Transcript August 31

Describing Location in a Distribution
8.31.2016
Activity: Where do I stand? (p. 84)
 In this activity, you and your classmates will explore ways to describe
1.
2.
3.
4.
5.
6.
where you stand (literally!) within a distribution
There is a line on the floor
Make a human dotplot.You will stand at the appropriate spot along
the line for your height
I will copy the dotplot onto the board
Once back in your seats, calculate what percent of students in the
class have a height less than yours. This is your percentile in the
distribution of heights
Work with a partner (if you want) to calculate the mean and standard
deviation of the class’s heights.
Where does your height fall in relation to the mean? Above or below?
How far above or below? How many standard deviations above or
below the mean is it? This last number is the z-score for your height
Percentile
 The Pth percentile of a distribution is the value in the with p
percent of the observations less than it
 So, if 25% of the teachers did worse than Mr. Wetherbee on
their Praxis scores, at what percentile was his score?
Percentile
 The Pth percentile of a distribution is the value in the with p
percent of the observations less than it
 So, if 25% of the teachers did worse than Mr. Wetherbee on
their Praxis scores, at what percentile was his score?
 25th
Think about it
 Why are percentiles useful?
Think about it
 Why are percentiles useful?
 Show us information about how an observation/individual
compares to others
 Does this by showing what percent are below that individual
 Often used to report scores on standardized tests
Let’s Try It
 The following data represents the number of wins of each of
the 30 major league baseball team in 2009, ranked from
fewest to most:
 59,62,64,65,65,70,70,74,75,75,75,78,79,80,83,84,85,86,
86,87,87,87,88,91,92,93,95,95,97,103
 The Rockies won 92 games. At what percentile do they fall?
 The Yankees won 103 games. At what percentile do they fall?
Let’s Try It
 The following data represents the number of wins of each of the
30 major league baseball team in 2009, ranked from fewest to
most:
 59,62,64,65,65,70,70,74,75,75,75,78,79,80,83,84,85,86,86,87
,87,87,88,91,92,93,95,95,97,103
 The Rockies won 92 games. At what percentile do they fall?
 24 of 30 (.8) teams did worse than the Rockies
 So they are at the 80th percentile
 The Yankees won 103 games. At what percentile do they fall?
 29 of 30 teams did worse than the Yankees, so they are at about the
97th percentile
Z-score
 The Z-score for a value in a dataset is the number of standard
deviations that it falls away from the mean of the dataset
 𝑧=
𝑥−𝑥
𝑠𝑥
 Or, in natural language, the value minus the mean all divided
by the standard deviation
 Why are z-scores useful?
 Why are z-scores useful?
 Also gives us information about how an individual compares to
others
 Does this by showing how many standard deviations above or
below the mean an individual is
 This is particularly useful when comparing dissimilar things
 Questions like: “Am I better at math than my friend is at English?”

“Is the Broncos’ defense better at defense than the Steelers’ offense is
at offense?”
Is the Broncos’ defense better than the
Steelers’ offense?
 Let’s calculate it
 Mean of offensive yards/play: 5.45
 Standard deviation of yards/play: .3297
 Steelers’ offense: 6.22 yards/play
 (6.22-5.45)/.3297= 2.336= z-score
 Steelers’ offense is 2.336 standard deviations above the mean
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


Mean of defensive yards/play: 5.45
Standard deviation of yards/play: .4235
Broncos’ defense: 4.48 yards/play
(6.22-4.48)/.4235= -2.291
 But remember, on defense a lower number is better
 However, the Steelers’ offense does seem to be just a little bit better than the
Broncos’ defense
 At least in terms of yards/play
Cumulative Relative Frequency
 What does this graph tell
us?
Cumulative Relative Frequency
 What does this graph tell
us?
 President Obama was 47
when inaugurated. Was he
abnormally young?
Cumulative Relative Frequency
 What does this graph tell
us?
 President Obama was 47
when inaugurated. Was he
abnormally young?
 Roughly the 11th
percentile—89% were
older than him.
What happens when we transform our
data?
 Adding or subtracting a number to each observation
 How does it affect shape:
 Outliers:
 Center:
 Spread:
What happens when we transform our
data?
 Adding or subtracting a number (X) to each observation
 How does it affect shape: no effect
 Center: shifts it by X
 Spread: no effect
What happens when we transform our
data?
 Multiplying by a number (X) to each observation
 How does it affect shape:
 Center:
 Spread:
What happens when we transform our
data?
 Multiplying by a number (X) to each observation
 How does it affect shape: no effect
 Center: multiplies it by X
 Spread: multiplies it by X
What happens when we transform our
data?
 What happens to our Z-scores when we transform our data?
What happens when we transform our
data?
 What happens to our Z-scores when we transform our data?
 Does not change!
 This allows us to compare Z-scores of variables that are on
many different scales
 Because the z-score is telling us in terms of standard deviations
Homework
 P. 105: 1,5,9-15,19-23,31-38