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Chapter 5 lecture

# Chapter 5 lecture - CHAPTER 5 Two Quantitative Variables...

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10 15 20 10 20 30 40 50 60 70 80 Diameter Volume 60 70 80 100 150 200 250 Temp2 Elongation CHAPTER 5 Two Quantitative Variables’ Relationship 3 Tools: Scatterplot – a two-dimensional graph of data values Regression equation – an equation that describes the average relationship between a quantitative response variable and an explanatory variable Correlation – a statistic that measures the strength and direction of a linear relationship between two quantitative variables Scatterplots The response variable is often called the dependent variable and is plotted along the y-axis of the graph. The explanatory variable is often called the independent variable and is plotted along the x-axis of the graph. Two variables have a positive association when the values of one variable tend to increase and the values of the other variable increase. Two variables have a negative association when the values of one variable tend to decrease as the values of the other variable increase. If a straight line describes the general pattern of the graph, the two variables may have a linear relationship. Does the diameter of a certain type of tree predict the volume of the trunk of the tree? This scatterplot is an example of a positive linear relationship. What effect does temperature have on the elongation of a certain type of metal rod? This scatterplot is an example of a curvilinear relationship.

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30 40 50 60 70 80 90 100 30 40 50 60 70 80 90 midterm final Outliers in Scatterplots When considering two variables, an outlier is a point that has an unusual combination of data values.
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