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Chapter 2

Chapter 2. Central Tendency & Variability Tues. Aug. 27, 2013. Measures of Central Tendency The Mean. Sum of all the scores divided by the number of scores Mean of 7,8,8,7,3,1,6,9,3,8 Σ X = N = 10 Mean (or M) =. The Mode. Most common single number in a distribution

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Chapter 2

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  1. Chapter 2 Central Tendency & Variability Tues. Aug. 27, 2013

  2. Measures of Central Tendency The Mean • Sum of all the scores divided by the number of scores • Mean of 7,8,8,7,3,1,6,9,3,8 • ΣX = • N = 10 • Mean (or M) =

  3. The Mode • Most common single number in a distribution • Mode of 7,8,8,7,3,1,6,9,3,8? • Measure of central tendency for nominal variables (most common category was…)

  4. The Median • The middle score when all scores are arranged from lowest to highest • Median of 7,8,8,7,3,1,6,9,3,8 • 1 3 3 6 7 7 8 8 8 9 median • Median is the average (mean) of the 5th and 6thscores here • ? How do we decide between using mean or median?

  5. The Median • With large data sets, shortcut… • If odd N, divide by 2, then add ½, gives position of the median score in list. • Ex? • If even N, divide by 2, this and score above it need to be averaged for median. • ex?

  6. Example of Mean/Median Preference • Evolutionary psych example in book (p. 40-41) – competing theories of gender diffs in how many mates we prefer • Buss (evol.) – men should prefer more partners than women (to spread genes) • Miller – men/women should prefer same #

  7. Table 2-1, data from 106 men, 160 women • Women’s M=2.8, Median = 1, Mode = 1 • Men’s M=64.3, Median = 1, Mode = 1 • See Fig 2-8, how do outliers affect the mean in this study? Median?

  8. Location of Mean, Mode, Median

  9. Measures of SpreadThe Variance • The average of each score’s squared difference from the mean • Steps for computing the variance: 1. Subtract the mean from each score 2. Square each of these deviation scores 3. Add up the squared deviation scores 4. Divide the sum of squared deviation scores by the number of scores

  10. Measures of SpreadThe Variance • Formula for the variance: SS- Sum of Squares SD=Standard Deviation (when squared = variance)

  11. What variance tells us • Conceptually, it is the average of the squared deviation scores, so… • The more spread out the distribution, the larger the variance • What if variance = 0? • Very important for many stat tests • Conceptual difference in unit of variance versus standard deviation? • Which is more intuitive?

  12. Measures of SpreadThe Standard Deviation • Most common way of describing the spread of a group of scores • Steps for computing the standard deviation: 1. Figure the variance 2. Take the square root • Conceptually, it is the average of deviations from the mean. • How much do most scores differ from the mean?

  13. Measures of SpreadThe Standard Deviation • Formula for the standard deviation:

  14. SD Computational Formula: • Easier to use w/large data sets • Uses sum of x scores (X) and sum of squared x scores (X2) • SD2 = X2 – [(X)2 / N] N • Note that your book prefers the definitional formula, not this one • p. 51 – some instances when we divide SS by N-1

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