response variable better than a model with no explanatory variables. "
Yes, when you wanna know if a variable predicts another, one thing you can do is that you compare how well a model with the predictor included fares compared to a model without the predictor. One way of doing that is by using an F-test.
In case your 101 course hasn’t covered that yet: F-tests are also commonly used when performing an analysis of variance.
“As is it’s impossible to say if the model they found is actually very good.”
You say that after quoting explained variance, which is much more useful (could use confidence intervals… but significance substitutes here a little) in this context for judging how good a model is in absolute terms than some model comparison would be (which could give relative goodness).
Your criticism amounts to “maybe they are understating the evidence”.