AP Statistics - Chapter 3 Summary Sheet
CSSO: Use this acronym when being asked to comment on the appearance of or to describe any distribution of
single variable data. It stands for Center – Shape – Spread – Outliers.
FSDD: Use this acronym when being asked to comment on the appearance of or to describe any scatterplot of
bivariate data. It stands for Form – Strength – Direction – Deviations.
When asked to explain the meaning of – or to interpret:
Slope ( b ) of the LSRL :
There is a(n) (numerical slope value increase/decrease) in the ( Response variable named with the correct
units) per a one unit increase in the (Explanatory Variable named with correct units).
Correlation Coefficient r :
There is a (very strong , strong, moderate, weak, very weak) (positive/negative) linear relationship between
(state the name of the response variable in the context of the problem) and (state the name of the explanatory
variable in the context of the problem) .
Coefficient of determination r 2
:
(Percentage arrived at on calculator) % of the VARIATION in the (state the name of the response variable in
the context of the problem) can be explained by a linear relationship to the (state the name of the explanatory
variable in the context of the problem).
(or)
(Percentage arrived at on calculator) % of the VARIATION in the (state the name of the response variable in
the context of the problem) can be explained by the LSRL of (state the name of the response variable in the
context of the problem) on (state the name of the explanatory variable in the context of the problem)
Facts on Correlation:
1. Correlation makes no distinction regarding which you call x and which you call y. Switch the axes, and
the r – value remains the same.
2. Correlation requires Quantitative Variable only. (Ex: There is no such thing as a correlation between
race and crime; or gender and IQ.)
3. Because r (based on standardized z-values) is itself, unit-less … changes units of measure will not alter
the r value. (Ex: height in inches vs. age in months, r =.963. Repeat same data with height in cm vs.
age in days, and the r still will - .963)
4. Positive r indicates a positive association (and positive sloped LSRL); Negative r indicates a negative
association (and a negatively slope LSRL).
5. ; Where r = 1 is a perfectly linear positive relationship; and r = -1 is a perfectly linear
negative relationship. An r = 0 indicates the weakest linear association between variables. AS you move
away from 0 to either extreme, this indicates a stronger relationship.
6. Correlation is ONLY applicable to measuring the strength of a LINEAR relationship.
7. Similar to the mean and standard deviation, r is very much so influenced by individual extreme values
(regression outliers and influential points). That is to say … r is a “non-resistant measure.
