# IPS6eCh02_1bb - Looking at Data Relationships Scatterplots...

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Looking at Data - Relationships   Scatterplots IPS Chapter 2.1

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Objectives (IPS Chapter 2.1) Scatterplots Scatterplots Explanatory and response variables Interpreting scatterplots Outliers Categorical variables in scatterplots Scatterplot smoothers
Examining Relationships Most statistical studies involve more than one variable. Questions: What individuals does the data describe? What variables are present and how are they measured? Are all of the variables quantitative? Do some of the variables explain or even cause changes in other variables?

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Student Beers Blood Alcohol 1 5 0.1 2 2 0.03 3 9 0.19 6 7 0.095 7 3 0.07 9 3 0.02 11 4 0.07 13 5 0.085 4 8 0.12 5 3 0.04 8 5 0.06 10 5 0.05 12 6 0.1 14 7 0.09 15 1 0.01 16 4 0.05 Here, we have two quantitative variables for each of 16 students. 1) How many beers they drank, and 2) Their blood alcohol level (BAC) We are interested in the relationship between the two variables: How is one affected by changes in the other one?
Looking at relationships Start with a graph Look for an overall pattern and deviations from the pattern Use numerical descriptions of the data and overall pattern (if appropriate)

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Student Beers BAC 1 5 0.1 2 2 0.03 3 9 0.19 6 7 0.095 7 3 0.07 9 3 0.02 11 4 0.07 13 5 0.085 4 8 0.12 5 3 0.04 8 5 0.06 10 5 0.05 12 6 0.1 14 7 0.09 15 1 0.01 16 4 0.05 Scatterplots In a scatterplot, one axis is used to represent each of the variables, and the data are plotted as points on the graph.
Explanatory (independent) variable: number of beers Response (dependent) variable: blood alcohol content x y Explanatory and response variables A response variable measures or records an outcome of a study. An explanatory variable explains changes in the response variable.

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• Winter '09
• Brown
• blood alcohol content, overall pattern

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