normal_p: Find the Proportion of Observations Above or Below a Point...

In jrpriceUPS/Math160UPS: Suite of Functions for Math 160 Classes at the University of Puget Sound

Find the Proportion of Observations Above or Below a Point for a Normal Distribution

Description

This function is one of those chosen through prompts by normal(). If one is looking for the proportion of observations expected to fall above or below a particular value for a distribution that is normal, one can use this function to calculate and visualize that.

Usage

normal_p(x, mu, sigma, type, print = TRUE)

Arguments

x	the point of interest
mu	the mean of the normal distribution
sigma	the standard deviation of the normal distributions
type	whether we are looking for the proportion above or below the chosen point. Options are 'less', 'greater', or 'both'. 'both' is used by the z-test function to find more extreme points.
print	whether or not to print output to the screen. Defaults to TRUE. Typically we want to print the results, but this is also called by the z-test function, in which case we wish to print something else.

Value

the probability of finding a value in the desired interval and the pnorm() command to find that result.

Examples

> output = normal_p(-1.5,0,1,'less',TRUE)
The proportion of observations with a value of -1.5 or more extreme is 0.0668072

You can get this result by typing:
 pnorm(-1.5,0,1)


> output = normal_p(-1.5,0,1,'greater',TRUE)
The proportion of observations with a value of -1.5 or more extreme is 0.9331928

You can get this result by typing:
  1-pnorm(-1.5,0,1) 

jrpriceUPS/Math160UPS documentation built on April 28, 2024, 12:41 p.m.