2. Basic Statistics Measures of Variability

3. The Range Deviation Score The Standard Deviation The Variance

4. STRUCTURE OF STATISTICS STATISTICS DESCRIPTIVE INFERENTIAL TABULAR GRAPHICAL NUMERICAL CONFIDENCE INTERVALS TESTS OF HYPOTHESIS Continuing with numerical approaches. NUMERICAL

5. STRUCTURE OF STATISTICS NUMERICAL DESCRIPTIVE MEASURES DESCRIPTIVE TABULAR GRAPHICAL NUMERICAL CENTRAL TENDENCY VARIABILITY SYMMETRY

6. STRUCTURE OF STATISTICS NUMERICAL DESCRIPTIVE MEASURES NUMERICAL CENTRAL TENDENCY VARIABILITY SYMMETRY RANGE VARIANCE STANDARD DEVIATION

7. You are an elementary school teacher who has been assigned a class of fifth graders whose mean IQ is 115. Because children with IQ of 115 can handle more complex, abstract material, you plan many sophisticated projects for the year. Do you think your project will succeed ? 115 100130145857055 General population 85% We need the variability of IQs in the class!

8. Having graduated from college, you are considering two offers of employment. One in sales and the other in management. The pay is about same for both. After checking out the statistics for salespersons and managers at the library, you find that those who have been working for 5 years in each type of job also have similar averages. Can you conclude that the pay for two occupations is equal? Is the average salary enough? We need the variability! management Sales Much more Much less $20,000

9. Group of scores Single score Central Tendency measures IQ of 100 students Mean IQ=118

10. Central Tendency Measures ? ? ? Measures of Central Tendency do not tell you the differences that exist among the scores More homogeneous

11. Central Tendency 1 2 3 4 Same Mean---Different Variability So What? 60 How many are out here ?

12. 1. The Range = The difference between the largest (X max ) and the smallest (X min ). 21 25, 21, 22, 23, 28, 26, 24, 29 2425262829 Range = 29 –21 = 8 2223 A large range means there is a lot of variability in data.

13. 10, 28, 26, 27, 29 10262829 Range = 29 –10 = 19 27 ? Drawbacks of The Range Range = 29 –26=3 The Range depends on only the two extreme scores

14. Because the range is determined by just two scores in the group, it ignores the spread of all scores except the largest and smallest. One aberrant score or outlier can be greatly increase the range Range and Extreme Observations R R R

15. Range and Measurement Scales 1, 2, 3, Country Code SESFAge 3-1=2 1=American 2=Asian 3=Mexican 1=Upper 2=Middle 3=Lower Before you determine the Range, all scores must be arranged in order

16. 3. The Variance 35 66 53 88 83 35 42 63 41 72 81 95 49 41 77 57 62 78 49 27 35 44 49 41 81 49 53 27 35 53 78 49 66 88 ? Differences among Scores

17. Total Variability = Sum of Individual Variability How can you determine the variability of each individual in group? 72 67 70 55 22 3 12 The amount of Individual difference entirely depends on comparison criteria.

18. Can you figure out total amount of differences among scores ? Can you figure out how much each score is different from other scores ?

19. 48 Reference score ? Mean Score You need a Common Criteria for computing Total Variability 46 47 53 50 52 45 51 49

20. 47 46 49 51 52 50 53 48 Reference score ? 49 You need a Common Criteria for computing Total Variability 45 -2 +4 -3 0 +2 -4 +3 +1 Deviation Scores A Deviation score tells you that a particular score deviate, or differs from the mean

21. DEVIATION SCORE = (X i - Mean) A score a great distance from the mean will have large deviation score. Mean ABCDEF 1 2 3

22. Total amount of variability?! Sum of all distance values! Sum of Deviation Scores No way! conceptually mathematically

23. The idea makes sense…but If you compute the sum of the deviation scores, the sum of the deviation scores equals zero! Sum of Deviation scores = (-4) + (-3) + (-2) + 0 + (1) + (2) + (3) + (4) = 0

24. The Sum of Absolute Deviation Scores Sum of absolute deviation scores ( 4 + 3 + 2 + 1 + 0 +1 + 2 + 3 + 4) = 20 The sum of absolute deviations is rarely used as a measure of variability because the process of taking absolute values does not provide meaningful information for inferential statistics.

25. Sum of Squares of deviation scores “SS” Conceptually And Mathematically

26. Sum of Squares of Deviation Scores, SS Instead of working with the absolute values of deviation scores, it is preferable to (1) square each deviation score and (2) sum them to obtain a quantity know as the Sum of Squares. SS=(-4)+(-3)+(-2)+(-1)+0+(1)+(2)+(3)+(4) =16+9+4+1+0+1+2+9+16 =60 222222222 SS= i

27. Group of scores “A” Group of scores “B” SS(A)=30 SS(B)=40 Can you say that the variability of the data in Group B is greater than the data in Group A? So !

28. 3, 4 What happens to SS when we look at some data? Group A Group B Mean = 3.5 SS = (3 - 3.5) + (4 - 3.5) + (3 - 3.5) + (4 - 3.5) =1.00 22 22 22 SS = (3 - 3.5) + (4 - 3.5) =.50

29. i=1 N i SS tends to increase as number of data(N) increase. SS is not appropriate for comparing variability among groups having unequal sample size. How can you overcome the limitation of SS Mean

30. If SS is divided by N The resulting value will be Mean of the Deviation Scores (Mean Square) VARIANCE

31. 3, 4 Group A Group B Mean = 3.5 V = (3 - 3.5) + (4 - 3.5) =.50/2 =.25 V = (3 - 3.5) + (4 - 3.5) + (3 - 3.5) + (4 - 3.5) = 1.00/4 = 2.5 2222 22

32. Variance Population Variance Sample Variance

33. POPULATION VARIANCE Sigma Square Population size Population meanIndividual value

34. SAMPLE VARIANCE Sample variance The sample variance (S 2 ) is used to estimate the population variance (  2 ) Individual value Sample Mean Sample size-1 Degree of freedom

35. Why n-1 instead n ?

36. Sampling n Sampling error = ?<100<? 100 population sample

37. The value of the squared deviations is less from X than from any other score. Hence, in a sample, the value of (X-X) n would be less than n. > n n Ideally, a sample variance would be based on  (x -  )2. This is impossible since  is not known if one has only a sample of n cases.  is substituted by.

38. > Ideal sample variance One could correct for this bias by dividing by a factor somewhat less than n n-1

39. sample n=5 If we know that the mean is equal to 5, and the first 4 scores add to 18, then the last score MUST equal 7. n-1 are free to change Degree of freedom 7 We know that ? must equal 25.

40. 4. Standard Deviation SD Positive square root of the variance PopulationSample

41. The Standard Deviation and the Mean with Normal Distribution 

42. Normal Distribution   -1  -2  -3  +2  +3  +1  Relationship between  and 

43. Normal Distribution Relationship between and S -1S -2S -3S +1S +2S +3S 68% 95% 99.9%

44. EMPIRICAL RULE For any symmetrical, bell-shaped distribution, approximately 68% of the observations will lie within  1 standard deviation of the mean; approximately 98% within  2 standard deviations of the mean; and approximately 99.9% within  3 standard deviations of the mean.

45. You can approximately reproduce your data! If a set of data has a Mean=50 and SD=10, then… 50403020607080 68% 95% 99%