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CurveFitting
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2-7 Curve
Warm Up
Lesson Presentation
Lesson Quiz
Holt
Algebra
Holt
Algebra
22
2-7 Curve Fitting with Linear Models
Warm Up
Write the equation of the line passing through
each pair of passing points in slope-intercept
form.
1. (5, –1), (0, –3)
2. (8, 5), (–8, 7)
Use the equation y = –0.2x + 4. Find x for each
given value of y.
3. y = 7
x = –15
Holt Algebra 2
4. y = 3.5
x = 2.5
2-7 Curve Fitting with Linear Models
Objectives
Fit scatter plot data using linear models
with and without technology.
Use linear models to make predictions.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Vocabulary
regression
correlation
line of best fit
correlation coefficient
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Researchers, such as
anthropologists, are
often interested in how
two measurements are
related. The statistical
study of the relationship
between variables is
called regression.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
A scatter plot is helpful in understanding the
form, direction, and strength of the relationship
between two variables. Correlation is the
strength and direction of the linear relationship
between the two variables.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
If there is a strong linear relationship between two
variables, a line of best fit, or a line that best fits
the data, can be used to make predictions.
Helpful Hint
Try to have about the same number of points
above and below the line of best fit.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Example 1: Meteorology Application
Albany and Sydney are
about the same distance
from the equator. Make
a scatter plot with
Albany’s temperature as
the independent
variable. Name the type
of correlation. Then
sketch a line of best fit
and find its equation.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Example 1 Continued
Step 1 Plot the data points.
Step 2 Identify the correlation.
Notice that the data set is
negatively correlated–as the
temperature rises in Albany, it
falls in Sydney.
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Holt Algebra 2
2-7 Curve Fitting with Linear Models
Example 1 Continued
Step 3 Sketch a line of best fit.
Draw a line that splits
the data evenly above
and below.
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Holt Algebra 2
2-7 Curve Fitting with Linear Models
Example 1 Continued
Step 4 Identify two points on the line.
For this data, you might select (35, 64) and
(85, 41).
Step 5 Find the slope of the line that models the
data.
Use the point-slope form.
Point-slope form.
y – y1= m(x – x1)
y – 64 = –0.46(x – 35)
y = –0.46x + 80.1
Substitute.
Simplify.
An equation that models the data is y = –0.46x + 80.1.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
The correlation coefficient r is a measure of how
well the data set is fit by a model.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
You can use a graphing calculator to perform a
linear regression and find the correlation
coefficient r.
To display the correlation
coefficient r, you may have
to turn on the diagnostic
mode. To do this, press
and choose the
DiagnosticOn mode.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Example 2: Anthropology Application
Anthropologists can
use the femur, or
thighbone, to estimate
the height of a human
being. The table shows
the results of a
randomly selected
sample.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Example 2 Continued
a. Make a scatter
plot of the data
with femur
length as the
independent
variable.
The scatter plot is
shown at right.
Holt Algebra 2
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2-7 Curve Fitting with Linear Models
Example 2 Continued
b. Find the correlation coefficient r and the
line of best fit. Interpret the slope of the
line of best fit in the context of the problem.
Enter the data into lists L1
and L2 on a graphing
calculator. Use the linear
regression feature by
pressing STAT, choosing
CALC, and selecting
4:LinReg. The equation of
the line of best fit is
h ≈ 2.91l + 54.04.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Example 2 Continued
The slope is about 2.91, so for each 1 cm
increase in femur length, the predicted increase
in a human being’s height is 2.91 cm.
The correlation coefficient is r ≈ 0.986 which
indicates a strong positive correlation.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Reading Math
A line of best fit may also be referred to as a
trend line.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Lesson Quiz: Part I
Use the table for Problems 1–3.
1. Make a scatter
plot with mass
as the independent
variable.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Lesson Quiz: Part II
2. Find the correlation coefficient and the
equation of the line of best fit on your scatter
plot. Draw the line of best fit on your scatter
plot.
r ≈ 0.67 ;
y = 0.07x – 5.24
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Lesson Quiz: Part III
3. Predict the weight of a $40 tire. How
accurate do you think your prediction is?
≈646 g; the scatter plot and value of r show that
price is not a good predictor of weight.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Homework
P 148 15, 17, 23,
27, 31, 33
Due Friday
Holt Algebra 2