PSC 41 Lecture Notes - Lecture 12: Standard Deviation, Statistical Inference, Rds-1
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PSC 41 Full Course Notes
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Ex: the mean for central tendency, and the variance/standard deviation for. We eventually want to perform inferential statistics, but first we needed descriptive statistics variability. The second step is understanding standard scores, probability, and the normal distribution. Ex: joe, a high school student, is concerned about his weight. In statistics, we make decisions (ex: defining someone as overweight", underweight" or. Info: mean weight = 150, standard deviation = 10. How about jamie, another high school student, who weighs 145 pounds. We use z-scores and the normal distribution to compute probabilities for individual observations. Z-scores = represent a transformation of a variable. The transformation uses the mean and the standard deviation of the variable. The purpose is to give the variable a new metric that has more meaning. Change celsius to fahrenheit because we more familiar with this metric. Has the shape of a bell (symmetric and unimodal) Distribution defined by 2 parameters: mean and standard deviation (or variance)
