The `NORMINV` function in Excel is used to find the inverse of the cumulative normal distribution function. Essentially, it returns the value (x) associated with a given probability in a normal distribution characterized by a specified mean and standard deviation. This function is particularly useful in statistical analyses and scenarios where you need to find specific data points given a probability.
Here’s how to use the `NORMINV` function in Excel:
Syntax
NORMINV(probability, mean, standard_dev)
- probability: The probability corresponding to the normal distribution. This is a cumulative probability and must be between 0 and 1.
- mean: The arithmetic mean or average of the normal distribution.
- standard_dev: The standard deviation of the normal distribution, a measure of dispersion.
Steps to Use NORMINV
- Open Excel and select the cell where you want the result.
- Enter the function:
- Start by typing `=NORMINV(` in the cell.
- Fill in the probability argument. This is the cumulative probability for which you want to find the value. For example, if you want to know the data point for the 95th percentile, you’d enter `0.95`.
- Add a comma `,`.
- Enter the mean of your dataset.
- Add another comma `,`.
- Enter the standard deviation of your dataset.
- Close the parentheses `)` and press Enter.
Example
Suppose you have a normal distribution with a mean of 100 and a standard deviation of 15, and you want to find the data point corresponding to the 95th percentile.
You would enter:
=NORMINV(0.95, 100, 15)
Once you hit Enter, Excel will calculate and display the data point corresponding to the 95th percentile of your normal distribution.
Important Notes
- Ensure your inputs for mean and standard deviation are correct, as incorrect inputs will yield inaccurate results.
- `NORMINV` was replaced in Excel 2010 and later by `NORM.INV`, but `NORMINV` will still work for backward compatibility.
This function is very useful for determining thresholds, cutoffs, or critical values in normally distributed data sets. Be careful when interpreting probabilities and ensure you understand the assumptions of a normal distribution.
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