1. Describing Data Using Numerical Measures

2. Topics

3. Summary Measures

4. Measures of Central Tendency

6. Mean (Arithmetic Average)

8. Median

10. Median Example

11. Mode

12. Weighted Mean

13. Geometric Mean The geometric mean indicates the central tendency or typical value of a set of numbers by using the product of their values (as opposed to the arithmetic mean which uses their sum). The geometric mean is defined as the nth root (where n is the count of numbers) of the product of the numbers.arithmetic meannth rootproduct For instance, the geometric mean of two numbers, say 2 and 8, is just the square root of their product; that is 2√2 × 8 = 4.square root

14. The geometric mean only applies to positive numbers in order to avoid taking the root of a negative product In statistical surveys when proportional differences are more important than the absolute differences, geometric mean referred instead of arithmetic mean. Geometric Mean

15. Harmonic Mean The harmonic mean H is defined to be the reciprocal of the arithmetic mean of the reciprocals of :reciprocalarithmetic mean When prices are expressed in quantities (so many units per dollars) harmonic mean should be calculated.

16. Shape of a Distribution

17. Which Measure of Central Tendency is the “best”?

18. Measures of Location (Measures of Statistical Dispersion)

19. Percentiles

20. Quartiles

22. Box and Whisker Plot

23. Constructing the Box and Whisker Plot

24. Shape of Box and Whisker Plots

25. Distribution Shape and Box and Whisker Plot

26. Measures of Statistical Dispersion (Variation)

27. Statistical Dispersion (variation) Measures of statistical dispersion or variation give information on the spread or variability of the data values.

28. Range

29. Disadvantages of the Range

30. Interquartile Range

31. Interquartile Range Example

32. Variance

33. Degrees of Freedom (df)

34. Standart Deviation

35. Calculation Example: Sample Standart Deviation

36. Comparing Standart Deviations

37. Coefficient of Variation

38. Comparing Coefficients of Variation

39. Standardized Data Values

40. Standardized Population Values

41. Standardized Sample Values

42. Standardized Value Example

43. Using Probability and Probability Distributions

44. Important Terms

45. Sample Space

46. Events

47. Visualizing Events

48. Experimental Outcomes

49. Probability Concepts

51. Independent vs. Dependent Events

52. Assigning Probability

53. Rules of Probability

54. Addition Rule for Elementary Events

55. Complement Rule

56. Addition Rule for Two Events

57. Addition Rule Example

58. Addition Rule for Mutually Exclusive Events

59. Conditional Probability

60. Conditional Probability Example

63. For Independent Events

64. Multiplication Rules

65. Tree Diagram Example

66. Bayes’ Theorem

67. Bayes’ Theorem Example