1. CONTINUOUS RANDOM VARIABLES
AND THEIR PROBABILITY DENSITY FUNCTIONS(P.D.F.)

2. REMINDER
A discrete random variable is one whose possible values either constitute a finite set [e.g. E = {2, 4, 6, 8, 10}] or else can be listed in an infinite sequence [e.g. N = {0, 1, 2, 3, 4, …}]. A random variable whose set of possible values is an entire interval of numbers is not discrete.

3. CONTINUOUS RANDOM VARIABLES
A random variable X is said to be continuous if its set of possible values is an entire interval of numbers – that is, if for some a < b, any number x between a and b is possible.
FOR EXAMPLE: [2,5]; (- 4, 7); [24, 71); (11, 31].

4. DEFINITION: PROBABILITY DENSITY FUNCTION (P.D.F.)
The function f(x) is a probability density function for the continuous random variable X, defined over the set of real numbers R, if

5. PROBABILITY DENSITY FUNCTION FOR A CONTINUOUS RANDOM VARIABLE, X.

6. EXAMPLES FROM PRACTICE SHEET

7. SOME CONTINUOUS PROBABILITY DISTRIBUTION FUNCTIONS
UNIFORM PROBABILITY DISTRIBUTION FUNCTION;
EXPONENTIAL PROBABILITY DISTRIBUTION FUNCTION;
NORMAL PROBABILITY DISTRIBUTION FUNCTION.

8. UNIFORM PROBABILITY DISTRIBUTION FUNCTION
A continuous random variable, r.v. X, is said to have a uniform distribution on the interval [a,b] if the probability density function, p.d.f. of X is

9. UNIFORM PROBABILITY DENSITY FUNCTION

10. EXAMPLES FROM PRACTICE SHEET

11. EXPONENTIAL PROBABILITY DENSITY FUNCTION
The continuous random variable X has an exponential distribution, with parameter >0 if its density function is given by

12. EXPECTED VALUE, E(X), AND VARIANCE, VAR(X), OF A CONTINUOUS RANDOM VARIABLE, X, EXPONENTIALLY DISTRIBUTED

13. EXAMPLES FROM PRACTICE SHEET

14. NORMAL PROBABILITY DISTRIBUTION FUNCTION,

15. STANDARD NORMAL PROBABILITY DENSITY FUNCTION, N(0,1)

16. Z – SCORES OR STANDARDIZED SCORES

17. REMARKS
Probably the most important continuous distribution is the normal distribution which is characterized by its “bell-shaped” curve. The mean is the middle value of this symmetrical distribution.
When we are finding probabilities for the normal distribution, it is a good idea first to sketch a bell-shaped curve. Next, we shade in the region for which we are finding the area, i.e., the probability. [Areas and probabilities are equal] Then use a standard normal table to read the probabilities.

18. REMARKS CONTINUED AREA UNDER N(0,1) = 1
PROBABILITY OF A CONTINUOUS RANDOM VARIABLE, NORMALLY DISTRIBUTED = AREA UNDER THE BELL SHAPED CURVE.
STANDARD NORMAL TABLES GIVE AREAS OR PROBABILITIES TO THE LEFT OF THE Z – SCORES AND TO FOUR DECIMAL PLACES.

19. EMPIRICAL RULE OR THE 68 – 95 – 99.7% RULE

20. EMPIRICAL RULE OR 68 – 95 – 99.7% RULE
IN A NORMAL MODEL, IT TURNS OUT THAT 
1. 68% OF VALUES FALL WITHIN ONE STANDARD DEVIATION OF THE MEAN;
2. 95% OF VALUES FALL WITHIN TWO STANDARD DEVIATIONS OF THE MEAN;
% OF VALUES FALL WITHIN THREE STANDARD DEVIATIONS OF THE MEAN.

21. MEAN OR EXPECTED VALUE, E(X), VARIANCE, VAR(X), AND STANDARD DEVIATION SD(X),OF A CONTINUOUS RANDOM VARIABLE X.