At times it is necessary to convert a continuous predictor into a categorical predictor. For example, income per household is shown below.
This data is censored, all family income above $155,000 is stated as $155,000. A further explanation about censored and truncated data can be found here. It would be incorrect to use this variable as a continuous predictor due to its censoring.
What’s a good method for interpreting the results of a model with two continuous predictors and their interaction?
Let’s start by looking at a model without an interaction. In the model below, we regress a subject’s hip size on their weight and height. Height and weight are centered at their means.
Choosing statistical software is part of The Fundamentals of Statistical Skill and is necessary to learning a second software (something we recommend to anyone progressing from Stage 2 to Stage 3 and beyond).
One approach to model building is to use all predictors that make theoretical sense in the first model. For example, a first model for determining birth weight could include mother’s age, education, marital status, race, weight gain during pregnancy and gestation period.
The main effects of this model show that a mother’s education level and marital status are insignificant.
(more…)
My poor colleague was pulling her hair out in frustration today.
You know when you’re trying to do something quickly, and it’s supposed to be easy, only it’s not? And you try every solution you can think of and it still doesn’t work?
And even in the great age of the Internet, which is supposed to know all the things you don’t, you still can’t find the answer anywhere?
Cue hair-pulling.
Here’s what happened: She was trying to import an Excel spreadsheet into SAS, and it didn’t work.
Instead she got:
The expression “can’t see the forest for the trees” often comes to mind when reviewing a statistical analysis. We get so involved in reporting “statistically significant” and p-values that we fail to explore the grand picture of our results.
It’s understandable that this can happen. We have a hypothesis to test. We go through a multi-step process to create the best model fit possible. Too often the next and last step is to report which predictors are statistically significant and include their effect sizes.