14. Use the output shown above and write an equation that can be used to predict the price of the stock. y = 33.575 +0.031x +0.359x2 b. a. = 118.506 - 0.016x1 - 1.573x2 y y = -0.125x1 - 1.06x2 d. с. y = 3.53 - 0.5176x - 4.381x2

Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter7: Analytic Trigonometry
Section7.6: The Inverse Trigonometric Functions
Problem 94E
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Question
The prices of Rawlston, Inc. stock (y) over a period of 12 days, the number of shares (in 100s) of company's stocks sold
(x1), and the volume of exchange (in millions) on the New York Stock Exchange (x2) are shown below.
Day
(x1)
950
(x2)
(v)
87.50
11.00
2
86.00
945
11.25
3
84.00
940
11.75
11.75
4
83.00
930
5
84.50
935
12.00
6.
84.00
935
13.00
13.25
7
82.00
932
8
80.00
938
14.50
78.50
925
15.00
10
79.00
900
16.50
77.00
77.50
11
875
17.00
12
870
17.50
Excel Analysis Toolpak was used to determine the least-squares regression equation. The computer output is shown
below.
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
0.949
0.901
Adjusted RS
Standard Em
Observations
0.879
1.205
12.000
ANOVA
df
MS
59.424
Significance F
SS
Regression
2.000
118.847
40.922
0.000
Residual
9.000
13.069
1.452
Total
11.000
131.917
Coefficients Standard Ero
118.506
0.016
33.575
0.031
t Stat
3.530
P-value
0.006
Lower 95% Upper 95% Lower 95.0% Upper 95.0%
194.458
194.458
Intercept
(x1)
(2)
42.553
42.553
-0.517
0.618
0.088
0.055
0.088
0.055
-1.573
0.359
4.381
0.002
-2.385
-0.761
2.385
-0.761
Transcribed Image Text:The prices of Rawlston, Inc. stock (y) over a period of 12 days, the number of shares (in 100s) of company's stocks sold (x1), and the volume of exchange (in millions) on the New York Stock Exchange (x2) are shown below. Day (x1) 950 (x2) (v) 87.50 11.00 2 86.00 945 11.25 3 84.00 940 11.75 11.75 4 83.00 930 5 84.50 935 12.00 6. 84.00 935 13.00 13.25 7 82.00 932 8 80.00 938 14.50 78.50 925 15.00 10 79.00 900 16.50 77.00 77.50 11 875 17.00 12 870 17.50 Excel Analysis Toolpak was used to determine the least-squares regression equation. The computer output is shown below. SUMMARY OUTPUT Regression Statistics Multiple R R Square 0.949 0.901 Adjusted RS Standard Em Observations 0.879 1.205 12.000 ANOVA df MS 59.424 Significance F SS Regression 2.000 118.847 40.922 0.000 Residual 9.000 13.069 1.452 Total 11.000 131.917 Coefficients Standard Ero 118.506 0.016 33.575 0.031 t Stat 3.530 P-value 0.006 Lower 95% Upper 95% Lower 95.0% Upper 95.0% 194.458 194.458 Intercept (x1) (2) 42.553 42.553 -0.517 0.618 0.088 0.055 0.088 0.055 -1.573 0.359 4.381 0.002 -2.385 -0.761 2.385 -0.761
14. Use the output shown above and write an equation that can be used to predict the price of the stock.
y = 33.575 +0.031x +0.359x2
b. y= 118.506 - 0.016x1 - 1.573x2
y = -0.125x1 - 1.06x2
d.
a.
с.
y = 3.53 - 0.5176x1 - 4.381x2
Transcribed Image Text:14. Use the output shown above and write an equation that can be used to predict the price of the stock. y = 33.575 +0.031x +0.359x2 b. y= 118.506 - 0.016x1 - 1.573x2 y = -0.125x1 - 1.06x2 d. a. с. y = 3.53 - 0.5176x1 - 4.381x2
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