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Review

Review. Sections 2.5-2.7 Qualitative Numerical Statistics Outliers Measures of variability Range Variance Standard Deviation. Review. R=Max-Min. Example – Using Standard Deviation. Here are eight test scores from a previous Stats 201 class: 35, 59, 70, 73, 75, 81, 84, 86.

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Review

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  1. Review • Sections 2.5-2.7 • Qualitative Numerical Statistics • Outliers • Measures of variability • Range • Variance • Standard Deviation

  2. Review R=Max-Min

  3. Example – Using Standard Deviation Here are eight test scores from a previous Stats 201 class: 35, 59, 70, 73, 75, 81, 84, 86. The mean and standard deviation are 70.4 and 16.7, respectively. Work out which data points are within • one standard deviation from the mean i.e. • two standard deviations from the mean i.e. • three standard deviations from the mean i.e.

  4. Example – Using Standard Deviation Here are eight test scores from a previous Stats 201 class: 35, 59, 70, 73, 75, 81, 84, 86. The mean and standard deviation are 70.4 and 16.7, respectively. Work out which data points are within • one standard deviation from the mean i.e. • two standard deviations from the mean i.e. • three standard deviations from the mean i.e.

  5. Example – Using Standard Deviation Here are eight test scores from a previous Stats 201 class: 35, 59, 70, 73, 75, 81, 84, 86. The mean and standard deviation are 70.4 and 16.7, respectively. Work out which data points are within • one standard deviation from the mean i.e. • two standard deviations from the mean i.e. • three standard deviations from the mean i.e.

  6. Example – Using Standard Deviation Here are eight test scores from a previous Stats 201 class: 35, 59, 70, 73, 75, 81, 84, 86. The mean and standard deviation are 70.4 and 16.7, respectively. Work out which data points are within • one standard deviation from the mean i.e. • two standard deviations from the mean i.e. • three standard deviations from the mean i.e. 59, 70, 73, 75, 81, 84, 86

  7. Example – Using Standard Deviation Here are eight test scores from a previous Stats 201 class: 35, 59, 70, 73, 75, 81, 84, 86. The mean and standard deviation are 70.4 and 16.7, respectively. Work out which data points are within • one standard deviation from the mean i.e. • two standard deviations from the mean i.e. • three standard deviations from the mean i.e. 59, 70, 73, 75, 81, 84, 86 59, 70, 73, 75, 81, 84, 86

  8. Example – Using Standard Deviation Here are eight test scores from a previous Stats 201 class: 35, 59, 70, 73, 75, 81, 84, 86. The mean and standard deviation are 70.4 and 16.7, respectively. Work out which data points are within • one standard deviation from the mean i.e. 59,70, 73, 75, 81, 84, 86 • two standard deviations from the mean i.e. 59, 70, 73, 75, 81, 84, 86 c) three standard deviations from the mean i.e. 35, 59, 70, 73, 75, 81, 84, 86

  9. Interpreting the Standard Deviation Chebyshev’s Theorem The proportion (or fraction) of any data set lying within K standard deviations of the mean is always at least 1-1/K2, where K is any positive number greater than 1. For K=2 we obtain, at least 3/4 (75 %) of all scores will fall within 2 standard deviations of the mean, i.e. 75% of the data will fall between

  10. Interpreting the Standard Deviation Chebyshev’s Theorem The proportion (or fraction) of any data set lying within K standard deviations of the mean is always at least 1-1/K2, where K is any positive number greater than 1. For K=3 we obtain, at least 8/9 (89 %) of all scores will fall within 3 standard deviations of the mean, i.e. 89% of the data will fall between

  11. This Data is Symmetric, Bell Shaped (or Normal Data) Relative Frequency 0.5 0.4 0.3 0.2 0.1 1 2 3 4 5 0

  12. This Data is Symmetric, Bell Shaped (or Normal Data) Relative Frequency 0.5 0.4 0.3 0.2 0.1 1 2 3 4 5 0

  13. This Data is Symmetric, Bell Shaped (or Normal Data) Relative Frequency 0.5 0.4 0.3 0.2 0.1 1 2 3 4 5 7 0 6

  14. The Empirical Rule The Empirical Rule states that for bell shaped (normal) data: 68% of all data points are within 1 standard deviations of the mean 95% of all data points are within 2 standard deviations of the mean 99.7% of all data points are within 3 standard deviations of the mean

  15. The Empirical Rule The Empirical Rule states that for bell shaped (normal) data, approximately: 68% of all data points are within 1 standard deviations of the mean 95% of all data points are within 2 standard deviations of the mean 99.7% of all data points are within 3 standard deviations of the mean

  16. Z-Score To calculate the number of standard deviations a particular point is away from the standard deviation we use the following formula.

  17. Z-Score To calculate the number of standard deviations a particular point is away from the standard deviation we use the following formula. The number we calculate is called the z-score of the measurement x.

  18. Example – Z-score Here are eight test scores from a previous Stats 201 class: 35, 59, 70, 73, 75, 81, 84, 86. The mean and standard deviation are 70.4 and 16.7, respectively. a) Find the z-score of the data point 35. b) Find the z-score of the data point 73.

  19. Example – Z-score Here are eight test scores from a previous Stats 201 class: 35, 59, 70, 73, 75, 81, 84, 86. The mean and standard deviation are 70.4 and 16.7, respectively. • Find the z-score of the data point 35. z = -2.11 b) Find the z-score of the data point 73. z = 0.16

  20. Interpreting Z-scores The further away the z-score is from zero the more exceptional the original score. Values of z less than -2 or greater than +2 can be considered exceptional or unusual (“a suspected outlier”). Values of z less than -3 or greater than +3 are often exceptional or unusual (“a highly suspected outlier”).

  21. Percentiles Another method for detecting outliers involves percentiles.

  22. Percentiles Another method for detecting outliers involves percentiles. The pth percentile ranking is a number so that p% of the measurements fall below the pth percentile and 100 – p% fall above it.

  23. How to Find Percentiles 1) Rank the n points of data from lowest to highest 2) Pick a percentile ranking you want to find. Say p. 3) Compute • If L is a whole number, then 1/2 way between the L and L+1st number. • If L is not a whole number then round up.

  24. Important Percentiles Memorize: The 25th percentile is called the lower quartile (QL) The 75th percentile is called the upper quartile (QU)

  25. Important Percentiles Memorize: The 25th percentile is called the lower quartile (QL) The 75th percentile is called the upper quartile (QU) The 50th percentile is called the

  26. Important Percentiles Memorize: The 25th percentile is called the lower quartile (QL) The 75th percentile is called the upper quartile (QU) The 50th percentile is called the median (M)

  27. Important Percentiles The interquartile range (IQR) is defined to be: IQR = QU -QL

  28. Example - Fax

  29. Example - Fax Here are the number of pages faxed by each fax sent from our Math and Stats department since April 24th, in the order that they occurred. 5, 1, 2, 6, 10, 3, 6, 2, 2, 2, 2, 2, 2, 4, 5, 1, 13, 2, 5, 5, 1, 3, 6, 37, 2, 8, 2, 25

  30. Example - Fax Here are the number of pages faxed by each fax sent from our Math and Stats department since April 24th, in the order that they occurred. 5, 1, 2, 6, 10, 3, 6, 2, 2, 2, 2, 2, 2, 4, 5, 1, 13, 2, 5, 5, 1, 3, 6, 37, 2, 8, 2, 25 Find the 40th percentile, QU , QL , M and IQR.

  31. How to Find Percentiles 1) Rank the n points of data from lowest to highest 2) Pick a percentile ranking you want to find. Say p. 3) Compute • If L is not a whole number then round up. • The percentile is 1/2 way between the L and L+1st number.

  32. Example - Fax 1) Rank the n points of data from lowest to highest 5, 1, 2, 6, 10, 3, 6, 2, 2, 2, 2, 2, 2, 4, 5, 1, 13, 2, 5, 5, 1, 3, 6, 37, 2, 8, 2, 25 Find the 40th percentile.

  33. Example - Fax 1) Rank the n points of data from lowest to highest 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 8, 10, 13, 25, 37 Find the 40th percentile.

  34. Example - Fax 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 8, 10, 13, 25, 37 2) Pick a percentile ranking you want to find. 40% Find the 40th percentile.

  35. Example - Fax 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 8, 10, 13, 25, 37 2) Pick a percentile ranking you want to find. 40% 3) Compute

  36. Example - Fax 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 8, 10, 13, 25, 37 2) Pick a percentile ranking you want to find. 40% 3) Compute

  37. Example - Fax 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 8, 10, 13, 25, 37 3) Compute Half way between the 11th and 12th number.

  38. Example - Fax 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 8, 10, 13, 25, 37 3) Compute Half way between the 11th and 12th number. Answer: 2

  39. Example - Fax 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 8, 10, 13, 25, 37 To compute QU and QL , M. Find the Median, divide the data into two equal parts and then the Medians of these. An example and more specific instructions will be done on the board.

  40. Example - Fax 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 8, 10, 13, 25, 37 M = 3 QU = 6 QL = 2 IQR=6-2=4.

  41. Percentiles Sometimes the IQR, is a better measure of variance then the standard deviation since it only depends on the center 50% of the data. That is, it is not effected at all by outliers.

  42. Percentiles Sometimes the IQR, is a better measure of variance then the standard deviation since it only depends on the center 50% of the data. That is, it is not effected at all by outliers. To use the IQR as a measure of variance we need to find the Five Number Summary of the data and then construct a Box Plot.

  43. Five Number Summary and Outliers The Five Number Summary of a data set consists of five numbers, • MIN, QL , M, QU, Max

  44. Five Number Summary and Outliers The Five Number Summary of a data set consists of five numbers, • MIN, QL , M, QU, Max Suspected Outliers lie • Above 1.5 IQRs but below 3 IQRs from the Upper Quartile • Below 1.5 IQRs but above 3 IQRs from the Lower Quartile Highly Suspected Outliers lie • Above 3 IQRs from the Upper Quartile • Below 3 IQRs from the Lower Quartile.

  45. Five Number Summary and Outliers The Inner Fences are: • data between the Upper Quartile and 1.5 IQRs above the Upper Quartile and • data between the Lower Quartile and 1.5 IQRs below the Lower Quartile The Outer Fences are: • data between 1.5 IQRs above the Upper Quartile and 3 IQRs above the Upper Quartile and • data between 1.5 IQRs Lower Quartile and 3 IQRs below the Lower Quartile

  46. Example - Fax 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 8, 10, 13, 25, 37 Min=1, QL = 2, M = 3, QU = 6, Max = 37. IQR=6-2=4.

  47. Example - Fax 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 8, 10, 13, 25, 37 Min=1, QL = 2, M = 3, QU = 6, Max = 37. IQR=6-2=4. Inner Fence extremes: -4, 12

  48. Example - Fax 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 8, 10, 13, 25, 37 Min=1, QL = 2, M = 3, QU = 6, Max = 37. IQR=6-2=4. Inner Fence extremes: -4, 12 Outer Fence extremes: -10, 18

  49. Example - Fax 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 8, 10, 13, 25, 37 Min=1, QL = 2, M = 3, QU = 6, Max = 37. IQR=6-2=4. Inner Fence extremes: -4, 12 Outer Fence extremes: -8, 18 Suspected Outliers: 13 Highly Suspected Outliers: 25, 37

  50. Definition: Boxplot A boxplotis a graph of lines (from lowest point inside the lower inner fence to highest point in the upper inner fence) and boxes (from Lower Quartile to Upper quartile) indicating the position of the median. * Lowest data Point more than the lower inner fence Highest data Point less than the upper inner fence Median Upper Quartile Lower Quartile Outliers

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