#Forgot spss code software
I guess the way I see it is that using a large negative integer avoids interfering with the automatic typing of variables across most software setups at the cost of needing to take an extra step to flag that value as missing. The only problem with using NAs for missing values in the raw data is that other programs (e.g., SPSS) don’t recognize “NA” as missing automatically (I think). If a column only contains integers and NAs, then it will correctly treat the column as an integer. Of course, the only exception is the string “NA”, which it knows to treat as missing. So, if R detects a single unfamiliar string in a column, it just assumes that the entire column should be treated as a string. For example, AFAIK every kind of number can be treated as a string with no inherent order or scale (e.g., ID codes), but not every string can be treated as a number. However, there are inherent assumptions in an automatic process like that. I use R and for the most part R will automatically identify the correct type when importing a dataset. (Note: they do this for *totals* it’s not just “average death rate” I’m talking about.) They don’t go on to say it, but of course this means their reported deaths numbers for the past 2 months are pretty much worthless. They explain that the data is added as it comes in from the states, and that takes 1 to 8 weeks. In their online official list of covid19 deaths, check out the latest week. This reminds me of the CDC, except the CDC isn’t that smart.
#Forgot spss code code
Eyeballing the data, the user would see that “-66” was a nonsense value, and, being clever, would deduce that it must be the code for “missing value”. I have to admit I might do that myself in a dataset for personal use, though I at least would use “-999”. Instead, he had the bright idea of putting something like the integer “-66” or “-77” to represent missing values.
I’m guessing, but I can imagine some foolish person decided that the best way to code “Missing” was not to use the string “missing”, because if he did that, then whoever ran a regression would have the app return an error like “Your data includes string values” instead of giving an answer.
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