Excel and Statistics, everybody should know. : Contents
Data validation
Select the cells where you want to enter the age and click ‘Data Validation’ under ‘Data ‘.
Select ‘whole number’ or ‘Decimal’.
If you specify a minimum and maximum value, the rest cannot be entered.
You can also enter an explanatory message as you wish.
The moment you select to enter, you will see ‘Explanatory text’. If the input is not in this range, an ‘error message’ is displayed and is not entered.
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Set ‘Restricted to’ to ‘List‘ and enter the appropriate values.
Now, when you select a cell to enter, a triangle appears on the right, which you can click to see the values in the list to help you enter. Clicking on one of the presented ones to enter is convenient and eliminates mistakes at the source. You can enter directly from the keyboard, but errors will occur if you misspell it, so it prevents incorrect data from being entered at the source.
As for ‘data validation’, you only need to know the above, and if you try it yourself, you can quickly understand the rest of the functions.
Finding and correcting incorrect data entries is cumbersome, and very important. It’s best to avoid entering incorrect data in the first place, and ‘Data Validation’ helps with that.
During research, researchers sometimes forget the contents of coding as the data collection period increases, researchers change in the middle, and when various researchers perform it, ‘data validation’ is a very useful tool for communicating with each other, and once you get used to it, it is very intuitive so that you can remember it naturally without having to memorize it.
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- R statisics portal https://tinyurl.com/stat-portal
- R data visualization book 1 https://tinyurl.com/R-plot-I (chart)
- R data visualization book 2
https://tinyurl.com/R-plot-II-3-4 many variables / map
https://tinyurl.com/R-plot-II-5-6 time related / statistics related
https://tinyurl.com/R-plot-II-7-8 others / reactive chart
- R data visualization book 3 https://tinyurl.com/R-data-Vis3
- R data visualization book 4 R 데이터 시각화 4권
- Meata Analysis book 1 https://tinyurl.com/MetaA-portal
- Meata Analysis book 2 https://tinyurl.com/MetaA-portal(2)
- Preciction Model and Machine Learning https://tinyurl.com/Machine-Learning-EZ
- Sample Size Calculations https://tinyurl.com/MY-sample-size
- Sample data https://tinyurl.com/data4edu
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