# CIS2300_TEST1

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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
x-µ
• the average of the absolute values of the deviations around the mean for a set of numbers
• 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
 Author: Anonymous ID: 7294 Card Set: CIS2300_TEST1 Updated: 2010-02-18 20:27:07 Tags: CIS2300 TEST1 Exam1 1 Chapter Ch 1 4 Business Statistics MSCD Folders: Description: Exam 1 for CIS2300 Business Statistics Show Answers: