Regression Statistics |
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Multiple
R |
0.9197 |
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R
Square |
0.8459 |
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Adjusted
R Square |
0.8202 |
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Standard
Error |
1.1994 |
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Observations |
8 |
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ANOVA |
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df |
SS |
MS |
F |
Significance F |
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Regression |
1 |
47.4 |
47.4 |
32.93 |
0.0012 |
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Residual |
6 |
8.6 |
1.4 |
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Total |
7 |
56.0 |
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Coefficients |
Standard Error |
t Stat |
P-value |
Lower 99% |
Upper 99% |
Intercept |
-10.42105 |
4.9711 |
-2.0963 |
0.0809 |
-28.8510 |
8.0089 |
X |
0.78947 |
0.1376 |
5.7382 |
0.0006 |
0.2794 |
1.2996 |
a)
so, regression line is Y? =
-10.4211 + 0.7895 *x
b)Predicted Y at X= 40 is
Y? = -10.4211 +
0.7895 * 40 =
21.157894736842
answer: $21157895
c)
on average, increasing Advertising expenditure
by $1000, predicted sales increase by $789474
d)R Square = 0.8459
about 84.59% of variation in observation of Y is explained by
variable X
e)std error ,Se = 1.1994
f)
F=32.93
p value= 0.0012=0.05, so, regression model is useful
g)
Ho: ß1= 0
H1: ß1 = 0
T test stat=5.7382
p value= 0.0012=0.05, so, slope is significant
h)
99% CI is
lower confidence limit = 0.279
upper confidence limit= 1.300