Statistics - You should also watch for curved relationships...

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AP Statistics Chapter 3 3.1 A response variable measures an outcome of a study. An explanatory variable attempts to explain the observed outcomes. It is easiest to identify them from each other when we actually set values of one variable in order to see how it affects another variable. The most effective way to display the relation between two quantitative variables is a scatterplot, which shows the relationship when measured on the same individuals. The explanatory variably should always be plotted on the horizontal axis. To interpret a scaterplot you look for direction form and strength of the relationship of the two variables. The direction can either be a positively associated or a negatively associated relationship. A Linear relationship is when the points show a straight-line pattern.
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Unformatted text preview: You should also watch for curved relationships and clusters. After examining the overall pattern of the scatterplot, look for deviations from the pattern such as outliers. 3.2 The correlation measures the strength and direction of the linear relationship between two quantitative variables. It Is written as r. You always have to standardize the observation the equation for this is: r is positive when there is a positive association between the variables. Patterns closer to a straight line have correlations closer to 1 or -1 Perfect correlation is r= 1 Correlation ignores the distinction between explanatory and response variables. The value of r is not affected by changes in the unit of measurement of either variable....
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This note was uploaded on 04/10/2011 for the course MATH 1070 taught by Professor Akbas during the Fall '08 term at Georgia State.

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