PSC 41 Lecture Notes - Lecture 13: Frequency Distribution, Central Tendency, Interquartile Range
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PSC 41 Full Course Notes
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Preliminaries: variable review, descriptive vs. inferential statistics, normal distribution. Continuous outcome data sets: measures of central tendency, measures of variability. Must vary (if not, it is a constant). Predictor variables (independent variables: a pre-existing characteristic used to classify subjects, i. v. Outcome variables (dependent variables: response, measured, depends on the level of the predictor variable. Categorical: levels vary in quality or kind, levels are labels or words, nominal and ordinal data. Continuous: values vary in quantity or amount, values are numbers along a scale, interval and ratio data. Descriptive statistics: describe the data set, quantify the observations. Inferential statistics: make inferences about the population. Your choice of appropriate statistics is based on : whether your variables are categorical or continuous. Is your outcome variable categorical or continuous: whether your population is normally distributed for this trait. Every individual or observation is under the curve. Area under the curve is 1 (or 100%). A skewed distribution is not symmetric around the mean.
