CurveFitting
Fittingwith
withLinear
LinearModels
Models
2-7
2-7 Curve
Warm Up
Lesson Presentation
Lesson Quiz
Holt
Holt
Algebra
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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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.
y – y1= m(x – x1)
y – 64 = –0.46(x – 35)
y = –0.46x + 80.1
Point-slope form.
Substitute.
Simplify.
An equation that models the data is y = –0.46x + 80.1.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Check It Out! Example 1
Make a scatter plot for this set of data.
Identify the correlation, sketch a line of best
fit, and find its equation.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Check It Out! Example 1 Continued
Step 1 Plot the data points.
Step 2 Identify the correlation.
Notice that the data set is
positively correlated–as time
increases, more points are
scored
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Holt Algebra 2
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2-7 Curve Fitting with Linear Models
Check It Out! 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
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2-7 Curve Fitting with Linear Models
Check It Out! Example 1 Continued
Step 4 Identify two points on the line.
For this data, you might select (20, 10) and (40, 25).
Step 5 Find the slope of the line that models the data.
Use the point-slope form.
y – y1= m(x – x1)
y – 10 = 0.75(x – 20)
y = 0.75x – 5
Point-slope form.
Substitute.
Simplify.
A possible answer is p = 0.75x + 5.
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
Example 2 Continued
c. A man’s femur is 41 cm long. Predict the
man’s height.
The equation of the line of best fit is
h ≈ 2.91l + 54.04. Use the equation to predict the
man’s height.
For a 41-cm-long femur,
h ≈ 2.91(41) + 54.04 Substitute 41 for l.
h ≈ 173.35
The height of a man with a 41-cm-long femur
would be about 173 cm.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Check It Out! Example 2
The gas mileage for randomly selected cars
based upon engine horsepower is given in the
table.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Check It Out! Example 2 Continued
a. Make a scatter
plot of the data
with horsepower
as the independent
variable.
The scatter plot is
shown on the right.
Holt Algebra 2
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2-7 Curve Fitting with Linear Models
Check It Out! 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
y ≈ –0.15x + 47.5.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Check It Out! Example 2 Continued
The slope is about –0.15, so for each 1 unit
increase in horsepower, gas mileage drops ≈
0.15 mi/gal.
The correlation coefficient is r ≈ –0.916, which
indicates a strong negative correlation.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Check It Out! Example 2 Continued
c. Predict the gas mileage for a 210-horsepower
engine.
The equation of the line of best fit is
y ≈ –0.15x + 47.5. Use the equation to predict
the gas mileage. For a 210-horsepower engine,
y ≈ –0.15(210) + 47.50.
Substitute 210 for x.
y ≈ 16
The mileage for a 210-horsepower engine would be
about 16.0 mi/gal.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Example 3: Meteorology Application
Find the following for
this data on average
temperature and
rainfall for eight
months in Boston, MA.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Example 3 Continued
a. Make a scatter plot of the data with
temperature as the independent variable.
The scatter plot is
shown on the right.
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Holt Algebra 2
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2-7 Curve Fitting with Linear Models
Example 3 Continued
b. Find the correlation coefficient and the
equation of the line of best fit. Draw the line of
best fit on your scatter plot.
The correlation
coefficient is
r = –0.703.
The equation of the
line of best fit is
y ≈ –0.35x + 106.4.
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2-7 Curve Fitting with Linear Models
Example 3 Continued
c. Predict the temperature when the rainfall
is 86 mm. How accurate do you think
your prediction is?
86 ≈ –0.35x + 106.4 Rainfall is the dependent variable.
–20.4 ≈ –0.35x
58.3 ≈ x
The line predicts 58.3F, but the scatter plot and the
value of r show that temperature by itself is not an
accurate predictor of rainfall.
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
Check It Out! Example 3
Find the following information for this data set
on the number of grams of fat and the number
of calories in sandwiches served at Dave’s Deli.
Use the equation of the line of best fit to predict
the number of grams of fat in a sandwich with
420 Calories. How close is your answer to the
value given in the table?
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Check It Out! Example 3
a. Make a scatter plot of the data with fat
as the independent variable.
The scatter plot is
shown on the right.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Check It Out! Example 3
b. Find the correlation coefficient and the
equation of the line of best fit. Draw the
line of best fit on your scatter plot.
The correlation coefficient is
r = 0.682. The equation of
the line of best fit is
y ≈ 11.1x + 309.8.
Holt Algebra 2
2-7 Curve Fitting with Linear Models
Check It Out! Example 3
c. Predict the amount of fat in a sandwich
with 420 Calories. How accurate do you
think your prediction is?
420 ≈ 11.1x + 309.8
Calories is the dependent variable.
110.2 ≈ 11.1x
9.9 ≈ x
The line predicts 10 grams of fat. This is not close
to the 15 g in the table.
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