0.1 North Carolina births

In 2004, the state of North Carolina released a large data set containing information on births recorded in this state. This data set is useful to researchers studying the relation between habits and practices of expectant mothers and the birth of their children. We will work with a random sample of observations from this data set.

0.2 Exploratory analysis

Load the nc data set into our workspace.

load("more/nc.RData")

We have observations on 13 different variables, some categorical and some numerical. The meaning of each variable is as follows.

variable description
fage father’s age in years.
mage mother’s age in years.
mature maturity status of mother.
weeks length of pregnancy in weeks.
premie whether the birth was classified as premature (premie) or full-term.
visits number of hospital visits during pregnancy.
marital whether mother is married or not married at birth.
gained weight gained by mother during pregnancy in pounds.
weight weight of the baby at birth in pounds.
lowbirthweight whether baby was classified as low birthweight (low) or not (not low).
gender gender of the baby, female or male.
habit status of the mother as a nonsmoker or a smoker.
whitemom whether mom is white or not white.

0.2.1 Exercise 1

  1. What are the cases in this data set? How many cases are there in our sample?
head(nc)
##   fage mage      mature weeks    premie visits marital gained weight
## 1   NA   13 younger mom    39 full term     10 married     38   7.63
## 2   NA   14 younger mom    42 full term     15 married     20   7.88
## 3   19   15 younger mom    37 full term     11 married     38   6.63
## 4   21   15 younger mom    41 full term      6 married     34   8.00
## 5   NA   15 younger mom    39 full term      9 married     27   6.38
## 6   NA   15 younger mom    38 full term     19 married     22   5.38
##   lowbirthweight gender     habit  whitemom
## 1        not low   male nonsmoker not white
## 2        not low   male nonsmoker not white
## 3        not low female nonsmoker     white
## 4        not low   male nonsmoker     white
## 5        not low female nonsmoker not white
## 6            low   male nonsmoker not white
dim(nc)
## [1] 1000   13
paste0("The cases are mothers who have had babies and there are ",nrow(nc)," cases/observations in this dataset that represent births in North Carolina")
## [1] "The cases are mothers who have had babies and there are 1000 cases/observations in this dataset that represent births in North Carolina"

As a first step in the analysis, we should consider summaries of the data. This can be done using the summary command:

summary(nc)
##       fage            mage            mature        weeks      
##  Min.   :14.00   Min.   :13   mature mom :133   Min.   :20.00  
##  1st Qu.:25.00   1st Qu.:22   younger mom:867   1st Qu.:37.00  
##  Median :30.00   Median :27                     Median :39.00  
##  Mean   :30.26   Mean   :27                     Mean   :38.33  
##  3rd Qu.:35.00   3rd Qu.:32                     3rd Qu.:40.00  
##  Max.   :55.00   Max.   :50                     Max.   :45.00  
##  NA's   :171                                    NA's   :2      
##        premie        visits            marital        gained     
##  full term:846   Min.   : 0.0   married    :386   Min.   : 0.00  
##  premie   :152   1st Qu.:10.0   not married:613   1st Qu.:20.00  
##  NA's     :  2   Median :12.0   NA's       :  1   Median :30.00  
##                  Mean   :12.1                     Mean   :30.33  
##                  3rd Qu.:15.0                     3rd Qu.:38.00  
##                  Max.   :30.0                     Max.   :85.00  
##                  NA's   :9                        NA's   :27     
##      weight       lowbirthweight    gender          habit    
##  Min.   : 1.000   low    :111    female:503   nonsmoker:873  
##  1st Qu.: 6.380   not low:889    male  :497   smoker   :126  
##  Median : 7.310                               NA's     :  1  
##  Mean   : 7.101                                              
##  3rd Qu.: 8.060                                              
##  Max.   :11.750                                              
##                                                              
##       whitemom  
##  not white:284  
##  white    :714  
##  NA's     :  2  
##                 
##                 
##                 
##

As you review the variable summaries, consider which variables are categorical and which are numerical. For numerical variables, are there outliers? If you aren’t sure or want to take a closer look at the data, make a graph.

Consider the possible relationship between a mother’s smoking habit and the weight of her baby. Plotting the data is a useful first step because it helps us quickly visualize trends, identify strong associations, and develop research questions.

0.2.2 Exercise 2

  1. Make a side-by-side boxplot of habit and weight. What does the plot highlight about the relationship between these two variables?
plot_ly(x=~nc$habit,y=~nc$weight,type = "box")
## Warning: Ignoring 1 observations
boxplot(nc$weight ~ nc$habit,
        data=nc, 
        col = "lightblue",
        main="Relation Between Mother's Habit and Baby's Weight", 
        ylab="Baby's Weight", xlab="Mother Smoker/Non-Smoker")

paste0("The above plots indicate that the median weight is almost equal, however, both non-smoker and smoker have many outliers.")
## [1] "The above plots indicate that the median weight is almost equal, however, both non-smoker and smoker have many outliers."

The box plots show how the medians of the two distributions compare, but we can also compare the means of the distributions using the following function to split the weight variable into the habit groups, then take the mean of each using the mean function.

by(nc$weight, nc$habit, mean)
## nc$habit: nonsmoker
## [1] 7.144273
## -------------------------------------------------------- 
## nc$habit: smoker
## [1] 6.82873

There is an observed difference, but is this difference statistically significant? In order to answer this question we will conduct a hypothesis test .

0.3 Inference

0.3.1 Exercise 3

  1. Check if the conditions necessary for inference are satisfied. Note that you will need to obtain sample sizes to check the conditions. You can compute the group size using the same by command above but replacing mean with length.

3 conditions needs to be satisfied for inference - Independence - Randomization - Normally Distributed

by(nc$weight, nc$habit, length)
## nc$habit: nonsmoker
## [1] 873
## -------------------------------------------------------- 
## nc$habit: smoker
## [1] 126
paste0("As can be seen from above :
       1. Independence : Each birth has not influence on another. 
       2. Randomization : Simple random sample is less than 10% of the population. 
       3. Normally Distributed : Over 30 samples exists in both groups with little skewness, so we can assume normal distribution")
## [1] "As can be seen from above :\n       1. Independence : Each birth has not influence on another. \n       2. Randomization : Simple random sample is less than 10% of the population. \n       3. Normally Distributed : Over 30 samples exists in both groups with little skewness, so we can assume normal distribution"

0.3.2 Exercise 4

  1. Write the hypotheses for testing if the average weights of babies born to smoking and non-smoking mothers are different.
- There is no difference in the mean of the birth weight between mothers who smoke and those who dont smoke
Ho:NULL Hypothesis : (??nonsmoker?????smoker) = 0 (i.e., ??NS=??S) 

- There is a difference in the mean of the birth weight between mothers who smoke and those who dont smoke
Ha:ALTERNATE Hypothesis : (??nonsmoker?????smoker) != 0 *=(i.e., ??NS?????S)

Next, we introduce a new function, inference, that we will use for conducting hypothesis tests and constructing confidence intervals.

inference(y = nc$weight, x = nc$habit, est = "mean", type = "ht", null = 0, 
          alternative = "twosided", method = "theoretical")
## Warning: package 'BHH2' was built under R version 3.5.3
## Response variable: numerical, Explanatory variable: categorical
## Difference between two means
## Summary statistics:
## n_nonsmoker = 873, mean_nonsmoker = 7.1443, sd_nonsmoker = 1.5187
## n_smoker = 126, mean_smoker = 6.8287, sd_smoker = 1.3862
## Observed difference between means (nonsmoker-smoker) = 0.3155
## 
## H0: mu_nonsmoker - mu_smoker = 0 
## HA: mu_nonsmoker - mu_smoker != 0 
## Standard error = 0.134 
## Test statistic: Z =  2.359 
## p-value =  0.0184

Let’s pause for a moment to go through the arguments of this custom function. The first argument is y, which is the response variable that we are interested in: nc$weight. The second argument is the explanatory variable, x, which is the variable that splits the data into two groups, smokers and non-smokers: nc$habit. The third argument, est, is the parameter we’re interested in: "mean" (other options are "median", or "proportion".) Next we decide on the type of inference we want: a hypothesis test ("ht") or a confidence interval ("ci"). When performing a hypothesis test, we also need to supply the null value, which in this case is 0, since the null hypothesis sets the two population means equal to each other. The alternative hypothesis can be "less", "greater", or "twosided". Lastly, the method of inference can be "theoretical" or "simulation" based.

0.3.3 Exercise 5

  1. Change the type argument to "ci" to construct and record a confidence interval for the difference between the weights of babies born to smoking and non-smoking mothers.
inference(y = nc$weight, x = nc$habit, est = "mean", type = "ci", null = 0, alternative = "twosided", method = "theoretical", order = c("smoker","nonsmoker"))
## Response variable: numerical, Explanatory variable: categorical
## Difference between two means
## Summary statistics:
## n_smoker = 126, mean_smoker = 6.8287, sd_smoker = 1.3862
## n_nonsmoker = 873, mean_nonsmoker = 7.1443, sd_nonsmoker = 1.5187

## Observed difference between means (smoker-nonsmoker) = -0.3155
## 
## Standard error = 0.1338 
## 95 % Confidence interval = ( -0.5777 , -0.0534 )
paste0("95% confidence interval is: ( 0.0534 , 0.5777 )")
## [1] "95% confidence interval is: ( 0.0534 , 0.5777 )"

By default the function reports an interval for (\(\mu_{nonsmoker} - \mu_{smoker}\)) . We can easily change this order by using the order argument:

inference(y = nc$weight, x = nc$habit, est = "mean", type = "ci", null = 0, 
          alternative = "twosided", method = "theoretical", 
          order = c("smoker","nonsmoker"))
## Response variable: numerical, Explanatory variable: categorical
## Difference between two means
## Summary statistics:
## n_smoker = 126, mean_smoker = 6.8287, sd_smoker = 1.3862
## n_nonsmoker = 873, mean_nonsmoker = 7.1443, sd_nonsmoker = 1.5187

## Observed difference between means (smoker-nonsmoker) = -0.3155
## 
## Standard error = 0.1338 
## 95 % Confidence interval = ( -0.5777 , -0.0534 )

0.4 On your own

0.4.1 Question 1

  • Calculate a 95% confidence interval for the average length of pregnancies (weeks) and interpret it in context. Note that since you’re doing inference on a single population parameter, there is no explanatory variable, so you can omit the x variable from the function.
inference(y = nc$weeks, est = "mean", type = "ci", conflevel = 95, null = 0, alternative = "twosided", method = "theoretical")
## Warning: Confidence level converted to 0.95.
## Single mean 
## Summary statistics:

## mean = 38.3347 ;  sd = 2.9316 ;  n = 998 
## Standard error = 0.0928 
## 95 % Confidence interval = ( 38.1528 , 38.5165 )

We can assume with 95% confidence, that the average length of pregnancies for the population is between 38.1528 and 38.5165.

0.4.2 Question 2

  • Calculate a new confidence interval for the same parameter at the 90% confidence level. You can change the confidence level by adding a new argument to the function: conflevel = 0.90.
inference(y = nc$weeks, est = "mean", type = "ci", conflevel = 0.90, null = 0, alternative = "twosided", method = "theoretical")
## Single mean 
## Summary statistics:

## mean = 38.3347 ;  sd = 2.9316 ;  n = 998 
## Standard error = 0.0928 
## 90 % Confidence interval = ( 38.182 , 38.4873 )

0.4.3 Question 3

  • Conduct a hypothesis test evaluating whether the average weight gained by younger mothers is different than the average weight gained by mature mothers.

The null and alternative hypothesis can be seen below: H0:??MM=??YM Ha:??MM?????YM where MM is mature mother and YM is young mother

inference(y = nc$gained, x = nc$mature, est = "mean", type = "ht", conflevel = 95, null = 0, alternative = "twosided", method = "theoretical")
## Response variable: numerical, Explanatory variable: categorical
## Warning: Confidence level converted to 0.95.
## Difference between two means
## Summary statistics:
## n_mature mom = 129, mean_mature mom = 28.7907, sd_mature mom = 13.4824
## n_younger mom = 844, mean_younger mom = 30.5604, sd_younger mom = 14.3469
## Observed difference between means (mature mom-younger mom) = -1.7697
## 
## H0: mu_mature mom - mu_younger mom = 0 
## HA: mu_mature mom - mu_younger mom != 0 
## Standard error = 1.286 
## Test statistic: Z =  -1.376 
## p-value =  0.1686

With 95% confidence interval, we see that the p-value > 0.05. Therefore, we cannot reject the null hypothesis, and there is not sufficient evidence to say that average weight gained is different between mature and younger moms.

0.4.4 Question 4

  • Now, a non-inference task: Determine the age cutoff for younger and mature mothers. Use a method of your choice, and explain how your method works.
#by(nc$mage,nc$mature,length)
#by(nc$mage,nc$mature,sd)
by(nc$mage,nc$mature,summary)
## nc$mature: mature mom
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   35.00   35.00   37.00   37.18   38.00   50.00 
## -------------------------------------------------------- 
## nc$mature: younger mom
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   13.00   21.00   25.00   25.44   30.00   34.00
by(nc$mage,nc$mature,range)
## nc$mature: mature mom
## [1] 35 50
## -------------------------------------------------------- 
## nc$mature: younger mom
## [1] 13 34
paste0("As can be seen that the range for mature moms is 35-50, and the range for young moms is 13-34. Theregore the cutoff is for young moms is 34 years old.")
## [1] "As can be seen that the range for mature moms is 35-50, and the range for young moms is 13-34. Theregore the cutoff is for young moms is 34 years old."
boxplot(mage ~ mature, data = nc, ylab = "Mother's age in years")

0.4.5 Question 5

  • Pick a pair of numerical and categorical variables and come up with a research question evaluating the relationship between these variables. Formulate the question in a way that it can be answered using a hypothesis test and/or a confidence interval. Answer your question using the inference function, report the statistical results, and also provide an explanation in plain language.

Lets compare the low birth weight with the weight gained by mothers to determine how much weight the mother gains during pregnancy affects with low birth weight babies and mothers with babies who are not considered low birth weight.

boxplot(gained ~ lowbirthweight, data = nc, xlab = "Weight Gained by Mother", ylab = "Low Birth Weights")

inference(y = nc$gained, x = nc$lowbirthweight, est = "mean", type = "ht", null = 0, alternative = "twosided", method = "theoretical")
## Response variable: numerical, Explanatory variable: categorical
## Difference between two means
## Summary statistics:
## n_low = 104, mean_low = 26.0769, sd_low = 14.4065
## n_not low = 869, mean_not low = 30.8343, sd_not low = 14.1444
## Observed difference between means (low-not low) = -4.7574
## 
## H0: mu_low - mu_not low = 0 
## HA: mu_low - mu_not low != 0 
## Standard error = 1.492 
## Test statistic: Z =  -3.189 
## p-value =  0.0014

Our p-value, .0014 < .05 so we reject our null hypothesis meaning there is enough evidence in the data to say that there is some difference in the average weight gained by mothers whose babies were considered low birth weight and those not.

inference(y = nc$gained, x = nc$lowbirthweight, est = "mean", type = "ht", null = 0, alternative = "greater", method = "theoretical") 
## Response variable: numerical, Explanatory variable: categorical
## Difference between two means
## Summary statistics:
## n_low = 104, mean_low = 26.0769, sd_low = 14.4065
## n_not low = 869, mean_not low = 30.8343, sd_not low = 14.1444
## Observed difference between means (low-not low) = -4.7574
## 
## H0: mu_low - mu_not low = 0 
## HA: mu_low - mu_not low > 0 
## Standard error = 1.492 
## Test statistic: Z =  -3.189 
## p-value =  0.9993