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Analyzing data in Statwing
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Select variables
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Analyze
Describe
produce a summary of each selected variable
Relate
relate the variable to each other selected variable
Video Tutorial
produce a summary of each selected variable
relate the variable to each other selected variable
Add filters to analyze a specific subset of your data.
For example, you may have done a survey of all people in the US and you may want to focus only on men in Maryland. Create a filter where say your 'gender' variable equals 'men' and 'state' is 'Maryland'. Apply it and new analyses will be run on that subset of your data!
You can create and switch between multiple filters to quickly segment and drill down your data.
You can change variables to the following types:
| Category | a grouping (like gender or political party) | |
| Number | points on a scale (like weight or dollars) or a count (like number of wins) | |
| Rank | ordered categories (like military ranks) or rank ordering (like place in a race) | |
| Time | for dates and times (like date of birth or time of measurement taken) | |
| ID | for unique identifiers (like a social security number); cannot be used for analysis | |
| Text | for arbitrary text fields (like tweets or comments); cannot currently be used for analysis |
Categories may have a particular order in which you want to display them. Use the reorder settings to set that order.
You may want to ignore certain values from being analyzed. For example, in surveys the answer “Don't Know” is often represented by a “99.”
Use the missing value settings below to tell which values Statwing should treat as missing and ignore.
If you're analyzing a survey that under- or over-represents certain groups (e.g., sex, race), you can use weights to make sure your sample represents the population accurately. You can only have one weight variable set at a time. Don't weight your data unless you know what you're doing.
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