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## Measures of Central Tendency

by: Andrew Isbell

6

0

2

# Measures of Central Tendency MAT 120

Marketplace > Tri-County Technical College > Mathmatics > MAT 120 > Measures of Central Tendency
Andrew Isbell
TCTC

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These notes cover mean and median primarily and how to calculate them
COURSE
Probability and Statistics
PROF.
Merle Glick
TYPE
Class Notes
PAGES
2
WORDS
CONCEPTS
Mathematics, Probability, Statistics
KARMA
25 ?

## Popular in Mathmatics

This 2 page Class Notes was uploaded by Andrew Isbell on Tuesday August 30, 2016. The Class Notes belongs to MAT 120 at Tri-County Technical College taught by Merle Glick in Fall 2016. Since its upload, it has received 6 views. For similar materials see Probability and Statistics in Mathmatics at Tri-County Technical College.

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Date Created: 08/30/16
Measures of Central Tendency  Andrew Isbell  Objectives  1.) Determine the mean of a variable from raw data  2.) Determine the median of a variable from raw data  3.) Determine the mode of a variable from raw data  ­Arithmetic mean­ a mean computed by adding all values and dividing by the number of values added  ­Population arithmetic mean­ computed using a known mean, this is usually an estimate ­Sample arithmetic mean­ computed using sample data, this is a statistic  Finding a median in a data set: ­List numbers smallest to largest, then use the middle number  ­If odd, add the middle two together and divide by two  ­Sometimes it is better to use the mean than the median because the mean is resistant to unrealistic values  which may not accurately represent the raw data.  ­Shapes of Distribution: ­Skewed left­ mean is substantially smaller than the median  ­Symmetric­ mean roughly equal to the median  ­Skewed right­ the mean is substantially bigger than the median  ­Empirical Rule­ judgement of population based on sample­ only with bell­shaped data  ­Chebyshev’s Rule­ any data shape/set or distribution: (1­1/k^2) x 100% _K is the number of standard deviations from the mean  ­Chebyshev’s Rule is not as accurate as the Empirical Rule, yet has the benefit of being applicable with  any data set  Empirical Rule Diagram

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