# Load the data into SPSS: baby weight.sav The variables are bwt: baby’s weight in ounces at birthgestation: duration of pregnancy in daysparity: parity indicator (first born = 1, later birth =

baby weight.sav

The variables are

• bwt: baby’s weight in ounces at birth
• gestation: duration of pregnancy in days
• parity: parity indicator (first born = 1, later birth = 0)
• age: mother’s age in years
• height: mother’s height in inches
• weight: mother’s weight in pounds (during pregnancy)
• smoke: indicator for whether mother smokes (1=yes, 0=no)

The mean birth weight for non-smokers was 123oz, for smokers 113.8oz. A t-test comparing the two indicates the difference is statistically significant (t=8.7, p <0.01, 1172 df). However, this is observational data, not experimental data. The subjects were not randomly assigned to smoking and non-smoking conditions.

There are other factors known to be associated with birth weight. The length of gestation is a major determinant of birth weight, and physiological factors such as the mother’s weight, height, age and parity may also be related to birth weight.

Since smoke is 1 for smokers and 0 for non-smokers. Since there are almost 1200 cases, you may or may not be able to see anything obvious in these plots. Use SPSS to analyse multiple regression on the following instruction

a) Fit the regression model

bwt = gestation + smoke + height + weight + parity

b) Estimate the difference in birthweight between mothers who smoke, and mothers who don’t smoke (assuming gestation length and other relevant factors are equal for both). Give a 95% CI for that difference.

c) Give a 95% CI for the slope in gestation.

d) Explain in substantive terms what each coefficient means.