Textbook Notes (362,881)
Canada (158,081)
Psychology (900)
PSYC 210 (23)
Chapter 5

Chapter 5, 6, and 7

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Simon Fraser University
PSYC 210
Cathy Mc Farland

PSYC 210 CHAPTER 5 – MEASURES OF VARIABILITY (p. 75) Variability / Dispersion – the degree to which individual data points are distributed around the mean Range (R) – the distance from the lowest to the highest score - R = highest number – lowest number - Total reliance on extreme values Interquartile Range – the range of the middle 50% of the observations - Discards the lower and upper 25% of the distribution - If it discards too much data then it is not a very good estimate of the overall variability - Trimmed Samples – samples with a percentage of the extreme scores removed - Trimmed Statistics – statistics calculated on trimmed samples Variance - Mean absolute deviation (m.a.d.) – the average of the absolute values of the deviations from the mean - Sample Variance (s ) – sum of the squared deviations about the mean divided by N – 1 ( ) o 2 - Population variance (σ ) – variance of a population; usually an estimate, rarely computed Standard Deviation (SD, s or σ) – the square root of the variance ( ) - √ Bias – a property of a statistic whose long-range average is not equal to the parameter it estimates Expected Value or E() – the long range average of a statistic over repeated samples Boxplot – a graphical representation of the dispersion of a sample Box-and-whisker plot – a graphical representation of a sample Hinges (quartiles) – those points that cut off the bottom and top quarter of a distribution Quartile location – the location of the quartile in an ordered series - H-spread – the range between the two hinges (also the interquartile range) Whisker – line from the top and bottom of the box to the farthest point that is no more than 1.5 times the H-spread from the box ***see page 91*** Winsorized Variance – the variance of a winsorized sample Winsorized Sample – a sample in which trimmed observations are replaced with the highest and lowest values CHAPTER 6: THE NORMAL DISTRIBUTION Why the normal distribution is important - Many of the dependent variables are commonly assumed to be normally distributed in the population - If we can assume that a variable is at least approximately normally distributed, then we can make inferences about the values of that variable - The theoretical distribution of the hypothetical set of sample means obtained by drawing an infinite number of samples from a specified population can be shown to be approximately normal under a wide variety of conditions - Most statistical procedures have assumed that a variable is normally distributed Abs
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