Lab 1 Baby Boom - Prof. Wu PDF

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Davon Fred Coburn 005-120-436 October 2, 2019 Stats 10 Lab #1: Baby Boom 1. The unit of observation in the data is the effect smoking has on infants. The 13 variables recorded in this data are: ● Birth Month: Categorical Variable ● Birth Day: Numerical Variable ● Gender of Baby: Categorical Variable ● Day of Week: Categorical Variable ● Apgar score at 5 minutes: Numerical Variable ● Premature: Categorical Variable ● Low Birth Weight: Categorical Variable ● Weight of Baby at Birth: Numerical Variable ● Length of Gestation: Numerical Variable ● Father’s Age: Numerical Variable ● Mother’s Age: Numerical Variable ● Father’s Education: Categorical Variable ● Mother’s Education: Categorical Variable ● Total Number of Pregnancies: Numerical Variable ● Pre-delivery doctor visits: Numerical Variable ● Marital Status: Categorical Variable ● Race of mom: Categorical Variable ● Race of dad: Categorical Variable ● Hispanic mom: Categorical Variable ● Hispanic dad: Categorical Variable ● Weight gained by mom: Numerical Variable ● Mom’s smoking habits: Categorical Variable

2. There were 192 smokers reported in this data. The 192 smoking mothers make up only 9% of the sample reported in the data.

3. Out of the other categorical variables, I would expect babies with low birth weights to be associated with the mothers smoking because the cause of the low birth weights could be the fact that the mothers smoked during their pregnancies. The two-way summary table shows that smoking mothers is associated with low birth weights as 17% of the babies who were born with low birth weights had mothers who smoke.

4. The ribbon chart best depicts the association between smoking mothers and babies with low birth weights. The ribbon chart gives a clear visual that depicts the larger proportion of smokers that give birth to children with lower birth weights than nonsmokers.

5. When you drop a numerical variable into the summary table instead of a categorical variable, you are now comparing the two variables instead of looking for a connection between the two variables. The difference in average weight of infants between nonsmoking mothers and smoking mothers is about 217g; this tells us that mothers who don’t smoke during their pregnancies usually have babies 217 g heavier than babies

whose mothers do smoke. The difference in standard deviation between the birthweight of children from nonsmoking mothers and smoking mother is about 11g, which tells us that the data was not spread out and more congregated around the mean.

6. The histogram below tells us that mothers who do smoke will likely have a baby with a low birth weight, as opposed to mothers who do not smoke. The data for mothers who do not smoke is slight skewed to the right, while the data for the mothers who do smoke is more symmetrical. The results shown in the histogram below are unsurprising to me as the previous graphs were enough to convince me that the hypothesis is true.

Summary Question: The concepts covered in this lab that are in the textbook are mean, categorical variable, numerical variable, bar charts, ribbon charts, histograms, standard deviation,

data, spread, and variability. There were no concepts in this lab that weren’t covered in lecture, discussion or in the textbook....