Selecting the Use in Formula dropdown list on the Formulas ribbon. This post explores three such solutions, and if you have aHere are some other options you have from the Defined Names group on the Formulas. Overview As with just about anything in Excel, there are several ways to achieve the goal. This post explores macro-free methods for using Excel’s data validation feature to create an in-cell drop-down that displays choices depending on the value selected in a previous in-cell drop-down.
Have Drop Down In Excel That Influences Other Cells Manual As WellNever clean a dataset by blindly entering commands (or worse, clicking buttons). Simply type “help” in the command window, followed by the name of the command you need help with and press the Enter key:Write a do file. Stata has a built in feature that allows you to access the user manual as well as help files on any given command. Your first pass at a dataset may involve any or all of the following:Whether this is your first time cleaning data or you are a seasoned “data monkey”, you might find some useful tips by reading more.Some useful tips before you get started Use the Stata help file. Stata is a good tool for cleaning and manipulating data, regardless of the software you intend to use for analysis.The first, “clear”, is used to clear any previous dataset you may have been working on. These three commands are administrative commands that are quite useful to have at the beginning of a do-file. To run a whole do-file, do not highlight any part of it and click on the “Execute Selection (do)” icon.You may wonder about the commands “clear”, “set more off” and “set mem 15000” in the screenshot example. In order to execute a number of commands rather than the whole do-file, simply highlight the ones you want to execute, and click on the “Execute Selection (do)” icon on the top of your do-file editor, at the far right.As you become more proficient with programming in Stata, you won’t need to try out commands anymore, and you’ll discover the joy of writing a do-file and having it run without a glitch.Keeping a log means you can go back and look at what you did without having to do it again. Again, this is a general rule of thumb on Stata. Finally, the command “set mem 15000” increases the memory available to Stata from your computer here we will need it as the size of the data set we downloaded from is larger than the 10mb allocated to data by default.One last comment about do files: if you double click a saved do file, it will not open for editing, but rather Stata will run that do-file, which can be a bit annoying… To reopen a do-file from a folder without executing the commands in it, right-click on it and select “edit” rather than “open”.Always keep a log. Saving a do file is done the same way as saving any text editor document: either click on the diskette icon, or press “CTRL+S”:You should also save your dataset as you modify it, but make sure to keep one version of the original dataset, in case you need to start over. Save your do-files every few minutes as you write them. Computers crash, power goes out, stuff happens. The last command on your do-file will usually be “log close”.Save as you go. A good practice is to actually look at the data, so that you understand the structure of the information. You should know what each variable is, how it’s coded, how missing values are identified. Datasets come with codebooks. Become familiar with your dataset. To make this clearer, let’s look at the data with no labels. Basically, the value label sits on top of the code, so that when you browse, you see what the code means rather than what it is. Variable labels are descriptions of variables, and value labels are used to describe the way variables are coded. A new window will open and you can see your data.You can also use the command “browse”, either by typing it directly in the command window, or from a do file:One of the distinguishing features of is that when you download a dataset, it comes with labels. This can be a problem if you are not 100% certain of what you want to keep.The “drop” command will drop from your dataset what you specifically ask Stata to drop.The “if” qualifier restricts the scope of the command to those observations for which the value of an expression is true. Now, let’s look at how you would do that.To drop observations, you need to combine one of two Stata commands (keep or drop) with the “if” qualifier.Make sure you have saved your original dataset before you get started.The “keep” command should be used with caution (or avoided altogether) because it will drop all but what you specifically keep. Furthermore, you can probably drop women under 15 and over 55 years old. You clearly don’t need to keep the men in your dataset, and you won’t need to keep the residents of provinces other than Ontario. Your research agenda determines what your final dataset will contain.Let’s say you have data on the health habits of Canadians aged 12 and up, but your research question is specific to women of reproductive age living in Ontario. Your goal is to make your dataset as small as possible, while keeping all the relevant information. For example, say you want to create a variable that tells you whether the women in the dataset have a live-in partner. You can combine these with qualifiers such as “if” or “in” as well as prefix such as “by” and “bysort”. Ignore it if the height variable is not actually that important in your research and the rest of the variables for this observations are coded just fineThere are two main commands you need to know to generate new variables: “gen” is for the basics, while “egen” allows you to get pretty fancy. Use the “if” qualifier to exclude it when generating statistics that use the height variable (“ command if hwtghtm<=1.803”) A common mistake is to ask Stata to “drop if DHHGAGE>10 &DHHGAGE1.803”) Using the example of women of reproductive age in Ontario, the first highlighted line drops men, the second line drops any observation not in Ontario, while the last line drops observations in age groups older or younger than our subset of interest.You have to be careful with logical operators notice the syntax in the third line. Free scrabble download for macThe second line replaces the missing value code by 0, making the “livein” variable binary.Now, let’s say you would like to create a categorical variable that tells you, by age group, if a woman is below or above average in terms of body mass index (BMI).The first line of command creates a variable (meanbmi) which takes on a unique value for each age group, the average BMI for that age group.
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