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Ch13_Correlation

Ch13_Correlation - Chapter 13 Correlation Introduction to...

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Chapter 13 Correlation
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Introduction to Correlation Three types of Research: Descriptive, Relational and Experimental. So far we have learned in experimental research: independent variable manipulated dependent variable measured. (i.e., background music – test scores) Relational studies ask whether 2 variables are related. Neither variable is manipulated No dependent or independent variables Direction and strength of the relationship is of interest
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Definitions Bivariate distribution : 2 scores are obtained from each subject (i.e., loneliness and depression scores). Covary : 2 variables covary when a change in x is related to a consistent change in y.
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Definitions Scatterplot : A graphing approach usually used to show a bivariate distribution where every subject is represented in the graph. A score on each of the two measures is given by one point. x y
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Definitions Linear relationship : relationship between two variables that can be described by a straight line. In a perfect correlation, knowing the score on one variable allows us to exactly predict the score of the other variable. Scatterplot 1 Scatterplot 2
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Definitions Positive relationship : As the value of x increases, the value of y increases. Negative [inverse] relationship : As the value of x increases, the value of y decreases. x=Age x=Drink y=Smoke y=Vision
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What is correlation? Correlation represents the extent to which two variables have a linear relationship. Therefore, to calculate correlations we need pairs of numbers (i.e., height =60 inches, weight=150 pounds). Correlation coefficients “r” are descriptive statistics that describe the degree/strength of relationship between 2 variables.
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Pearson Correlation Karl Pearson came up with a number to represent this relationship and called it Pearson Product Correlation .
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