ANOVA df Total SS Regression 2 121.0625 60.5312 50.1910 Residual 9 10.8542 1.2060 11 131.9167 MS 120.0522 F -0.0181 Coefficients Standard Error t Stat P-value -1.5626 Intercept (x₂) (x₂) (a) Use the output shown above and write an equation that can be used to predict the price of the stock. (Round your numerical values to four decimal place.) 9= 28.4829 4.21 Significance F 0.0268 -0.68 0.0000 0.3026 -5.16 0.0023 0.5160 0.0006 (b) Interpret the coefficients of the estimated regression equation that you found in part (a). (Give your answers in dollars. Round your answers to four decimal places.) when the volume of exchange on the NYSE is held constant. As the volume of exchange when the number of shares of the stock sold is held constant. As the number of shares of the stock sold goes up by 100 units, the stock price goes down by $ goes up by 1 million, the stock price goes down by $ (c) At a 0.01 level of significance, determine which variables are significant and which are not.
ANOVA df Total SS Regression 2 121.0625 60.5312 50.1910 Residual 9 10.8542 1.2060 11 131.9167 MS 120.0522 F -0.0181 Coefficients Standard Error t Stat P-value -1.5626 Intercept (x₂) (x₂) (a) Use the output shown above and write an equation that can be used to predict the price of the stock. (Round your numerical values to four decimal place.) 9= 28.4829 4.21 Significance F 0.0268 -0.68 0.0000 0.3026 -5.16 0.0023 0.5160 0.0006 (b) Interpret the coefficients of the estimated regression equation that you found in part (a). (Give your answers in dollars. Round your answers to four decimal places.) when the volume of exchange on the NYSE is held constant. As the volume of exchange when the number of shares of the stock sold is held constant. As the number of shares of the stock sold goes up by 100 units, the stock price goes down by $ goes up by 1 million, the stock price goes down by $ (c) At a 0.01 level of significance, determine which variables are significant and which are not.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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net/w o/student/Assignment-Responses/last?dep=30597856#Q12
Excel was used to determine the least squares regression equation. Part of the computer output is shown below.
ANOVA
?
H₂
Residual
Total
Intercept
f2
Regression 2 121.0625 60.5312 50.1910
(x₂)
(x₂)
df
f3
SS
9
#
10.8542
11 131.9167
3
*
Coefficients Standard Error t Stat
120.0522
-0.0181
-1.5626
MS
1.2060
f4
28.4829
$
(a) Use the output shown above and write an equation that can be used to predict the price of the stock. (Round your numerical values to four decimal place.)
ý =
F
(b) Interpret the coefficients of the estimated regression equation that you found in part (a). (Give your answers in dollars. Round your answers to four decimal places.)
As the number of shares of the stock sold goes up by 100 units, the stock price goes down by $
goes up by 1 million, the stock price goes down by $
0.0268 -0.68
f5
0.3026 -5.16
(c) At a 0.01 level of significance, determine which variables are significant and which are not.
State the null and alternative hypotheses for the number of shares (in 100s) of company's stocks sold. (Enter != for # as needed.)
Ho:
4.21
Find the value of the test statistic. (Round your answer to two decimal places.)
%
LC
5
Significance F
0
f6
0.0000
P-value
4-
0.0023
0.5160
6
0.0006
f7
4+
when the volume of exchange on the NYSE is held constant. As the volume of exchange on the NYSE
when the number of shares of the stock sold is held constant.
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Transcribed Image Text:esc
You may need to use the appropriate technology to answer this question.
The prices (in $) 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
2
1
3
4
5
2 86.00 944 11.50
6
7
8
9
y
10
87.50 951 10.75
84.00 939 11.50
x₁
83.00 929 12.00
84.50 934 11.75
84.00 936 12.75
82.00 933
80.00 939
78.50 926
x2
Regression
79.00 899 16.75
Excel was used to determine the least squares regression equation. Part of the computer output is shown below.
ANOVA
Residual
11 77.00 874 17.25
Total
12 77.50 869 17.25
#
13.50
3
14.25
15.25
df
SS
10.8542
2 121.0625 60.5312 50.1910
131.9167
f4
$
4
MS
1.2060
f5
%
5
F
f6
4-
Significance F
6
f7
0.0000
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1:18 PM
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