Chapter 6Formulas and Vocabulary

• Continuous random variable (p. 311)
• Discrete random variable (p. 311)
• Expected value, or expectation, of a random variable (p. 318). Denoted as ().
• Mean of a discrete random variable (p. 315).
  
• Probability distribution of a discrete random variable (p. 312)
• Random variable (p. 310)
• Rules for a discrete probability distribution (p. 313).
  and

• Variance and standard deviation of a discrete random variable (p. 319).

• Binomial experiment (p. 327)
• Binomial probability distribution formula (p. 330).
  
• Factorial (!) (p. 329).
• Mean, variance, and standard deviation of a binomial random variable (p. 334).

  Mean (or expected value):

  Variance:

  Standard deviation:

• Number of combinations (p. 329).

• Mean, variance, and standard deviation for a Poisson random variable (p. 344). Mean = , Variance = , .

• Poisson probability distribution formula (p. 342).

• Continuous probability distribution (p. 349)
• Density curve (p. 349)
• Law of Total Probability for Continuous Random Variables (p. 349)
• Probability for continuous distributions (p. 350)
• Properties of the normal density curve (normal curve) (p. 352)
• Requirements for a continuous probability distribution (p. 349)
• Uniform probability distribution (p. 349)
• Standard normal () distribution (p. 354)
• table for the standard normal random variable (p. 355)

• Finding normal data values for specified probabilities (p. 372)
• Finding probabilities for any normal distribution (p. 369)

• Standardizing a normal random variable (p. 368).

• Normal probability plot (p. 379)

• Normal approximation to the binomial probability distribution (p. 386)