DATASET:https://dasl.datadescription.com/datafile/babysamp-98/?_sfm_cases=80+200

Categorical Variables:

Sex of the baby (listed as "M" for male.

Or "F" for female, status as premature or not (listed as "true" for premature or "false as mature).

Numerical Variables:

Weight at birth, gestation period, mom's age, number of live births, and education of the mother (in years, based on the academic system rather than vocational schools).

1. Make oneBivariate Analyses(plus its interpretation)

2. Make twoUnivariate

DATASET: Website: https://dasl.datadescription.com/datafile/babysamp-98/ 1. Make one Bivariate Analyses (plus its interpretation) and two Univariate. Primary objective: To explore whether it is more or less likely to have a premature baby when the mother is younger or older than the average. Secondary Objective: Is there a correlation between the child's sex and whether they were premature and is there a correlation between the baby's weight and sex. Categorical Variables: . Sex of the baby (listed as "M" for male or "F" for female), status as premature or not (listed as "true" for premature or "false" as mature) Numerical Variables: . Weight at birth, gestation period, mom's age, number of live births, and education of the mother (in years, based on the academic system rather than vocational schools)