1. Not a Venn diagram?

2. Warm up When rolling a die with sides numbered from 1 to 20, if event A is the event that a number divisible by 5 is rolled: i) What is the sample space, S? What is n(S)? ii) What is the event space, A? What is n(A)? iii) What is P(A)? i) S = {1, 2, 3, …, 20}, n(S) = 20 ii) A = {5, 10, 15, 20}, n(A) = 4 iii)P(A) = 4/20 = 1/5 or 0.20

3. 4.3 Finding Probability Using Sets Questions? pp. 209-212 #1, 5, 8-10, 12-13 Learning goals: Construct Venn diagrams and use them to compute probabilities MSIP/Home Learning: p. 228 #1, 2, 4, 7, 8, 10–14, 17

4. John Venn 1834 -1923 “Of spare build, he was throughout his life a fine walker and mountain climber, a keen botanist, and an excellent talker and linguist” -- John Archibald Venn (John Venn’s son), writing about his father

5. A Simple Venn Diagram Venn Diagram: a diagram in which sets are represented by geometrical shapes. A’ A S http://bclearningnetwork.com/LOR/games/mutual.swf

6. Set Notation In mathematics, curly brackets are used to denote a set of items e.g., Define the following sets of numbers  A = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}  B = {2, 4, 6, 8, 10}  C = {1, 2, 3, 4, 5}  D = {10} The items in a set are commonly called elements.

7. Intersection of Sets Given two sets, A and B, the set of common elements is called the intersection of A and B, is written as A ∩ B (“A intersect B”). S A ∩ B A B

8. Intersection of Sets (continued) Elements that belong to the set A ∩ B are members of set A and members of set B. So… A ∩ B = {elements in both A AND B} S A ∩ B

9. Example 1 - Intersection Let A = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}C = {1, 2, 3, 4, 5} B = {2, 4, 6, 8, 10} D = {10} a) What is A ∩ B? {2, 4, 6, 8, 10} or B b) B ∩ C? {2, 4} c) C ∩ D? { } or Ø (the empty set, sounds like the vowel sound in bird or hurt) http://encyclopedia.thefreedictionary.com/%D8 d) A ∩B ∩D? {10} or D

10. Union of Sets The set formed by combining the elements of A with those in B is called the union of A and B, and is written A U B. S A U B

11. Union of Sets (continued) Elements that belong to the set A U B are members of set A or members of set B (or both). So… A U B = {elements in A OR B (or both)} S A U B

12. Example 2 - Union A = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}B = {2, 4, 6, 8, 10} C = {1, 2, 3, 4, 5}D = {10} a) What is A U B? {1, 2, 3, 4, 5, 6, 7, 8, 9, 10} or A b) B U C? {1, 2, 3, 4, 5, 6, 8, 10} c) C U D? {1, 2, 3, 4, 5, 10} d) B U C U D? {1, 2, 3, 4, 5, 6, 8, 10}

13. Disjoint Sets A and B are disjoint sets if they have no elements in common  n(A ∩ B) = 0 The intersection of A and B is empty  A ∩ B = Ø What would the Venn diagram look like?

14. Disjoint Sets (continued) A Venn diagram for two disjoint sets might look like this: S BA

15. The Additive Principle Remember:  n(A) is the number of elements in set A  P(A) is the probability of event A The Additive Principle for the Union of Two Sets:  n(A U B) = n(A) + n(B) – n(A ∩ B)  P(A U B) = P(A) + P(B) – P(A ∩ B) Alternatively:  n(A ∩ B) = n(A) + n(B) – n(A U B)  P(A ∩ B) = P(A) + P(B) – P(A U B)

16. Mutually Exclusive Events Mutually exclusive events have no outcomes in common  A and B are mutually exclusive events if and only if (A ∩ B) = Ø  e.g., flipping a head or tail  e.g., drawing a red card or a black card So for mutually exclusive events A and B, n(A U B) = n(A) + n(B)

17. Example 3 What is the number of cards that are either red cards or face cards? Let R be the set of red cards, F the set of face cards If we have “or” we are looking at union n(R U F) = n(R) + n(F) – n(R ∩ F) = n(red) + n(face) – n(red face) = 26 + 12 – 6 = 32 What is the probability of picking a red card or a face card from a standard deck? P(R U F) = 32/52 = 8/13 or 0.62

18. Example 4 A survey of 100 students How many students study English only? French only? Math only? Course TakenNo. of students English 80 Mathematics 33 French 68 English and Mathematics 30 French and Mathematics 6 English and French 50 All three courses 5 We need to draw a Venn diagram

19. Example 4: what do we know? n(E ∩ M ∩ F) = 5 M F E 5

20. Example 4: what else do we know? n(E ∩ M ∩ F) = 5 M F E 5 n(M ∩ E) = 30 Therefore, the number of students in E and M, but not in F is 25. 25

21. Example 4 (continued) n(F ∩ E) = 50 Therefore, the number of students who take English and French, but not in Math is 45. M F E 5 25 45 n(E) = 80 5

22. Example 4 – completed Venn Diagram M F E 5 25 45 5 1 17 2

23. MSIP / Home Learning Read through Examples 2-3 on pp. 223-227 Complete p. 228 #1, 2, 4, 7, 8, 10–14, 17

25. Warm up What is the number of cards that are either even numbers (2, 4, 6, 8, 10) or clubs? What is the probability of picking such a card from a standard deck? Use n(E U C) = n(E) + n(C) – n(E ∩ C) = n(even) + n(clubs) – n(even clubs) = 20 + 13 – 5 = 28 Probability? P(E U C) = 28/52 = 7/13

26. 4.4 Conditional Probability Learning goal: Calculate probabilities when one event is affected by the occurrence of another Questions? p. 228 #1, 2, 4, 7, 8, 10–14, 17 MSIP/Home Learning: pp. 235 – 238 #1, 2, 4, 6, 7, 9, 10, 19

27. Conditional Probabilities In some situations, knowing that one event has occurred affects the probability that another event will occur. Examples:  Weather  Sequenced traffic lights  Star athletes’ performance  Dealing cards (no replacement)

28. Conditional Probability Formula The probability that event B will occur given that event A has occurred is: P(B | A) =P(A ∩ B) P(A)

29. Example 1a Light 1 and Light 2 are both green 60% of the time. Light 1 is green 80% of the time. What is the probability that Light 2 is green given that Light 1 is green?

30. Example 1b There is a 20% chance that it will snow both Saturday and Sunday. There is an 80% chance of snow on Saturday. What is the probability that it will snow Sunday given that it snowed Saturday?

31. Multiplication Law for Conditional Probability The probability of events A and B both occurring, when B is conditional on A is: P(A ∩ B) = P(B|A) x P(A)

32. Example 2 a) What is the probability of drawing 2 face cards in a row from a deck of 52 playing cards if the first card is not replaced? P(A ∩ B) = P(B | A) x P(A) P(1 st FC ∩ 2 nd FC) = P(2 nd FC | 1 st FC) x P(1 st FC) = 11 x 12 51 52 = 132 2652 = 11 or 0.05 221

33. Example 3 100 Students surveyed Course TakenNo. of students English 80 Mathematics 33 French 68 English and Mathematics 30 French and Mathematics 6 English and French 50 All three courses 5 Refer to yesterday’s Venn diagram. What is the probability that a student takes Mathematics given that he or she takes English?

34. Example 3 – Venn Diagram M F E 5 25 45 5 1 2 17

35. Another Example (continued) To answer the question, we need to find P(Math | English). We know...  P(Math | English) = P(Math ∩ English) P(English) Therefore…  P(Math | English) = 0.3 = 3 or 0.375 0.8 8

36. MSIP / Home Learning Read Examples 1-3, pp. 231 – 234 pp. 235 – 238 #1, 2, 4, 6, 7, 9, 10, 19