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Multiple datasets in memory in Stata 16
You can now load multiple datasets into memory. You type
. use people
and people.dta is loaded into memory. Next, you type
. frame create counties
. frame counties: use counties
and you have two datasets in memory. people.dta is in the frame named default, and counties.dta is in the frame named counties. Your current frame is still default. Most Stata commands use the data in the current frame. For example, if you typed
. list
then people.dta will be listed. If you typed
. frame counties: list
then counties.dta will be listed. Or you could make counties the current frame by typing
. frame change counties
and list will now list the counties data.
Navigating frames is easy and so is linking them. Imagine that both datasets have a variable named countycode that identifies counties in the same way. Type
. frlink m:1 countycode, frame(counties)
and each person in the default frame is linked to a county in the counties frame. This means you can now use the frget command to copy variables from the counties frame to the current frame. Or you can use the frval() function to directly access the values of variables in the counties frame. For instance, if we have each individual’s income in the default frame and median county income in the counties frame, we can generate a new variable containing relative income by typing
. generate rel_income = income / frval(counties, median_income)
This is the beginning. While this example uses only two frames, you can have up to 100 frames in memory at once, and you can have many links among those frames.

New reporting features in Stata 16:
• The dyndoc and markdown commands now create Word documents in addition to the HTML documents they previously created. Now, you can easily incorporate full Stata output and graphs with Markdown-formatted text to create customized Word documents.
• The Do-file Editor now provides syntax highlighting for Markdown language elements.
• The putdocx command now lets you include headers, footers, and page numbers. It also makes it easier to write large blocks of text.
• The html2docx command converts HTML documents, including CSS, to Word documents.
• The docx2pdf command converts Word documents to PDFs.

Frequentist analysis is entirely data-driven and strongly depends on whether or not the data
assumptions required by the model are met. On the other hand, Bayesian analysis provides a more
robust estimation approach by using not only the data at hand but also some existing information or
knowledge about model parameters.
In frequentist statistics, estimators are used to approximate the true values of the unknown parameters,
whereas Bayesian statistics provides an entire distribution of the parameters. In our example of a
prevalence of an infectious disease from What is Bayesian analysis?, frequentist analysis produced one
point estimate for the prevalence, whereas Bayesian analysis estimated the entire posterior distribution
of the prevalence based on a given sample.

We are excited to introduce you to the new features in Stata 16. Below, we list highlights of the release. In what follows, we tell you a little more about the first 13 of them. We introduce each feature using words that you might also use as you introduce them to existing and potential Stata users.
The majority of these features will be exciting to researchers in all disciplines. Where appropriate, we will highlight which disciplines will be most interested or provide advice about how different groups of users will relate to the feature. We
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