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PDF 10: CDFs The Normal Distribution

Discrete random variable Continuous random variable Probability mass function (PMF): Probability density function (PDF): To get probability: = = To get probability: ≤ ≤ = Both are measures of how likely is to take on a value

PDF Choosing whether to use normalcdf or invNorm

Choosing whether to use normalcdf or invNorm • The area under the normal bell curve can represent either a probability or a percentage • Use the normalcdf function to find the area under the curve when two “bounds” are known • On Texas Instrument graphing calculators press 2nd then VARS select 2:normalcdf

PDF Table 1: Table of the Standard Normal Cumulative Distribution

Table 1: Table of the Standard Normal Cumulative Distribution Function '(z)z 0 00 0 01 0 02 0 03 0 04 0 05 0 06 0 07 0 08 0 09-3 4 0 0003 0 0003 0 0003 0 0003 0 0003

PDF Using the normalcdf function on the TI-84

Slide 1 Using the normalcdf function on the TI-84 These instructions will work for the TI-83 and TI-84 families of calculators Read the problem carefully: Consider the weights of 18 month old boys in the U S According to published growth charts the average weight is approximately 11 8 kg with standard deviation of 1 28 kg

  • What is the difference between PDF and CDF?

    3. PDF and CDF of The Normal Distribution The probability density function (PDF) and cumulative distribution function (CDF) help us determine probabilities and ranges of probabilities when data follows a normal distribution. The CDF is the integration, from left to right, of the PDF.

  • How do you know if a PDF is a normal distribution?

    When the PDF is graphically portrayed, the area under the curve will indicate the interval in which the variable will fall. A continuous random variable X is said to follow the normal distribution if it’s probability density function (PDF) is given by: The variable µ is the mean of the data values.

  • How do I use normalcdf in a TI-84 calculator?

    normalcdf (lower_x, upper_x, μ, σ) returns the cumulative probability associated with the normal cdf between two values. Both of these functions can be accessed on a TI-84 calculator by pressing 2nd and then pressing vars. This will take you to a DISTR screen where you can then use normalpdf () and normalcdf ():

  • How do you calculate CDF?

    The CDF value corresponds to the sum of the area under a normal distribution curve (integration). So, we divide the whole area under the curve into small panels of a fixed width, and we add up all those individual panels to get the total area under the curve. The smaller the width of the panel, the more accurate the integration will be.

Discrete vs Continuous RVs

Discrete random variable Continuous random variable Probability mass function (PMF): Probability density function (PDF): To get probability: = = To get probability: ≤ ≤ = Both are measures of how likely is to take on a value. web.stanford.edu

Continuous RV

= = ( ) = ( ) ( ) = Both continuous and discrete RVs + = + Var( ) = ( − [ ]) = Var( + ) = − ( [ ]) Var( ) web.stanford.edu

Uniform Random Variable

def An Uniform random variable in an interval from to . takes on all values, with equal likelihood, web.stanford.edu

Exponential Random Variable

Consider an experiment that lasts a duration of time until success occurs. def An Exponential random variable is the amount of time until success. web.stanford.edu

Cumulative Distribution Function

A cumulative distribution function (CDF) is a “closed form” equation for the probability that a random variable is less than a given value. For a continuous random variable, the CDF is: = ( ≤ ) = Also written as: web.stanford.edu

Why the Normal?

Common for natural phenomena: height, weight, etc. Most noise in the world is Normal Often results from the sum of many random variables Sample means are distributed normally That’s what they want you to believe

Complexity is Tempting

This line describes this data well, but will it generalize? web.stanford.edu

Fewest Assumptions

A Gaussian maximizes entropy for a given mean and variance Fewest Assumptions A Gaussian makes the fewest assumptions after matching mean and variance web.stanford.edu

= = 0 Var = = 1

Other names: Unit Normal Note: not a new distribution; just a special case of the Normal CDF of defined as: ≤ = Φ( ) web.stanford.edu

Interval of Phi

< < ( = − ( ) ) Probabilities for a general Normal RV web.stanford.edu

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