1. Descriptive Statistics I REVIEW Measurement scales Nominal, Ordinal, Continuous (interval, ratio) Summation Notation: 3, 4, 5, 5, 8Determine: ∑ X, (∑ X) 2, ∑X 2 9+16+25+25+64 25 625 139 Percentiles: so what?

2. Measures of central tendency Mean, median mode 3, 4, 5, 5, 8 Distribution shapes

3. Variability Range Hi – Low scores only (least reliable measure; 2 scores only) Variance (S 2 ) inferential stats Spread of scores based on the squared deviation of each score from mean Most stable measure Standard Deviation (S) descriptive stats The square root of the variance Most commonly used measure of variability True Varia nce Total variance Error

4. Variance (Table 3.2) The didactic formula The calculating formula 4+1+0+1+4=1010 = 2.5 5-1=4 4 55 - 225 = 55-45=10 = 2.5 5 44 4

5. Standard Deviation The square root of the variance Nearly 100% scores in a normal distribution are captured by the mean + 3 standard deviations M + S 100 + 10

6. The Normal Distribution M + 1s = 68.26% of observations M + 2s = 95.44% of observations M + 3s = 99.74% of observations

7. Calculating Standard Deviation Raw scores 3 7 4 5 1 ∑ 20 Mean: 4 (X-M) 3 0 1 -3 0 S= √20 5 S= √4 S=2 (X-M) 2 1 9 0 1 9 20

8. Coefficient of Variation (V) Relative variability around the mean OR Determines homogeneity of scoresS M Helps more fully describe different data sets that have a common std deviation (S) but unique means (M) Lower V=mean accounts for most variability in scores.1 -.2=homogeneous>.5=heterogeneous

9. Descriptive Statistics II What is the “muddiest” thing you learned today?

10. Descriptive Statistics II REVIEW Variability Range Variance: Spread of scores based on the squared deviation of each score from meanMost stable measure Standard deviation Most commonly used measure Coefficient of variation Relative variability around the mean (homogeneity of scores) Helps more fully describe different data sets that have a common std deviation (S) but unique means (M) 50+10 What does this tell you?

11. Standard Scores Set of observations standardized around a given M and standard deviation Score transformed based on its magnitude relative to other scores in the group Converting scores to Z scores expresses a score’s distance from its own mean in sd units Use of standard scores: determine composite scores from different measures (bball: shoot, dribble); weight?

12. Standard Scores Z-score M=0, s=1 T-score T = 50 + 10 * (Z) M=50, s=10

13. Variability Range Hi – Low scores only (least reliable measure; 2 scores only) Variance (S 2 ) inferential stats Spread of scores based on the squared deviation of each score from mean Most stable measure Standard Deviation (S) descriptive stats The square root of the variance Most commonly used measure of variability True Varia nce Total variance Error

14. Variance (Table 3.2) The didactic formula The calculating formula 4+1+0+1+4=1010 = 2.5 5-1=4 4 55 - 225 = 55-45=10 = 2.5 5 44 4

15. Standard Deviation The square root of the variance Nearly 100% scores in a normal distribution are captured by the mean + 3 standard deviations M + S 100 + 10

16. The Normal Distribution M + 1s = 68.26% of observations M + 2s = 95.44% of observations M + 3s = 99.74% of observations

17. Calculating Standard Deviation Raw scores 3 7 4 5 1 ∑ 20 Mean: 4 (X-M) 3 0 1 -3 0 S= √20 5 S= √4 S=2 (X-M) 2 1 9 0 1 9 20

18. Coefficient of Variation (V) Relative variability around the mean OR Determines homogeneity of scoresS M Helps more fully describe different data sets that have a common std deviation (S) but unique means (M) Lower V=mean accounts for most variability in scores.1 -.2=homogeneous>.5=heterogeneous

19. Descriptive Statistics II What is the “muddiest” thing you learned today?

20. Descriptive Statistics II REVIEW Variability Range Variance: Spread of scores based on the squared deviation of each score from meanMost stable measure Standard deviation Most commonly used measure Coefficient of variation Relative variability around the mean (homogeneity of scores) Helps more fully describe different data sets that have a common std deviation (S) but unique means (M) 50+10 What does this tell you?

21. Standard Scores Set of observations standardized around a given M and standard deviation Score transformed based on its magnitude relative to other scores in the group Converting scores to Z scores expresses a score’s distance from its own mean in sd units Use of standard scores: determine composite scores from different measures (bball: shoot, dribble); weight?

22. Standard Scores Z-score M=0, s=1 T-score T = 50 + 10 * (Z) M=50, s=10

23. Conversion to Standard Scores Raw scores 3 7 4 5 1 Mean: 4 St. Dev: 2 X-M 3 0 1 -3 Z -.5 1.5 0.5 -1.5 Allows the comparison of scores using different scales to compare “apples to apples” SO WHAT? You have a Z score but what do you do with it? What does it tell you?

24. Normal distribution of scores Figure 3.7 99.9

25. Descriptive Statistics II Accelerated REVIEW Standard Scores Converting scores to Z scores expresses a score’s distance from its own mean in sd units Value? Coefficient of variation Relative variability around the mean (homogeneity of scores) Helps more fully describe different data sets that have a common std deviation (S) but unique means (M) 100+20 What does this tell you? Between what values do 95% of the scores in this data set fall?

26. Normal-curve Areas Table 3-3 Z scores are on the left and across the top Z=1.64: 1.6 on left,.04 on top=44.95 Values in the body of the table are percentage between the mean and a given standard deviation distance ½ scores below mean, so + 50 if Z is +/- The "reference point" is the mean +Z=better than the mean -Z=worse than the mean

28. Area of normal curve between 1 and 1.5 std dev above the mean Figure 3.9

29. Normal curve practice Z score Z = (X-M)/S T score T = 50 + 10 * (Z) Percentile P = 50 + Z percentile (+: add to 50, -: subtract from 50) Raw scores Hints Draw a picture What is the z score? Can the z table help?

30. Assume M=700, S=100 PercentileT scorez scoreRaw score 6453.7.37737 43 –1.23 618 17 68 835.57

31. Descriptive Statistics III Explain one thing that you learned today to a classmate What is the “muddiest” thing you learned today?