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

2. Chapter 3 Numerical methods for summarizing data: Location Variability Distribution measures

3. Measures of Location If the measures are computed for data from a sample, for data from a sample, they are called sample statistics. If the measures are computed for data from a population, for data from a population, they are called population parameters. Mean Median Mode Percentiles Quartiles

4. Mean The mean of a data set is the average of all the data values. The sample mean is the point estimator of the population mean .

5. Sample Mean ( ) Number of observations in the sample Number of observations in the sample Sum of the values of the n observations Sum of the values of the n observations

6. Population Mean  Number of observations in the population Number of observations in the population Sum of the values of the N observations Sum of the values of the N observations

7. Go Penguins, Again!!! MonthOpponents Rushing TDs Sep SLIPPERY ROCK4 Sep NORTHEASTERN4 Sep at Liberty1 Sep at Pittsburgh0 Oct ILLINOIS STATE1 Oct at Indiana State4 Oct WESTERN ILLINOIS2 Oct MISSOURI STATE4 Oct at Northern Iowa0 Nov at Southern Illinois0 Nov WESTERN KENTUCKY3

8. Sample Mean of TDs

9. Go Penguins…… Rushing TDsFrequencyf i.*x i 030*3=0 121*2=2 212*1=2 313*1=3 444*4=16 Total=11Total=23

10. Sample Mean for Grouped Data Where M i = the mid-point for class i f i = the frequency for class i n = the sample size

11. Median Whenever a data set has extreme values, the median Whenever a data set has extreme values, the median is the preferred measure of central location. is the preferred measure of central location. A few extremely large incomes or property values A few extremely large incomes or property values can inflate the mean. can inflate the mean. The median is the measure of location most often The median is the measure of location most often reported for annual income and property value data. reported for annual income and property value data. The median of a data set is the value in the middle The median of a data set is the value in the middle when the data items are arranged in ascending order. when the data items are arranged in ascending order.

12. Median with odd number of Obs. MonthOpponentsRushing TDs Sepat Pittsburgh0 Octat Northern Iowa0 Novat Southern Illinois0 Sepat Liberty1 OctIllinois State1 OctWestern Illinois2 NovWestern Kentucky3 SepSlippery Rock4 SepNortheastern4 Octat Indiana State4 OctMissouri State4

13. Median So the middle value is the game against Western Illinois in October. The median number of TDs is 2.

14. 121419262718 27 Median with even number of Obs. For an even number of observations: For an even number of observations: in ascending order 26182712142730 8 observations the median is the average of the middle two values. Median = (19 + 26)/2 = 22.5 19 30

15. Mode The mode of a data set is the value that occurs with The mode of a data set is the value that occurs with greatest frequency. greatest frequency. The greatest frequency can occur at two or more The greatest frequency can occur at two or more different values. different values. If the data have exactly two modes, the data are If the data have exactly two modes, the data are bimodal. bimodal. If the data have more than two modes, the data are If the data have more than two modes, the data are multimodal. multimodal.

16. Mode Modal value for our Go Penguins example is 4 TDs.

17. Percentiles A percentile provides information about how the A percentile provides information about how the data are spread over the interval from the smallest data are spread over the interval from the smallest value to the largest value. value to the largest value. Admission test scores for colleges and universities Admission test scores for colleges and universities are frequently reported in terms of percentiles. are frequently reported in terms of percentiles.

18. The pth percentile of a data set is a value such that at least p percent of the items take on this value or less and at least (100 - p) percent of the items take on this value or more. Percentiles

19. Percentiles Arrange the data in ascending order. Arrange the data in ascending order. Compute index i, the position of the p th percentile. Compute index i, the position of the p th percentile. i = (p/100)n If i is not an integer, round up to the next integer. If i is not an integer, round up to the next integer. The p th percentile is the value in the i th position. If i is an integer, the p th percentile is the average If i is an integer, the p th percentile is the average of the values in positions i and i +1. of the values in positions i and i +1.

20. Example: Rental Market in Youngstown Again Sample of 28 rental listings from craigslist:

21. 90 th Percentile i = ( p /100)* n = (90/100)*28 = 25.2 Rounding it to the next integer, which is the 26 th position 90th Percentile = 660

22. 50 th Percentile i = ( p /100) n = (50/100)28 = 14 Averaging the 14th and 15th data values: 50th Percentile = (520 + 540)/2 = 530

23. Percentile Rank The percentile rank of a data value of a variable is the percentage of all elements with values less than or equal to that data value. Of course it is related to p th percentile we found earlier. If the p th percentile of a dataset is some value (Let us say 400), then the percentile rank of 400 is p%.

24. Percentile Rank can be calculated as follows: PR of a score = (Cumulative Fre. of that score / Total Fre.)*100 Or Cumulative Relative frequency * 100 Percentile Rank (Cont’d)

25. Example 1: Go Penguins (Cont’d) Rushing TDs Cumulative Fre. Cumulative Relative Fre.PR 030.2727% 150.4545% 260.5454% 370.6363% 4111100% Total

26. Quartiles Quartiles are specific percentiles. Quartiles are specific percentiles. First Quartile = 25th Percentile First Quartile = 25th Percentile Second Quartile = 50th Percentile = Median Second Quartile = 50th Percentile = Median Third Quartile = 75th Percentile Third Quartile = 75th Percentile

27. First Quartile First quartile = 25 th percentile i = ( p /100) n = (25/100)28 = 7 Averaging 7 th and 8 th data values First quartile = (440+470)/2 = 455

28. Third Quartile Third quartile = 75th percentile i = ( p /100) n = (75/100)28 = 21 Averaging 21 st and 22 nd data values Third quartile = (595+595)/2 = 595

29. Measures of Variability It is often desirable to consider measures of variability It is often desirable to consider measures of variability (dispersion), as well as measures of location. (dispersion), as well as measures of location. For example, in choosing supplier A or supplier B we For example, in choosing supplier A or supplier B we might consider not only the average delivery time for might consider not only the average delivery time for each, but also the variability in delivery time for each. each, but also the variability in delivery time for each.

30. Measures of Variability Range Interquartile Range Variance Standard Deviation Coefficient of Variation

31. Range The range of a data set is the difference between the The range of a data set is the difference between the largest and smallest data values. largest and smallest data values. It is the simplest measure of variability. It is the simplest measure of variability. It is very sensitive to the smallest and largest data It is very sensitive to the smallest and largest data values. values.

32. Consider our penguins TDs data MonthOpponentsRushing TDs Sepat Pittsburgh0 Octat Northern Iowa0 Novat Southern Illinois0 Sepat Liberty1 OctIllinois State1 OctWestern Illinois2 NovWestern Kentucky3 SepSlippery Rock4 SepNortheastern4 Octat Indiana State4 OctMissouri State4

33. Range Range = largest value - smallest value Range = 4 - 0 = 4

34. Interquartile Range The interquartile range of a data set is the difference The interquartile range of a data set is the difference between the third quartile and the first quartile. between the third quartile and the first quartile. It is the range for the middle 50% of the data. It is the range for the middle 50% of the data. It overcomes the sensitivity to extreme data values. It overcomes the sensitivity to extreme data values.

35. Interquartile Range 3rd Quartile ( Q 3) = (75/100)*11=8.25 3 rd Quartile is 4 1st Quartile ( Q 1) = 0 Interquartile Range = Q 3 - Q 1 = 4 - 0 = 4

36. The variance is a measure of variability that utilizes The variance is a measure of variability that utilizes all the data. all the data. Variance It is based on the difference between the value of It is based on the difference between the value of each observation ( x i ) and the mean ( for a sample, each observation ( x i ) and the mean ( for a sample,  for a population).  for a population). Basically we are talking about how the data is Basically we are talking about how the data is SPREAD around the mean.

37. Variance The variance is computed as follows: The variance is computed as follows: The variance is the average of the squared The variance is the average of the squared differences between each data value and the mean. differences between each data value and the mean. for a sample population

38. When data is presented as a frequency Table Then, the variance is computed as follows:

39. Standard Deviation The standard deviation of a data set is the positive The standard deviation of a data set is the positive square root of the variance. square root of the variance. It is measured in the same units as the data, making It is measured in the same units as the data, making it more easily interpreted than the variance. it more easily interpreted than the variance.

40. The standard deviation is computed as follows: for a sample for a population Standard Deviation

41. 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

42. 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.

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

44. 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.

45. 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

46. 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

47. 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

48. 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

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

50. 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.

51. 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.