4.6.2 Calculations with Normal Distribution

Throughout this section we will use the random variable . For X distributed normally, X can take any real number. Therefore any values mentioned in this section will be assumed to be real numbers.

How do I find probabilities using a normal distribution?

• The area under a normal curve between the points  and  is equal to the probability 
  • Remember for a normal distribution you do not need to worry about whether the inequality is strict (< or >) or weak (≤ or ≥)
    • 
• You will be expected to use distribution functions on your GDC to find the probabilities when working with a normal distribution

How do I calculate P(X = x): the probability of a single value for a normal distribution? 

• The probability of a single value is always zero for a normal distribution
  • You can picture this as the area of a single line is zero
• 
• Your GDC is likely to have a "Normal Probability Density" function
  • This is sometimes shortened to NPD, Normal PD or Normal Pdf
  • IGNORE THIS FUNCTION for this course!
  • This calculates the probability density function at a point NOT the probability

How do I calculate P(a < X < b): the probability of a range of values for a normal distribution? 

• You need a GDC that can calculate cumulative normal probabilities
• You want to use the "Normal Cumulative Distribution" function
  • This is sometimes shortened to NCD, Normal CD or Normal Cdf
• You will need to enter:
  • The 'lower bound' - this is the value a
  • The 'upper bound' - this is the value b
  • The 'μ' value - this is the mean
  • The 'σ' value - this is the standard deviation
• Check the order carefully as some calculators ask for standard deviation before mean
  • Remember it is the standard deviation
    • so if you have the variance then square root it
• Always sketch a quick diagram to visualise which area you are looking for

How do I calculate P(X > a) or P(X < b) for a normal distribution? 

• You will still use the "Normal Cumulative Distribution" function
• can be estimated using an upper bound that is sufficiently bigger than the mean
  • Using a value that is more than 4 standard deviations bigger than the mean is quite accurate
  • Or an easier option is just to input lots of 9's for the upper bound (99999999... or 10⁹⁹)
• can be estimated using a lower bound that is sufficiently smaller than the mean
  • Using a value that is more than 4 standard deviations smaller than the mean is quite accurate
  • Or an easier option is just to input lots of 9's for the lower bound with a negative sign (-99999999... or -10⁹⁹)

Are there any useful identities?

• 
• As  you can use:
  • 
  • 
  • 
• These are useful when:
  • The mean and/or standard deviation are unknown
  • You only have a diagram
  • You are working with the inverse distribution

Given the value of P(X < a) how do I find the value of a? 

• Your GDC will have a function called "Inverse Normal Distribution"
  • Some calculators call this InvN
• Given that  you will need to enter:
  • The 'area' - this is the value p
    • Some calculators might ask for the 'tail' - this is the left tail as you know the area to the left of a
  • The 'μ' value - this is the mean
  • The 'σ' value - this is the standard deviation

Given the value of P(X > a)  how do I find the value of a? 

• Given 
• Use to rewrite this as
  • 
• Then use the method for P(X < a) to find a
• If your calculator does have the tail option (left, right or centre) then you can use the "Inverse Normal Distribution" function straightaway by:
  • Selecting 'right' for the tail
  • Entering the area as 'p'