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# Week5 - Chapter 7 Correlation and Simple Linear Regression...

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Chapter 7. Correlation and Simple Linear Regression Section 7.1. Correlation Scatterplots Plot of Y vs X, two quantitative variables, measured on the same individual X=explanatory variable, Y=response variable Interpreting scatterplots Direction (positive, negative) 1

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Strength (strong or moderate or weak ) Look for outliers. 2
Example: Interpret the following scatterplot. X=percent taking SAT Y=median SAT math score for each state Correlation A numerical measure of the strength and direction of linear association between two quantitative variables. Why? Because it is very hard to determine the strength of the linear association by eye. 3

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Facts about Correlation coefficient(r) Correlation does not distinguish between explanatory and response variables. r is always between -1 and +1. Interpretation: positive/negative, strong /weak. r measures only the strength of the linear relationship between x and y. Outliers can have a strong effect on r. Correlation is a “pure number,” in other words, it is unitless. r=+1 means perfect positive linear relationship. r=-1 means perfect negative linear relationship. It measures the linear relationship between two quantitative variables. The correlation coefficient r=0 doesn’t mean there is no relationship between two variables. It means there is no linear relationship between the variables. 4
CAUTION: Correlation measures the strength of the linear association between two variables. Therefore we can have two variables with a “low” correlation but with a strong association, which is not linear. Example: The variables X and Y are highly correlated and we can see a clear linear pattern. On the other hand, the correlation between the variables X and Z is small, but they also have a strong association. X Y 20 15 10 5 0 35 30 25 20 15 10 5 0 Scatterplot of Y vs X Correlations: X, Y Pearson correlation of X and Y = 1.000 5

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X Z 20 15 10 5 0 100 80 60 40 20 0 Scatterplot of Z vs X Correlations: X, Z Pearson correlation of X and Z = 0.191 Examples: 6
Formula: Correlation Coefficient Suppose you have n pairs of data points as: (x 1, y 1 ),…, (x n, y n ), Compute the sample means y x , and sample s.d.s s x and s y , where = = - - = - - = n i i y n i i x y y n s x x n s 1 2 1 2 ) ( 1 1 , ) ( 1 1 Compute z-scores: x i x s x x z - = , y i y s y y z - = Correlation coefficient: - - - = = y i x i n i s y y s x x n r 1 1 1 Note: You don’t need to remember this formula. You can use your calculator (should have 2-variable functions) or Minitab ( for homework problems). Why does the formula of r give the same sign (negative/positive) as the graph? 7

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Section 7.2. Least Squares Regression A method for finding the "best-fitting" line through a set of (x, y) points.
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