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Chapter 4 Two-Variables Analysis 09/19-20/2013. Outline  Issue: How to identify the linear relationship between two variables?  Relationship: Scatter.

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Presentation on theme: "Chapter 4 Two-Variables Analysis 09/19-20/2013. Outline  Issue: How to identify the linear relationship between two variables?  Relationship: Scatter."— Presentation transcript:

1 Chapter 4 Two-Variables Analysis 09/19-20/2013

2 Outline  Issue: How to identify the linear relationship between two variables?  Relationship: Scatter Plot is a collection of observations on an X-Y graph Covariance conveys the direction of the potential relationship Correlation coefficient measures the strength of a linear relationship between two variables  Causality and predictions: Least squares line

3 Scatter Plot

4 Scatter Plot: Degree of Association

5 Covariance  A measure of the strength of a linear relationship between two variables  While the magnitude changes with the units, its sign conveys direction only.  Positive covariance  Positive linear relationship  Negative covariance  Negative linear relationship Population CovarianceSample CovarianceRelation

6 Correlation coefficient  Unit-free and always between -1 (perfectly negative linear relationship) and +1 (perfectly positive linear relationship)  The greater the absolute value of the correlation coefficient, the stronger the linear relationship. Population Correlation Sample Correlation

7 Least squares line  A Unique line that describes the relationship between two variables, when one causes the other. It has the smallest sum of squared error!

8 Sum of Squared Error is the observed value andis the predicted value.


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