1. Statistics Chapter 6 / 7 Review

2. Random Variables and Their Probability Distributions Discrete random variables – can take on only a countable or finite number of values. Continuous random variables – can take on countless values in an interval on the real line Probability distributions of random variables – An assignment of probabilities to the specific values or a range of values for a random variable.

3. Discrete Probability Distributions 1)Each value of the random variable has an assigned probability. 2)The sum of all the assigned probabilities must equal 1.

4. Means and Standard Deviations for Discrete Probability Distributions

5. Binomial Experiments 1)There are a fixed number of trials. This is denoted by n. 2)The n trials are independent and repeated under identical conditions. 3)Each trial has two outcomes: S = successF = failure

6. Binomial Experiments 4)For each trial, the probability of success, p, remains the same. Thus, the probability of failure is 1 – p = q. 5)The central problem is to determine the probability of r successes out of n trials.

7. Binomial Probability Formula

8. Find the probability of observing 6 successes in 10 trials if the probability of success is p = 0.4. a). 0.111b). 0.251c). 0.0002d). 0.022

9. Binomial Probability Formula Find the probability of observing 6 successes in 10 trials if the probability of success is p = 0.4. a). 0.111b). 0.251c). 0.0002d). 0.022

10. Binomial Probabilities At times, we will need to calculate other probabilities: P(r < k) P(r ≤ k) P(r > k) P(r ≥ k) where k is a specified value less than or equal to the number of trials, n.

11. Mean and Standard Deviation of a Binomial Distribution

12. Critical Thinking Unusual values – For a binomial distribution, it is unusual for the number of successes r to be more than 2.5 standard deviations from the mean. – This can be used as an indicator to determine whether a specified number of r out of n trials in a binomial experiment is unusual.

13. Chapter 7

14. Features of the Normal Curve Smooth line and symmetric around µ. Highest point directly above µ. The curve never touches the horizontal axis in either direction. As σ increases, the curve spreads out. As σ decreases, the curve becomes more peaked around µ. Inflection points at µ ± σ.

15. Normal Probability The area under any normal curve will always be 1. The portion of the area under the curve within a given interval represents the probability that a measurement will lie in that interval.

16. The Empirical Rule

17. Raw Scores and z Scores

18. Distribution of z-Scores If the original x values are normally distributed, so are the z scores of these x values. –µ = 0 –σ = 1

19. Area to the Left of a Given z Value

20. Area to the Right of a Given z Value

21. Area Between Two z Values