Name:

Description:

The data come to us from Hosmer and Lemeshow (2000). Called the low birth weight (lbw) data, the response is a binary variable, low, which indicates whether the birth weight of a baby is under 2500g (low=1), or over (low=0).

Variables:

A data frame with 189 observations on the following 11 variables.

low

1=low birthweight baby; 0=norml weight

smoke

1=history of mother smoking; 0=mother nonsmoker

race

categorical 1-3: 1=white; 2-=black; 3=other

age

age of mother: 14-45

lwt

weight (lbs) at last menstrual period: 80-250 lbs

ptl

number of false of premature labors: 0-3

ht

1=history of hypertension; 0 =no hypertension

ui

1=uterine irritability; 0 no irritability

ftv

number of physician visits in 1st trimester: 0-6

bwt

birth weight in grams: 709 - 4990 gr

Details

lbw is saved as a data frame. Count models can use ftv as a response variable, or convert it to grouped format

Link To Google Sheets:

Rows:

Columns:

Files:

License Type:

References/Notes/Attributions:

Source

Hosmer, D and S. Lemeshow (2000), Applied Logistic Regression, Wiley

References

Hilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press Hilbe, Joseph M (2009), Logistic Regression Models, Chapman & Hall/CRC

R Dataset Upload:

Use the following R code to directly access this dataset in R.

R Coding Interface: