What is the minimum number of observation that I must have to perform a regression analysis?
For example, in regression analysis, many researchers say that there should be at least 10 observations per variable. If we are using three independent variables, then a clear rule would be to have a minimum sample size of 30.
How many points do you need for a regression?
1 Answer. Peters rule of thumb of 10 per covariate is a reasonable rule. A straight line can be fit perfectly with any two points regardless of the amount of noise in the response values and a quadratic can be fit perfectly with just 3 points.
What’s the minimum number of observations per parameter?
The general rule of thumb (based on stuff in Frank Harrell’s book, Regression Modeling Strategies) is that if you expect to be able to detect reasonable-size effects with reasonable power, you need 10-20 observations per parameter (covariate) estimated. Harrell discusses a lot of options for “dimension reduction”…
How many observations to rule out a variable?
From my experience with regression 21 observations with 5 variables is not enough data to rule out variables. So I would not be so quick to throw out variables nor get too enamored with the ones that appear significant.
How many observations do I need for multiple regression?
I am doing multiple linear regression. I have 21 observations and 5 variables. My aim is just finding the relation between variables Is my data set enough to do multiple regression? The t-test result revealed 3 of my variables are not significant.
When to use events per predictor or EPP?
Note that, if a predictor is categorical with three of more categories, or continuous and modelled as a nonlinear trend, then including the predictor will require two or more parameters being included in the model. Therefore, we refer to events per predictor parameter (EPP) here, rather than events per variable.