How do I report insignificant results?
A more appropriate way to report non-significant results is to report the observed differences (the effect size) along with the p-value and then carefully highlight which results were predicted to be different.
What does it mean if a result is not significant?
This means that the results are considered to be „statistically non-significant‟ if the analysis shows that differences as large as (or larger than) the observed difference would be expected to occur by chance more than one out of twenty times (p > 0.05).
When to remove insignificant variables in logistic regression?
Typically, when you use logistic regression in a business setting, both inferential information about the variables used along with a good prediction are what stakeholders are looking for. Additionally, another good reason to remove variables is for model parsimony.
How to remove insignificant variables based on AIC?
Have a look at the help pages for step (), drop1 () and add1 (). These will help you to add/remove variables based on AIC. However, all such methods are somewhat flawed in their path dependence. A better way would be to use the functions in the penalised or glmnet package to perform a lasso regression.
When to remove insignificant variables in an Excel spreadsheet?
If, instead, you are interested in examining which factors contribute to the probability of a customer buying, then there is no need to remove variables which fail to reject the null (especially in a stepwise sort of manner).
Why did I include a variable in my model?
Presumably, you included a variable in your model because you thought (from past experience or expert opinion) that it played an important part in a customer deciding if they will buy. That the variable failed to reject the null doesn’t make your model a bad one, it just means that your sample didin’t detect an effect of that variable.