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, x
2.79
5.10
4.53
3.06
3.70
2.20
Dividends, y
0.52
2.40
1.46
0.88
1.04
0.22
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 1924
1592
2413 2332
174.9
Sale
Price, y
136.9 275.
219.9
1552
1312
1278
120.0
99.9
145.0