# Correlation and Regression. Section 9.1  Correlation is a relationship between 2 variables.  Data is often represented by ordered pairs (x, y) and.

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Correlation and Regression

Section 9.1

 Correlation is a relationship between 2 variables.  Data is often represented by ordered pairs (x, y) and graphed on a scatter plot  X is the independent variable  Y is the dependent variable

 A numerical measure of the strength and direction of a linear relationship between 2 variables x and y.  -1 < r < 1 The closer to -1 or 1, the stronger the linear correlation. The closer to 0, the weaker the linear correlation.

 25. The earnings per share (in dollars) and the dividends per share (in dollars) for 6 medical supply companies in a recent year are shown below.  (A) display data in a scatter plot,  (B) calculate the sample correlation coefficient r, and  (C) describe the type of correlation and interpret the correlation in the context of the data. Earnings, x2.795.104.533.063.702.20 Dividends, y0.522.401.460.881.040.22

Section 9.2

The line whose equation best fits the data in a scatter plot. We can use the equation to predict the value y for a given value of x. Recall: basic form of a line is y = mx + b We’ll use this form, but calculate m and b differently…

 18. The square footages and sale prices (in thousands of dollars) of seven homes. Then use the line of regression to predict the sale price of a home when x = A) 1450 sq ft B) 2720 sq ft C) 2175 sq ft D) 1890 sq ft Sq Ft, x 1924159224132332155213121278 Sale Price, y 174.9136.9275.219.9120.099.9145.0

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