1. Population
    • a collection of persons, objects or items of interest.
    • Whatever the researcher is studying
  2. parameter
    • a descriptive measure of the population. Usually denoted by Greek letters
    • e.g. mean(µ), population variance(σ^2), populuation standard deviation(σ)
  3. sample
    a portion of the whole and if taken properly, representative of the whole
  4. statistic
    • a descriptive measure of the sample. Usually denoted by Roman letters
    • e.g. mean(x *bar*), sample variance (s^2), sample standard deviation(s)
  5. Descriptive Statistics
    • Using data gathered on a group to describe or reach concclusions about that same group
    • e.g. most athletic stats. The data is gathered from that group and conclusions are drawn about that group only. Basketball stats are about Basketball
  6. Inferential Statistics
    • gathering data from a sample and use the statistics generated to reach conlusions about the population from which the sample was taken
    • sometimes referred to as inductive statistics
  7. 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%
  8. Population Mean
    • µ = (∑x)/N
    • where x = actual data values
    • N = # total terms
  9. standard deviation
    • square root of the variance
    • σ = sqrt(σ)
    • Σ = sqrt( (∑(x- µ)^2)/N)
  10. sum of squares of x
    • SSx
    • The sum of the squared deviations about the mean of a set of values
  11. variance
    • average of the squared deviations about the arithmetic mean for a set of numbers
    • Population Variance
    • - σ^2 = (∑(x- µ)^2)/N)
  12. deviation from the mean
  13. mean absolute deviation (MAD)
    • the average of the absolute values of the deviations around the mean for a set of numbers
    • MAD = (∑|x-µ|)/N
    • where
    • x-µ = actual value of a given number minus the mean
    • N= Number of terms
  14. 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>1
    • e.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.
  15. sample variance
    • variance: s^2 = ∑(x- x(bar))^2)/(n-1)
    • also
    • s^2 = (∑x^2 - ((∑x)^2)/n)/n-1
    • where
    • x = actual value
    • x(bar) = sample mean
    • n = sample number
  16. sample standard deviation
    • sqrt(s^2) where s^2 =
    • s^2 = (∑x^2 - ((∑x)^2)/n)/n-1
Card Set
Exam 1 for CIS2300 Business Statistics