Height

Weight

BMI

Age1

College (1=college)

Foreign (1=foreign) ]]>

Thanks for providing the data; I fired up SAS to take a look at it.

I ran a Logit regression on whether a pitcher went on the DL or not. None of the variables were explanatory.

I next ran a linear regression on DL length of stay for those who were on the DL. That yielded a one-variable model that was not significant at the alpha=.05 level (although did make an alpha=.10 cut). The R-squared is terrible (.006). The variable that was included was BMI; higher BMI values were mildly and weakly associated with lower stays on the DL.

Conclusion: whatever causes variance in propensity to go on the DL or length of stay on the DL isn’t apparent in this data, at least on a first cut model.

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