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14 Cards in this Set
- Front
- Back
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Review Flash Lecture Slides--LESSON 6
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YES!
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The Center of the Distribution is the median
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True
When we think of a typical value, we usually look for the center of th distribution. |
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Finding the Median by hand
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Review pg. 57
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Spread
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When we describe a distribution numerically, we always report a measure of its SPREAD along with its center
We measure the spread by calculating the RANGE Range = Max value - Min Value |
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The Range like the midrange has the disadvantage that a single extreme value can make it very large, giving a value that doesn't really represent the data OVERALL
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True
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The Interquartile Range--Represents the middle 50% of the data
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Concentrates on the middle of the data.
The difference between the quartiles tells us how much territory the middle half of the data covers and is called the IQR IQR = upper quartile - lower quartile Divide the data in half at the median. Now divide both halves in half again, cutting the data into four quarters. We call these new dividing points quartiles. one quarter of the data lies below the lower quartile, and one quarter of the data lies above the upper quartile, so half the data lies between them. The quartiles border the middle half of the data--or the IQR |
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The lower and upper quartile are also known as the____
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25th and 75th percentiles of the data.
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The 5 Number Summary
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of a distribution reports
-median -Q3, Q1 -Max -Min |
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The Mean
(Average) |
when the distribution is unimodal and symmetric, we'll use the mean
Good when the data are symmetric and fairly balanced, if not it won't give a very good idea of the typical condition The mean feels like the center because it is the point where the histogram balances (pg. 62) |
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Why is the median better to use when data is skewed or not balanced very well when finding the typical condition in a set of data?
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b/c the median considers only the order of the values, it is resistant to values that are extraordinarily large or small; it simply notes that they are one of the "big ones" or the "small ones" and ignores the distance from the center.
if you try to use the mean an outlier could pull the mean away from the actual center of the data |
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Standard Deviation (s, s^2=variance)
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The standard deviation is appropriate only for symmetric data
one way to think about SPREAD is to examine how far each data value is from the mean. The difference is called a deviation. |
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What is the IQR and why is it a suitable measure of Spread?
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Like the Median the IQR is not affected by the outlying value or by the skewness of the distribution, so it is an appropriate measure of spread for skewed data
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What to TELL about a Quant. Variable
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Review page 66 now!
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Common Mistakes When Making Graphs--review pg 70
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Don't make a histogram of a categorical variable
Don't forget to sort the vlues before finding the <b> median or percentiles </b> Don't worry about small differences when using different methods Don't compute numerical summaries of a categorical variable Don't report too many decimal places Watch out for multiple modes (break the modes up and report them separately) Beware of outliers (the median and IQR are resistant to outliers, but the mean and standard deviation are not) Always MAKE A PICTURE! helps you spot outliers and notice initial trends about the data Don't look for shape center and spread of a bar chart(categorical) Don't use bars in every display--save them for histograms and bar charts Choose a bin width appropriate to the data |
