1. Econ 3790: Business and Economics Statistics Instructor: Yogesh Uppal Email: yuppal@ysu.edu

2. Lecture Slides 3 Measures of Variability Measures of Distribution Shape, Relative Location, and Detecting Outliers Introduction to probabilities

3. The coefficient of variation is computed as follows: Coefficient of Variation The coefficient of variation indicates how large the standard deviation is in relation to the mean. ← for a sample ← for a population

4. Coefficient of Variation (CV) CV is used in comparing variability of distributions with different means. A value of CV > 100% implies a data with high variance. A value of CV < 100% implies a data with low variance.

5. Measures of Distribution Shape, Relative Location, and Detecting Outliers Distribution Shape z-Scores Detecting Outliers

6. Distribution Shape: Skewness An important measure of the shape of a distribution is called skewness. The formula for computing skewness for a data set is somewhat complex.

7. Distribution Shape: Skewness n Symmetric (not skewed) Skewness is zero. Skewness is zero. Mean and median are equal. Mean and median are equal. Relative Frequency.05.10.15.20.25.30.35 0 0 Skewness = 0 Skewness = 0

8. Distribution Shape: Skewness Moderately Skewed Left Skewness is negative. Mean will usually be less than the median. Relative Frequency.05.10.15.20.25.30.35 0 0 Skewness = .31 Skewness = .31

9. Distribution Shape: Skewness Moderately Skewed Right Skewness is positive. Mean will usually be more than the median. Relative Frequency.05.10.15.20.25.30.35 0 0 Skewness =.31 Skewness =.31

10. Distribution Shape: Skewness n Highly Skewed Right Skewness is positive. Skewness is positive. Mean will usually be more than the median. Mean will usually be more than the median. Relative Frequency.05.10.15.20.25.30.35 0 0 Skewness = 1.25 Skewness = 1.25

11. Z-scores Z-score is often called standardized scores. It denotes the number of standard deviations a data value is from the mean.

12. z-Scores A data value less than the sample mean will have a A data value less than the sample mean will have a z-score less than zero. z-score less than zero. A data value greater than the sample mean will have A data value greater than the sample mean will have a z-score greater than zero. a z-score greater than zero. A data value equal to the sample mean will have a A data value equal to the sample mean will have a z-score of zero. z-score of zero. An observation’s z-score is a measure of the relative An observation’s z-score is a measure of the relative location of the observation in a data set. location of the observation in a data set.

13. Detecting Outliers An outlier is an unusually small or unusually large An outlier is an unusually small or unusually large value in a data set. value in a data set. A data value with a z-score less than -3 or greater A data value with a z-score less than -3 or greater than +3 might be considered an outlier. than +3 might be considered an outlier.

14. Introduction to Probability Some basic definitions and relationships of probability

15. Some Definitions Experiment: A process that generates well-defined outcomes. For example, Tossing a coin, Rolling a die or Playing Blackjack Sample Space: is the set for all experimental Outcomes. For example, sample space for an experiment of tossing a coin is: S={Head, Tail} Or rolling a die is: S={1, 2, 3, 4, 5, 6}

16. Definitions (Cont’d) Event: a collection of outcomes or sample points. For example, if our experiment is rolling a die, we can call an incidence of getting a number greater than 3 an event ‘A’.

17. Basic Rules of Probability 1. Probability of any outcome can never be negative or greater than 1. 2. The sum of the probabilities of all the possible outcomes of an experiment is 1.

18. Probability as a Numerical Measure of the Likelihood of Occurrence 0 1.5 Increasing Likelihood of Occurrence Probability: The event is very unlikely to occur. The occurrence of the event is just as likely as just as likely as it is unlikely. The event is almost certain to occur.

19. Example: Bradley Investments Bradley has invested in a stock named Markley Oil. Bradley has determined that the possible outcomes of his investment three months from now are as follows. Investment Gain or Loss Investment Gain or Loss (in $000) (in $000) 10 5 0  20

20. Example: Bradley Investments Experiment: Investing in stocks Experiment: Investing in stocks Sample Space: S = {10, 5, 0, -20} Sample Space: S = {10, 5, 0, -20} Event: Making a positive profit (Lets call it ‘A’) Event: Making a positive profit (Lets call it ‘A’) A = {10, 5} What is the event for not making a loss?

21. Assigning Probabilities Classical Method Relative Frequency Method Subjective Method Assigning probabilities based on the assumption Assigning probabilities based on the assumption of equally likely outcomes of equally likely outcomes Assigning probabilities based on experimentation Assigning probabilities based on experimentation or historical data or historical data Assigning probabilities based on judgment Assigning probabilities based on judgment

22. Classical Method Assigning probabilities based on the assumption of equally likely outcomes If an experiment has n possible outcomes, this method would assign a probability of 1/n to each outcome.

23. Example Experiment: Rolling a die Experiment: Rolling a die Sample Space: S = {1, 2, 3, 4, 5, 6} Sample Space: S = {1, 2, 3, 4, 5, 6} Probabilities: Each sample point has a 1/6 chance of occurring Probabilities: Each sample point has a 1/6 chance of occurring

24. Example Experiment: Tossing a Coin Experiment: Tossing a Coin Sample Space: S = {H, T} Sample Space: S = {H, T} Probabilities: Each sample point has Probabilities: Each sample point has 1/2 a chance of occurring

25. Relative Frequency Method Assigning probabilities based on experimentation or historical data Example: Lucas Tool Rental Lucas Tool Rental would like to assign probabilities to the number of car polishers it rents each day. Office records show the following frequencies of daily rentals for the last 40 days.

26. Relative Frequency Method Number of Polishers Rented Number of Days 0 1 2 3 4 4 6 18 10 2 Example: Lucas Tool Rental

27. Each probability assignment is given by dividing the frequency (number of days) by the total frequency (total number of days). Relative Frequency Method 4/404/40 Probability Number of Polishers Rented Number of Days 0 1 2 3 4 4 6 18 10 2 40.10.15.45.25.05 1.00

28. Example: Favorite Party PartyValueVotesRelative Fre. Rep150.24 Dem2140.67 Greens300.0 None420.09 211.00

29. Subjective Method When economic conditions and a company’s When economic conditions and a company’s circumstances change rapidly it might be circumstances change rapidly it might be inappropriate to assign probabilities based solely on inappropriate to assign probabilities based solely on historical data. historical data. We can use any data available as well as our We can use any data available as well as our experience and intuition, but ultimately a probability experience and intuition, but ultimately a probability value should express our degree of belief that the value should express our degree of belief that the experimental outcome will occur. experimental outcome will occur. The best probability estimates often are obtained by The best probability estimates often are obtained by combining the estimates from the classical or relative combining the estimates from the classical or relative frequency approach with the subjective estimate. frequency approach with the subjective estimate.

30. Some Basic Relationships of Probability Complement of an Event Complement of an Event Intersection of Two Events Intersection of Two Events Mutually Exclusive Events Mutually Exclusive Events Union of Two Events

31. Complement of an Event Complement of an event A is the event consisting of all outcomes or sample points that are not in A and is denoted by A c. Event A AcAcAcAc Sample Space S Sample Space S VennDiagram

32. Example: Rolling a die Event A: Getting a number greater than or equal to 3 A = {3, 4, 5, 6} A c = {1, 2} Event B: Getting a number greater than 1, but less than 5 B = {???} B c = {???}

33. Intersection of two events The intersection two events A and B is an event consisting of all sample points that are both in A and B, and is denoted by A ∩ B. Event A Event B Intersection of A and B

34. Union of two events The Union two events A and B is an event consisting of all sample points that are in A or B or both A and B, and is denoted by A U B. Event A Event B Union of A and B

35. Example: Rolling a Die (Cont’d) A ∩ B = {3, 4} A U B = {2, 3, 4, 5, 6} Lets find the following probabilities: P(A) = Outcomes of A / Total Number of Outcomes = 4/6 = 2/3 P(B) = ? P(A ∩ B) = ? P(A U B) = ?

36. Addition Law According to the Addition law, the probability of the event A or B or both can also be written as P ( A  B ) = P ( A ) + P ( B )  P ( A  B  In our rolling the die example, P(A U B) = 2/3 + 1/2 – 1/3 = 5/6

37. Mutually Exclusive Events Two events are said to be Mutually Exclusive if, when one event occurs, the other can not occur. Or if they do not have any common sample points. Event A Event B

38. When Events A and B are mutually exclusive, P(A ∩ B) = 0. The Addition Law for mutually exclusive events is Mutually Exclusive Events P ( A  B ) = P ( A ) + P ( B ) there’s no need to include “  P ( A  B  ”

39. Example: Mutually Exclusive Events Suppose C is an event of getting a number less than 3 on one roll of a die. C = {1, 2} A = {3, 4, 5,6} P(A ∩ C) = 0 Events A and C are mutually exclusive.

40. Conditional Probability The probability of an event (Lets say A) given that another event (Lets say B) has occurred is called Conditional Probability of A. It is denoted by P(A | B). It can be computed using the following formula:

41. Rolling the Die Example P(A ∩ B) = 1/3 P(A) =2/3 P(B) =1/2 P(A | B) = P(A ∩ B) / P(B) = (1/3)/(1/2) = 2/3 P(B | A) = P(A ∩ B) / P(A) = (1/3)/(2/3) = 1/2

42. Multiplication Law The multiplication law provides the way to calculate the probability of intersection of two events and is written as follows: P ( A  B ) = P ( B )× P ( A | B )

43. Independent Events If the probability of an event A is not changed or affected by the existence of another event B, then A and B are independent events. A and B are independent iff OR P ( A | B ) = P ( A ) P ( B | A ) = P ( B )

44. Multiplication Law for Independent Events In case of independent events, the Multiplication Law is written as P ( A  B ) = P ( A ) P ( B )

45. Rolling the Die Example So there are two ways of checking whether two events are independent or not: 1. Conditional Probability Method: P(A | B) = 2/3 = P(A) P(B | A) = 1/2 = P(B) A and B are independent.

46. Rolling the Die Example 2. The second way is using the Multiplication Law for independent events. P(A ∩ B) = 1/3 P(A) =2/3 P(B) =1/2 P(A).P(B)=1/3 Since P(A ∩ B) = P(A). P(B), A and B are independent events.

47. Education and Income Data Highest Grade Completed Annual Income <$25k$25k-50k>$50kTotal Not HS Grad196384949104825635 HS Grad34785259241072171430 Bachelor’s10081136801745841219 Total645044455329227138284

48. There are two experiments here: 1. Highest Grade Completed. S 1 = {not HS grad, HS grad, Bachelor’s} 2. Annual Income. S 2 ={ 50K} What does each cell represent in the above crosstab? Education and Income Data

49. Highest Grade Completed Annual Income <$25k$25k-50k>$50kTotal Not HS Grad19638/ 138284 =0.14 4949/ 138284 =0.04 1048/ 138284 =0.01 25635/ 138284 =0.19 HS Grad34785/ 138284 =0.25 25924/ 138284 =0.19 10721/ 138284 =0.08 71430/ 138284 =0.52 Bachelor’s10081/ 138284 =0.07 13680/ 138284 =0.10 17458/ 138284 =0.13 41219/ 138284 =0.30 Total64504/ 138284 =0.47 44553/ 138284 =0.32 29227/ 138284 =0.21 138284/ 138284 =1.00

50. P(Bachelor’s) = P(Bachelor’s and <25K) + P(Bachelor’s and 25-50K) + P(Bachelor’s and >50K) = 0.07+0.10+0.13 =0.30 P(>$50K) = P(Not HS and >50K) + P(HS grad and >50K) + P(Bachelor’s and >50K) = 0.01+0.08+0.13 = 0.21 Education and Income Data

51. Lets define an event A as the event of making >$50K. A={>$50K} P(A) = 0.21 Lets define another even B as the event of having a HS degree. B= {HS Grad} P(B) = 0.52

52. Rules of Probability A and B is an event of having an income >$50K and being a HS graduate: P(A and B) = 0.08 A or B is an event of having an income >$50K or being a HS graduate or both: P(A or B) = P(A) + P(B) – P(A and B) = 0.21 + 0.52 – 0.08 = 0.65

53. Event of making >$50K given the event of being a HS graduate: P(A | B)= P(A and B) / P(B) =0.08/ 0.52 = 0.15 Are A and B independent? 1. P(A | B)= 0.15 ≠ P(A) = 0.21 2. P(A and B)= 0.08 ≠ P(A)*P(B)=0.21*0.52=0.11 → A and B are not independent. Are Annual Income and Highest Grade Completed independent? Education and Income Data

54. The probability of any event is the sum of probabilities of its sample points. E.g. Lets define an event C as the event of having at least a HS degree. C= {HS Grad, Bachelor’s} P(C) = P(HS Grad) + P(Bachelor’s) =0.52 + 0.30 =0.82