Exactly how large a VIF has to be before it causes issues is a subject of debate. What is known is that the more your VIF increases, the less reliable your regression results are going to be. In general, a VIF above 10 indicates high correlation and is cause for concern. Some authors ...
I use the example below in my post abouthow to interpret regression p-values and coefficients. The graph displays a regression model that assesses the relationship between height and weight. For this post, I modified the y-axis scale to illustrate the y-intercept, but the overall results haven...
In this situation, it’s not that the model will not give you the results. The only problem is that these results might be unstable, which means that even small changes in the data can lead to large changes in the estimated coefficients. This makes it harder to interpret the importance of...
Over the years, I’ve had many questions about how to interpret this combination. Some people have wondered whether the significant variables are meaningful. Do these results even make sense? Yes, they do! In this post, I show how to interpret regression models that have significant independent...
Our finding of a positive effect of SLA difference on the mixture effect on species dominant height is difficult to interpret because SLA is integrative of different processes and because it provides information on light interception at the leaf level, but not at the tree or stand level. In par...
You can carry out multiple regression using code or Stata's graphical user interface (GUI). After you have carried out your analysis, we show you how to interpret your results. First, choose whether you want to use code or Stata's graphical user interface (GUI)....
Learn how to conduct one-way and two-way ANOVA tests, interpret results, and make informed statistical decisions using Python
Results will be easier to interpret if you code the event of interest, such as success or presence of an animal, as 1, as the regression will model the probability of 1. There must be variation of the ones and zeros in the data both globally and locally. You can use the Nei...
headquarters and the subsidiary slightly differently (i.e., the number of applied patents for the headquarters versus the PCN ratio of engineers and R&D personnel for the subsidiary) because of the nature of the data this study relied on. Readers are advised to interpret the results with this ...
results were found to be robust after controlling for endogeneity bias using instrumental variable for happiness. In addition, the threshold regression analyses revealed significant evidence that the relationships between CAs and growth vary according to happiness levels. Two prominent ranges of threshold ...