According to these results, Is regression analysis significant?
Analysis of Variance
- SS = 1904905
- DF = 8
- MS = 238113
- F (DFn, DFd)= F (8, 17) = 1.353
- P value P=0.2843
Goodness of Fit
- Degrees of Freedom = 17
- Multiple R = 0.6237
- R squared = 0.389
- Adjusted R squared = 0.1015
The p value for the overall F statistic indicates that the overall multiple correlation R (and R squared) value is not significantly different from zero at the .05 level (p is greater than .05). The reason for the non-significant result may be a small sample (as indicated by your degrees of freedom) and/or the presence of too many predictor variables. This also explains the large discrepancy between R squared and adjusted R squared. You are lacking statistical power to "detect" an effect.
The results are not statistically significant (p>0.05), which means that the predictors or independent variables are unable to predict the dependent variable.
I have a Youtube video on how to carry out and interpret the results of a multiple regression analysis that might be helpful.
https://www.youtube.com/watch?v=kG0vW-pZ62I&t=190s
Paul F Burke
Many thanks
Unfortunately, the sample size is a big problem with me (only 50 patients).
Without knowing things like research question, etc. it's impossible to say.what you give is meaningless.
To add to the other answers:
Given the small sample size I wonder whether your variables are normally distributed as well. Maybe you can consider a nonparametric test instead of a regression analysis.
Hans Westerbeek
Thanks,
Some variables are normally distributes while others are not
Could you suggest a nonparametric test in my situation?
That's hard to say because I don't know what your variables are. The most common ones are Mann Whitney U Test, Sign Test, Wilcoxon Signed-Rank Test and Kruskal Wallis Test. You need to look into these tests to see which one is suitable for your data.
You should check if the residuals are normally distributed, not the variables. You should check other assumptions of regression as well. The sample size is not that small, did you calculate the observed power? If assumptions are not met, you may consider to transform the outcome or perform nonparametric regression.
You should go through theory a bit before trying to apply and interpret a statistical model.
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