Name _______________________________________ Date __________________ Class _________________
MODULE
8
Modeling with Linear Functions
Module 8 Study Guide
5. The table shows the number of
employees in a company over five years.
2. Which correlation best describes the
scatter plot below?
A Positive
B Negative
Which of the following is the best
prediction of how many employees the
company will have in 2008?
C No correlation
3. Which of the following correlation
coefficients indicates a strong negative
correlation?
A 35
A 0.8
B 50
B 0
C 43
6. What is the sum of the squared residuals
shown on the table?
C 0.8
4. Which is the best equation of a line of fit for
the data represented on the scatter plot?
y  1.6 x  8
x
A y  
4
x  3.6
5
B y  
4
 4.3
5
C y  
4
 7.5
5
y (actual) y (predicted) residuals
0
9
2
9.2
4
15
6
17.6
A 0.16
B 5.36
C 12.96
7. The squared residual of line of fit A is
2.12. The squared residual of line of fit B
is 1.94. Which of the following statements
best describes the lines?
A Line A better fits the data.
B Line B better fits the data.
C Not enough information is given to
compare the lines of fit.
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45
Name _______________________________________ Date __________________ Class _________________
MODULE
8
Modeling with Linear Functions
Module 8 Study Guide
11. a.
Complete the table.
p  5x  4
9. Make a scatter plot for the points
(2, 1), (4, 2), (5, 5) and (7, 6).
x
p (actual) p (predicted) residuals
2
5
0
6
2
13
4
27
b. Find the sum of the squared residuals.
10. The table shows the number of runners in
an annual race over four years.
Year
’08
’09
’10
’11
Number of Runners
21
35
46
50
____________________________________
a. Draw a scatter plot and a trend line.
12.Keisha is trying to figure out which line of
fit is better for the data presented on the
table below, y   x  4 or y  3 x  2 .
Y = -x + 4
x
b. Which is the best prediction for the
number of runners in 2013, 40 or 72?
Explain your answer.
y (actual) y (predicted) residuals
-2
6
4
0
4
1
2
2
-1
4
0
0
Y = -3x - 2
___________________________________
x
c. Find residual in 2010.
___________________________________
y (actual) y (predicted) Residuals
-2
4
4
0
-2
1
2
-8
-1
4
-12
0
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46
Name _______________________________________ Date __________________ Class _________________
b. Find the squared residual of the line
of fit y   x  4.
___________________________________
c. Find the squared residual of the line
of fit y  3x  2.
___________________________________
d. Which line better fits the data?
___________________________________
13. Is the function a good fit between the
function and its data set?
14.
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46
Name _______________________________________ Date __________________ Class _________________
11. a.
Module 8 Study Guide
p  5x  4
2. A
3. A
4. B
5. C
6. B
x
y  1.6 x  8
y (actual) y (predicted) residuals
x
0
9
8
1
2
9.2
11.2
-2
4
15
14.4
0.6
6
17.6
17.6
0
p (actual) p (predicted) residuals
2
5
6
1
0
6
4
2
2
13
14
1
4
27
24
3
b. 15
12.
Y = -x + 4
x
7. B
9.
y (actual) y (predicted) residuals
-2
6
4
2
0
4
1
3
2
2
-1
3
4
0
0
0
Y = -3x - 2
x
10. a.
y (actual) y (predicted) residuals
-2
4
4
0
0
-2
1
-3
2
-8
-1
-7
4
-12
0
-12
b. 22
c. 202
d. y   x  4
13. The line is probably not a good fit.
14.
b. 72; the trend line is closer to 72
than 40 when the year is 2013.
c. Answers vary
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46