The Practice of Statistics



Chapter 16: Random Variables

Key Vocabulary:

|random variable |probability model |standard deviation |

|discrete random variable |expected value |continuous random variable |

Calculator Skills:

|1-VarStats L1, L2 | | |

1. What is meant by a random variable?

2. Explain the difference between a discrete random variable and a continuous random variable.

3. What information does a probability model give?

4. What is the expected value of a random variable?

5. How do you calculate the expected value of a random variable?

6. What is the variance of a probability distribution?

7. How do we calculate the standard deviation of a probability distribution?

8. Explain the meaning of the formulas [pic] and [pic].

9. Explain the meaning of the formulas [pic] and [pic].

10. The sum (or difference) of two random variables is the sum (or difference) of the expected values. What is the variance of the sum (or difference) of two independent random variables?

11. When two independent continuous random variables have _____ models, so does their sum or difference.

12. You can add expected values of any two random variables but you can only add variances of _____ variables.

Chapter 17: Probability Models

Key Vocabulary:

|Bernoulli trials |Binomial model |continuous random variable |

|Geometric model | | |

Calculator Skills:

|geometpdf( |binompdf( |normalcdf( |

|geometcdf( |binomcdf( | |

1. List three characteristics of Bernoulli trials.

2. What is the variable of interest in a geometric model?

3. How do you find the expected value and standard deviation of a geometric random variable?

4. In the geometic distribution, what does the parameter p represent?

5. If X has a geometric distribution, what does [pic] represent?

6. What is the 10% condition?

7. What is the difference between a probability distribution function (pdf) and a cumulative distribution function (cdf)?

8. What is the variable of interest in a binomial model?

9. Explain the difference between the binomial setting and the geometric setting.

10. How do you find the expected value and standard deviation of a binomial random variable?

11. In the binomial distribution, what do parameters n and p represent?

12. What is meant by [pic]?

13. In the formula [pic], what does n represent? What does k represent?

14. What does the value of [pic] represent?

15. When can we use the Normal model to estimate Binomial probabilities?

16. What is the problem with approximating a Binomial model with a Normal model?

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