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Chapter 7: Scatterplots, Association, and Correlation

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Gayle Smith

on 5 March 2015

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Transcript of Chapter 7: Scatterplots, Association, and Correlation

Chapter 7: Scatterplots, Association, and Correlation
Scatterplots are a type of display that shows the relationship between two quantitative variables. They make it easy to identify trends and patterns amongst the variables.
"r" is always used for correlation

Correlation Coefficient
Correlation Conditions
Positive Association:
As the x-value increases the y-value also increases
Negative Association:
As the x-value increases, the y-value decreases
No relationship among x and y values
Association Strength
"r" is a value between -1 and 1
"r" summarizes the direction and strength of the association for all points
Quantitative Variable Condition:
Both variables must be
Straight Enough Condition:
The form must be "straight enough". Correlation only works with
linear associations
Outlier Condition:
If an outlier is present, it is your best option to report the correlation
using the outlying point
Correlation ≠ Causation
Major Points
Correlation is not the same as association. Association is the relationship between two variables. Correlation measures the strength and direction of the linear relationship
Don't correlate categorical variables. Always check the Quantitative Variables Condition
The association must be linear
Just because the correlation coefficent is high does not mean the relationship is linear. Always look at the scatterplot
Beware of outliers. Some outliers can have a huge influence on your correlation coefficient.
Correlation does not equal Causation
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