MGEB11H3 Lecture Notes - Fall 2018 Lecture 3 - Standardized test, Cierva C.6
21 Sep 2018
School
Department
Course
Professor
Document Summary
Mgeb11 lecture 3: numerical measures co(cid:374)(cid:859)t: measures of center (location, mean - average, medium, mode, measures of variation (spread, range, variance, standard deviation, coefficient of variation, the use of standard deviation, example: a test is given. Let x = test score, z = standardized test score *how many s. dev away from the mean* (z= (x mew)/sigma has mean=0, and std dev = 1) Similarly in this test, mew=60 and sigma = 10. Chebyshev theorem: a population has mean (mew) and std. dev (sigma) Empirical rule a population has mean and s. dev. Suppose the population data follow a bell-shape distribution. **concept is same as chebyshev except the assumption of bell-shape curve** Approximately 68% of data falls inside (mew sigma, mew + sigma) Approximately 95% of the data fall inside (mew-2sigma, mew + 2sigma) Approximately 99. 74% fall inside (mew-3sigma, mew + 3sigma: example: a city has avrg household income (measured in ) as and s. dev .
