Due: April 27, 2018 @ 11:59pm
For this homework assignment, you will use statistical inference to answer a question about the National Survey of Family Growth, Cycle 6 dataset published by the National Center for Health Statistics.
Obtain the Github repository you will use to complete homework 4 that contains a starter RMarkdown file named homework_4.Rmd, which you will use to do your work and write-up when completing the questions below. Remember to fill in your name at the top of the RMarkdown document and be sure to save, commit, and push (upload) frequently to Github so that you have incremental snapshots of your work. When you’re done, follow the How to submit section below to setup a Pull Request, which will be used for feedback.
About the dataset
This homework uses the National Survey of Family Growth, Cycle 6 dataset in the file 2002FemPreg.rds, published by the National Center for Health Statistics. Complete descriptions of all the variables can be found in the NSFG Cycle 6: Female Pregnancy File Codebook. Below are selected descriptions of variables that will be used for this homework assignment:
|caseid||integer ID of the respondent|
|prglngth||integer duration of the pregnancy in weeks|
|outcome||integer code for the outcome of the pregnancy, with a 1 indicating a live birth|
|birthord||serial number for live births; the code for a respondent’s first child is 1, and so on. For outcomes other than live birth, this field is blank|
This homework assignment revolves around answering the following question using this dataset:
Do first born children either arrive early or late when compared with non-first-borns?
The questions in this assignment will guide you through the process of answering this question using statistical inference.
Addressing the question “do first born children either arrive early or late when compared with non-first-borns?” means that we should only consider live births in the dataset. Filter the dataset so that it only contains outcomes with live births and assign this result to the variable
Next, we need to label all births in
live_birthsas either “first” or “other” so that we can easily find the children that are first borns and the ones that are not. There are a couple of different ways that you can label the births:
Split the dataset into two parts, a
first_birthsdataset and an
other_birthsdataset. Do this by applying a filter to extract the first births, then use
mutate()to create a new column called
birth_orderthat labels these rows as “first”. Then repeat this process, except apply a filter to extract all other births and label those as “other” in
birth_order. To recombine the datasets into one, use
mutate()to create the
birth_ordercolumn and the “first” and “other” labels. You can also try using
ifelse()to accomplish this. If you do it this way, you won’t need to use
After labeling the births, remove the extraneous variables from the
tibbleleaving just the
birth_ordercolumns. Assign the resulting
tibbleto a variable named
pregnancy_lengthdataset and plot a probability mass function (PMF) histogram of the pregnancy length in weeks that shows “first” births and “other” births on the same plot. Choose a reasonable binwidth for the histogram and add
coord_cartesian(xlim = c(27, 46))to your plot so that the window focuses where most of the data is. After creating the plot, describe the shape, center, and spread of the two distributions. Based on the visualization, do you think the data looks like it supports the statement that “first born children either arrive early or arrive late when compared with non-first-borns”?
Group the variable
prglngthinto “first” and “other” births and compute the summary statistics (mean, median, standard deviation, inter-quartile range, minimum, maximum) for each group. How do the different summary statistics compare between the two distributions? Does it look like there may be a notable difference between the two distributions? Explain.
Create theoretical quantile-quantile (q-q) plots to determine whether the pregnancy length distributions for “first” and “other” births are nearly normal. You should make separate q-q plots for “first” births and “other” births (i.e. you should end up with two plots).
Each q-q plot should include a reference line that shows what the theoretical normal distribution looks like so that we can observe deviations from normality. You’ll need to compute this line manually. Adapt the following code to compute the slope and intercept for the theoretical line (you will need to do this twice, once for “first” births and again for “other” births):
qq_x <- qnorm(p = c(0.25, 0.75)) qq_y <- quantile(x = pull(dataset, variable), probs = c(0.25, 0.75), type = 1) qq_slope <- diff(qq_y) / diff(qq_x) qq_int <- pluck(qq_y, 1) - qq_slope * pluck(qq_x, 1)
“Adapting the code” means you will have to change the
variableparameters. Once you have found
qq_intfor “first” and “other” births, use
geom_abline()to include the theoretical reference line in your q-q plots.
Based on your plots, does it appear that the pregnancy length distribution is nearly normal for both “first” and “other” births? Why or why not?
Returning back to the question of whether or not “first babies arrive early or arrive late when compared with non-first-borns”, plot the cumulative distribution functions (CDFs) of the pregnancy lengths for “first” and “other” births. Plot the CDF for both distributions on the same figure so that we can directly compare them. How do the distributions compare? Does it look like there is there a meaningful difference between the two distributions?
If we want to determine whether or not the difference between two distributions is statistically significant, we need to run a hypothesis test. Formalize your analysis by writing down the null hypothesis for the question of whether first babies arrive early or arrive late when compared with non-first-borns. It should be clear from how you write your null hypothesis whether you’re conducting a one-sided or two-sided hypothesis test. You should also restate what the observed value is (this will be a number) that you will be comparing against the null distribution.
Use a simulation to generate the null distribution so that you can perform the hypothesis test. Do this using the functions provided in the
calculate(). To collect enough statistics, you should set the simulation to repeat 10,000 times.
Once you’ve obtained the null distribution, use it to compute the p-value for your hypothesis test. Assuming a significance level of \(\alpha = 0.05\), state whether we can we reject the null hypothesis.
Finally, visualize the simulated null distribution as a histogram and use
geom_vline()to show where the observed value sits relative to it.
Use a bootstrap simulation to calculate the 95% confidence interval for your observed value. Do this using the following functions from the
calculate(). To collect enough statistics, you should set the bootstrap simulation to repeat 10,000 times.
Once you’ve obtained the bootstrap distribution, use
quantile()to find the upper and lower bounds of the 95% confidence interval. Does the value corresponding to the null result fall within the range of the 95% confidence interval?
Finally, visualize the bootstrap distribution as a histogram and use two
geom_vline()s to show the region corresponding to the 95% confidence interval.
In addition to hypothesis tests and confidence intervals, we should also consider the effect size, which measures the relative difference between two distributions. The effect size helps us better know how important a given result actually is, not just whether or not we can reject the null hypothesis. One measure of the effect size is called Cohen’s d ( https://en.wikipedia.org/wiki/Effect_size#Cohen.27s_d), which we will use to compute the effect size between the pregnancy lengths for “first” and “other” births. The different ranges of d can be interpreted using the following table:
Effect size d Very small 0.01 Small 0.20 Medium 0.50 Large 0.80 Very large 1.20 Huge 2.00
The following set of functions should also be preloaded for you:
plot_ci(). These functions will use bootstrap simulations to compute the confidence interval for the Cohen’s d parameter. Run the bootstrap simulation as follows:
cohens_d_bootstrap(data = pregnancy_length, model = prglngth ~ birth_order)
Be sure to assign the results to a variable, for example
To print a report for the bootstrap simulation, run:
To visualize the bootstrap distribution and confidence interval, run:
Using the provided table, report how large the effect size is for the difference in pregnancy lengths for “first” and “other” births.
Having completed your analysis, write a paragraph that summarizes your results and answers the question “do first born children arrive early or late compared to other children?” Write the summary as if you are an academic or scientific journalist, focusing on how you can answer the question clearly, precisely, and honestly.
How to submit
When you are ready to submit, be sure to save, commit, and push your final result so that everything is synchronized to Github. Then, navigate to your copy of the Github repository you used for this assignment. You should see your repository, along with the updated files that you just synchronized to Github. Confirm that your files are up-to-date, and then do the following steps:
Click the Pull Requests tab near the top of the page.
Click the green button that says “New pull request”.
Click the dropdown menu button labeled “base:”, and select the option
Confirm that the dropdown menu button labeled “compare:” is set to
Click the green button that says “Create pull request”.
Give the pull request the following title: Submission: Homework 4, FirstName LastName, replacing FirstName and LastName with your actual first and last name.
In the messagebox, write: My homework submission is ready for grading @shuaibm @jkglasbrenner.
Click “Create pull request” to lock in your submission.
You are encouraged to review and keep the following cheatsheets handy while working on this assignment: