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  1. Probability Quantitative Methods in HPELS 440:210

  2. Agenda • Introduction • Probability and the Normal Distribution • Probability and the Binomial Distribution • Inferential Statistics

  3. Introduction • Recall: • Inferential statistics: Sample statistic  PROBABILITY  population parameter • Marbles Example

  4. Assume: N = 100 marbles 50 black, 50 white Assume: N = 100 marbles 90 black, 10 white What is the probability of drawing a black marble? What is the probability of drawing a black marble?

  5. Introduction • Using information about a population to predict the sample is the opposite of INFERENTIAL statistics • Consider the following examples

  6. While blindfolded, you choose n=4 marbles from one of the two jars Which jar did you PROBABLY choose your sample?

  7. Introduction • What is probability? • The chance of any particular outcome occurring as a fraction/proportion of all possible outcomes • Example: • If a hat is filled with four pieces of paper lettered A, B, C and D, what is the probability of pulling the letter A? • p = # of “A” outcomes / # of total outcomes • p = 1 / 4 = 0.25 or 25%

  8. Introduction • This definition of probability assumes that the samples are obtained RANDOMLY • A random sample has two requirements: • Each outcome has equal chance of being selected • Probability is constant (selection with replacement)

  9. What is probability of drawing Jack of Diamonds from 52 card deck? Ace of spades? What is probability of drawing Jack of Spades if you do not replace the first selection?

  10. Agenda • Introduction • Probability and the Normal Distribution • Probability and the Binomial Distribution • Inferential Statistics

  11. Probability  Normal Distribution • Recall  Normal Distribution: • Symmetrical • Unified mean, median and mode • Normal distribution can be defined: • Mathematically (Figure 6.3, p 168) • Standard deviations (Figure 6.4, 168)

  12. With either definition, the predictability of the Normal Distribution allows you to answer PROBABILITY QUESTIONS

  13. Probability Questions • Example 6.2 • Assume the following about adult height: • µ = 68 inches •  = 6 inches • Probability Question: • What is the probability of selecting an adult with a height greater than 80 inches? • p (X > 80) = ?

  14. Probability Questions • Example 6.2: • Process: • Draw a sketch: • Compute Z-score: • Use normal distribution to determine probability

  15. Step 1: Draw a sketch for p(X>80) Step 2: Compute Z-score: Z = X - µ /  Z = 80 – 68/6 Z = 12/6 = 2.00 Step 3: Determine probability There is a 2.28% probability that you would select a person with a height greater than 80 inches.

  16. Probability Questions • What if Z-score is not 0.0, 1.0 or 2.0? • Normal Table  Figure 6.6, p 170

  17. Column A: Z-score Column C: Tail = smaller side Column B: Body = larger side Column D: 0.50 – p(Z)

  18. Using the Normal Table • Several applications: • Determining a probability from a specific Z-score • Determining a Z-score from a specific probability or probabilities • Determining a probability between two Z-scores • Determining a raw score from a specific probability or Z-score

  19. Determining a probability from a specific Z-score • Process: • Draw a sketch • Locate the probability from normal table • Examples: Figure 6.7, p 171

  20. p(X > 1.00) = ? Tail or Body? p = 15.87% p(X < 1.50) = ? Tail or Body? p = 93.32% p(X < -0.50) = ? p(X > 0.50) = ? Tail or Body? p = 30.85%

  21. Using the Normal Table • Several applications: • Determining a probability from a specific Z-score • Determining a Z-score from a specific probability or probabilities • Determining a probability between two Z-scores • Determining a raw score from a specific probability or Z-score

  22. Process: Draw a sketch Locate Z-score from normal table Examples: Figure 6.8a and b, p 173 Determining a Z-score from a specific probability

  23. 20% (0.200) 20% (0.200) What Z-score is associated with a raw score that has 90% of the population below and 10% above? What two Z-scores are associated with raw scores that have 60% of the population located between them and 40% located on the ends? Column B (body)  p = 0.900 Z = 1.28 Column C (tail)  p = 0.100 Z = 1.28 Column C (tail)  p = 0.200 Z = 0.84 and -0.84 Column D (0.500 – p(Z))  0.300 Z = 0.84 and – 0.84 30% (0.300) 30% (0.300)

  24. Using the Normal Table • Several applications: • Determining a probability from a specific Z-score • Determining a Z-score from a specific probability or probabilities • Determining a probability between two Z-scores • Determining a raw score from a specific probability or Z-score

  25. Determining a probability between two Z-scores • Process: • Draw a sketch • Calculate Z-scores • Locate probabilities normal table • Calculate probability that falls between Z-scores • Example: Figure 6.10, p 176 • What proportion of people drive between the speeds of 55 and 65 mph?

  26. Step 1: Sketch Step 2: Calculate Z-scores: Z = X - µ /  Z = X - µ /  Z = 55 – 58/10 Z = 65 – 58/10 Z = -0.30 Z = 0.70 Step 2: Locate probabilities Z = -0.30 (column D) = 0.1179 Z = 0.70 (column D) = 0.2580 Step 4: Calculate probabilities between Z-scores p = 0.1179 + 0.2580 = 0.3759

  27. Using the Normal Table • Several applications: • Determining a probability from a specific Z-score • Determining a Z-score from a specific probability or probabilities • Determining a probability between two Z-scores • Determining a raw score from a specific probability or Z-score

  28. Determining a raw score from a specific probability or Z-score • Process: • Draw sketch • Locate Z-score from normal table • Calculate raw score from Z-score equation • Example: Figure 6.13, p 178 • What SAT score is needed to score in the top 15%?

  29. Step 1: Sketch Step 2: Locate Z-score p = 0.150 (column D) Z = 1.04 Step 3: Calculate raw score from Z-score equation Z = X - µ /   X = µ + Z  X = 500 + 1.04(100) X = 604

  30. Agenda • Introduction • Probability and the Normal Distribution • Probability and the Binomial Distribution • Inferential Statistics

  31. Probability  Binomial Distribution • Binomial distribution? • Literally means “two names” • Variable measured with scale consisting of: • Two categories or • Two possible outcomes • Examples: • Coin flip • Gender

  32. Probability Questions  Binomial Distribution • Binomial distribution is predictable • Probability questions are possible • Statistical notation: • A and B: Denote the two categories/outcomes • p = p(A) = probability of A occurring • q = p(B) = probability of B occurring • Example 6.13, p 185

  33. If you flipped the coin twice (n=2), how many combinations are possible? Heads p = p(A) = ½ = 0.50 Heads Heads Tails q = p(B) = ½ = 0.50 Heads Tails Each outcome has an equal chance of occurring  ¼ = 0.25 Tails Heads What is the probability of obtaining at least one head in 2 coin tosses? Figure 6.19, p 186 Tails Tails

  34. Normal Approximation  Binomial Distribution • Binomial distribution tends to be NORMAL when “pn” and “qn” are large (>10) • Parameters of a normal binomial distribution: • Mean: µ = pn • SD:  = √npq • Therefore: • Z = X – pn / √npq

  35. Normal Approximation  Binomial Distribution • To maximize accuracy, use REAL LIMITS • Recall: • Upper and lower • Examples: Figure 6.21, p 188

  36. Note: The binomial distribution is a histogram, with each bar extending to its real limits Note: The binomial distribution approximates a normal distribution under certain conditions

  37. Normal Approximation  Binomial Distribution • Example: 6.22, p 189 • Assume: • Population: Psychology Department • Males (A) = ¼ of population • Females (B) = ¾ of population • What is the probability of selecting 14 males in a sample (n=48)? • p(A=14)  p(13.5<A<14.5) = ?

  38. Normal Approximation  Binomial Distribution • Process: • Draw a sketch • Confirm normality of binomial distribution • Calculate population µ and : • µ = pn •  = √npq • Calculate Z-scores for upper and lower real limits • Locate probabilities in normal table • Calculate probability between real limits

  39. Step 1: Draw a sketch Step 2: Confirm normality pn = 0.25(48) = 12 > 10 qn = 0.75(48) = 36 > 12 Step 3: Calculate µ and  µ = pn  = √npq µ = 0.25(48) = √48*0.25*0.75 µ = 12  = 3 Step 5: Locate probabilities Z = 0.50 (column C) = 0.3085 Z = 0.83 (column C) = 0.2033 Step 4: Calculate real limit Z-scores Z = X–pn/√npq Z = X-pn/√npq Z = 13.5-12/3 Z = 14.5-12/3 Z = 0.50 Z = 0.83

  40. Z = 0.50 (column C) = 0.3085 Z = 0.83 (column C) = 0.2033 Step 6: Calculate probability between the real limits p = 0.3085 – 0.2033 p = 0.1052 There is a 10.52% probability of selecting 14 males from a sample of n=48 from this population

  41. Normal Approximation  Binomial Distribution • Example extended • What is the probability of selecting more than 14 males in a sample (n=48)? • p(A>14)  p(A>14.5) = ? • Process: • Draw a sketch • Calculate Z-score for upper real limit • Locate probability in normal table

  42. Step 1: Draw a sketch Step 2: Calculate Z-score of upper real limit Z = X–pn/√npq Z = 14.5 – 12 / 3 Z = 0.83 There is a 20.33% probability of selecting more than 14 males in a sample of n=48 from this population Step 3: Locate probability Z = 0.83 (column C) = 0.2033

  43. Agenda • Introduction • Probability and the Normal Distribution • Probability and the Binomial Distribution • Inferential Statistics

  44. Looking Ahead  Inferential Statistics • PROBABILITY links the sample to the population  Figure 6.24, p 191

  45. Textbook Assignment • Problems: 1, 3, 6, 8, 12, 15, 17, 27