1. Correlation and Regression

2. Section 9.1

3.  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

5.  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.

6.  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

7. Section 9.2

8. 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…

10.  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