PSYCH 100A Lecture Notes - Lecture 6: Standard Deviation, Central Tendency, Analysis Of Variance
Document Summary
Descriptive statistics: central tendency of scores in a distribution. Mean: the balancing point, a good estimator of its corresponding population value, takes into account every score. But, highly in uenced by outliers if small n: a least squares estimator. The sum of the squared di erence between scores in a distribution and the mean is as small as possible. Mode: variability of scores in a distribution. Range: max min, appropriate for ordinal, interval/ratio. Thursday, january 23, 2020: extremely crude: depends on only two numbers. Variability: to what degree do the scores spread out around the central tendency, what is the typical distance between a score and the central tendency. Examples of distributions: using favstats and favstats by categorical variable. Empty or null model examples: lm(), resid(), predict(, anova(), variance of residuals in null model. Adding in a substantive explanatory variable: reducing the variance, anova(), variance of residuals in null model.
