Chapter 1 Notes
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Organizing Data
AP Stats Chapter 1
Organizing Data
Categorical
Dotplot (also used for quantitative)
Bar graph
Pie chart
Quantitative
Stemplots
Unreasonable
with large data sets
Histogram
Frequency/relative
frequency
Describing Distributions
Remember “SECS-C”
S – Shape
E – Extreme Values (outliers)
C – Center
S – Spread
C – Context
**Make meaningful descriptions and
comparisons. Don’t just list numbers.**
Shape
Symmetric
Values smaller and larger than the midpoint
are mirror images.
Skewed
The tail on one end is much longer than the
other tail.
Example: Symmetric
Examples: Skewed
Ways to Measure Center
Mean
x =
x1 + x2 + . . . . + xn
n
xi
x =
n
The mean is not a resistant measure of
center. (sensitive to outliers)
Used mostly with symmetric distributions.
Ways to measure center
Median
Midpoint of a distribution
Median is a resistant measure of center
Used with symmetric or skewed distributions.
Ways to Measure Spread
1) Range
Highest value – lowest value
Problem: could be based on outliers
2) Quartiles (for use with median)
pth percentile – value such that p percent of the
observations fall at or below it
Q1 (quartile 1): 25th percentile
Median of the first half of the data
Q3 (quartile 3): 75th percentile
Median of the second half of the data
Ways to Measure Spread
5 Number Summary
Minimum, Q1, median, Q3, maximum
The 5-number summary for a distribution can
be illustrated in a boxplot.
1.5 x IQR Rule for Outliers
IQR = Q3 – Q1 (Interquartile Range)
Rule: If an observation falls more than 1.5
x IQR above Q3 or below Q1, then we
consider it an outlier.
The 5 Number Summary can be used for
distributions which are skewed, or which
have strong outliers.
Ways to Measure Spread
Standard deviation (for use with the mean)
Std Dev tells you, on average, how far
each observation is from the mean.
variance: s2 =
std dev: s =
(xi - x)2
n-1
(xi - x)2
n-1
Properties of Standard Deviation
s gets larger as the data become more
spread out.
Only use mean and std dev for reasonably
symmetric distributions which are free of
outliers.
Linear Transformation of Data
Xnew = a + bx
The shape of the distribution does not change.
Multiplying each observation by a positive
number, b, multiplies both measures of center
and measures of spread by b.
Adding the same number, a, to each observation
adds a to measures of center and to quartiles,
but does not change measures of spread.