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In Excel =2*NORMSDIST(2) puts out ~1.96. How do I get that same value in R?
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The NORMDIST Function[1] is categorized under Excel Statistical functions. It will return the normal distribution for a stated mean and standard distribution. That is, it will calculate the normal probability density function or the cumulative normal distribution function for a given set of parameters.
NORMSINV is an Excel function that provides a Z value for a cumulative probability using a standard normal distribution. If you assume your data is normally distributed and are interested in knowing the Z value for a given probability, NORMSINV will provide that using the cumulative probabilities of the distribution.
The syntax for the NORMSINV function is: =NORMSINV(x, mean, standard_deviation). x is the value for which you want to find the probability, mean is the mean of the standard normal distribution, and standard_deviation is the standard deviation of the standard normal distribution.
NORMSDIST is a function in Excel that calculates the normal distribution for a given set of data. To use NORMSDIST, you first need to input the data that you want to use for the calculation. Then, you need to specify the mean and standard deviation of the data.
Returns the standard normal cumulative distribution function. The distribution has a mean of 0 (zero) and a standard deviation of one. Use this function in place of a table of standard normal curve areas.
If you want to get the two-sided 95% confidence limit for a z-score, you would use =NORMSINV(0.975) (which is =NORMSINV(1 - (1-0.95)/2)). The fact that your formula gives approximately the same number is nothing more than a coincidence.
The equivalent in R would be qnorm(0.975) or qnorm(1 - (1-0.95)/2).
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You can get that value in R with
2*pnorm(2)
The pnorm() function is the cumulative density function for a normal random variable
Which is consistent with the description of the Excel NORMDIST function
NORMSDIST(z) returns the probability that the observed value of a standard normal random variable will be less than or equal to z. A standard normal random variable has mean 0 and standard deviation 1 (and also variance 1 because variance = standard deviation squared).
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