# CIS2300_TEST1

 .remove_background_ad { border: 1px solid #555555; padding: .75em; margin: .75em; background-color: #e7e7e7; } .rmbg_image { max-height: 80px; } Population a collection of persons, objects or items of interest.Whatever the researcher is studying parameter a descriptive measure of the population. Usually denoted by Greek letterse.g. mean(µ), population variance(σ^2), populuation standard deviation(σ) sample a portion of the whole and if taken properly, representative of the whole statistic a descriptive measure of the sample. Usually denoted by Roman letterse.g. mean(x *bar*), sample variance (s^2), sample standard deviation(s) Descriptive Statistics Using data gathered on a group to describe or reach concclusions about that same groupe.g. most athletic stats. The data is gathered from that group and conclusions are drawn about that group only. Basketball stats are about Basketball Inferential Statistics gathering data from a sample and use the statistics generated to reach conlusions about the population from which the sample was takensometimes referred to as inductive statistics emprical rule The approximate values that lie within a given number of standard deviations from the mean of a set of data if the data are normally distributed.Distance from the Mean Values within Distance µ + 1σ 68% µ + 2σ 95% µ + 3σ 99.7% Population Mean µ = (∑x)/Nwhere x = actual data valuesN = # total terms standard deviation square root of the varianceσ = sqrt(σ)Σ = sqrt( (∑(x- µ)^2)/N) sum of squares of x SSxThe sum of the squared deviations about the mean of a set of values variance average of the squared deviations about the arithmetic mean for a set of numbersPopulation Variance- σ^2 = (∑(x- µ)^2)/N) deviation from the mean x-µ mean absolute deviation (MAD) the average of the absolute values of the deviations around the mean for a set of numbersMAD = (∑|x-µ|)/Nwhere x-µ = actual value of a given number minus the meanN= Number of terms Chebyshev's Theorem at least (1-1/k^2) values will fall within + k standard deviations of the mean regardless of the shape of the distribution. Assume k>1e.g. k=2.5, 1-1/(2.5^2) = .84. so at least .84 of all values are within µ + 2.5σ.or at least .84 of all values will be within 2.5 standard deviations of the mean, µ. sample variance variance: s^2 = ∑(x- x(bar))^2)/(n-1)alsos^2 = (∑x^2 - ((∑x)^2)/n)/n-1where x = actual valuex(bar) = sample meann = sample number sample standard deviation sqrt(s^2) where s^2 = s^2 = (∑x^2 - ((∑x)^2)/n)/n-1 .remove_background_ad { border: 1px solid #555555; padding: .75em; margin: .75em; background-color: #e7e7e7; } .rmbg_image { max-height: 80px; } AuthorAnonymous ID7294 Card SetCIS2300_TEST1 DescriptionExam 1 for CIS2300 Business Statistics Updated2010-02-18T20:27:07Z Show Answers