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Normal Distribution. Introduction. Probability Density Functions. Probability Density Functions…. Unlike a discrete random variable which we studied in Chapter 7, a continuous random variable is one that can assume an uncountable number of values.

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probability density functions1
Probability Density Functions…
  • Unlike a discrete random variable which we studied in Chapter 7, a continuous random variable is one that can assume an uncountable number of values.
  •  We cannot list the possible values because there is an infinite number of them.
  •  Because there is an infinite number of values, the probability of each individual value is virtually 0.
point probabilities are zero
Point Probabilities are Zero
  • Because there is an infinite number of values, the probability of each individual value is virtually 0.

Thus, we can determine the probability of a range of values only.

  • E.g. with a discrete random variable like tossing a die, it is meaningful to talk about P(X=5), say.
  • In a continuous setting (e.g. with time as a random variable), the probability the random variable of interest, say task length, takes exactly 5 minutes is infinitesimally small, hence P(X=5) = 0.
  • It is meaningful to talk about P(X ≤ 5).
probability density function
Probability Density Function…
  • A function f(x) is called a probability density function (over the range a ≤ x ≤ b if it meets the following requirements:
  • f(x) ≥ 0 for all x between a and b, and
  • The total area under the curve between a and b is 1.0






uniform distribution
Uniform Distribution…
  • Consider the uniform probability distribution (sometimes called the rectangular probability distribution).
  • It is described by the function:





area = width x height = (b – a) x = 1

  • The amount of petrol sold daily at a service station is uniformly distributed with a minimum of 2,000 litres and a maximum of 5,000 litres.
  • What is the probability that the service station will sell at least 4,000 litres?
  • Algebraically: what is P(X ≥ 4,000) ?
  • P(X ≥ 4,000) = (5,000 – 4,000) x (1/3000) = .3333





conditions for use of the normal distribution
Conditions for use of the Normal Distribution
  • The data must be continuous (or we can use a continuity correction to approximate the Normal)
  • The parameters must be established from a large number of trials
the normal distribution
The Normal Distribution…
  • The normal distribution is the most important of all probability distributions. The probability density function of a normal random variable is given by:
  • It looks like this:
  • Bell shaped,
  • Symmetrical around the mean …
the normal distribution1
The Normal Distribution…
  • Important things to note:

The normal distribution is fully defined by two parameters:

its standard deviation andmean

The normal distribution is bell shaped and

symmetrical about the mean

Unlike the range of the uniform distribution (a ≤ x ≤ b)

Normal distributions range from minus infinity to plus infinity

standard normal distribution



Standard Normal Distribution…
  • A normal distribution whose mean is zero and standard deviation is one is called the standard normal distribution.
  • Any normal distribution can be converted to a standard normal distribution with simple algebra. This makes calculations much easier.
normal distribution1
Normal Distribution…
  • Increasing the mean shifts the curve to the right…
normal distribution2
Normal Distribution…
  • Increasing the standard deviation “flattens” the curve…
calculating normal probabilities
Calculating Normal Probabilities…
  • Example: The time required to build a computer is normally distributed with a mean of 50 minutes and a standard deviation of 10 minutes:
  • What is the probability that a computer is assembled in a time between 45 and 60 minutes?
  • Algebraically speaking, what is P(45 < X < 60) ?


calculating normal probabilities1
Calculating Normal Probabilities…

…mean of 50 minutes and a

standard deviation of 10 minutes…

  • P(45 < X < 60) ?


distinguishing features
  • The mean ± 1 standard deviation covers 66.7% of the area under the curve
  • The mean ± 2 standard deviation covers 95% of the area under the curve
  • The mean ± 3 standard deviation covers 99.7% of the area under the curve

Tripthi M. Mathew, MD, MPH

68 95 99 7 rule
68% of the data

95% of the data

99.7% of the data

68-95-99.7 Rule
are my data normal
Are my data “normal”?
  • Not all continuous random variables are normally distributed!!
  • It is important to evaluate how well the data are approximated by a normal distribution
are my data normally distributed
Are my data normally distributed?
  • Look at the histogram! Does it appear bell shaped?
  • Compute descriptive summary measures—are mean, median, and mode similar?
  • Do 2/3 of observations lie within 1 stddev of the mean? Do 95% of observations lie within 2 stddev of the mean?
law of large numbers
Law of Large Numbers
  • Rest of course will be about using data statistics (x and s2) to estimate parameters of random variables ( and 2)
  • Law of Large Numbers: as the size of our data sample increases, the mean x of the observed data variable approaches the mean  of the population
  • If our sample is large enough, we can be confident that our sample mean is a good estimate of the population mean!

Stat 111 - Lecture 7 - Normal Distribution

points of note
Points of note:
  • Total area = 1
  • Only have a probability from width
    • For an infinite number of z scores each point has a probability of 0 (for the single point)
  • Typically negative values are not reported
    • Symmetrical, therefore area below negative value = Area above its positive value
  • Always draw a sketch!