Ex 9 1) ANOVA. df Regres sion Residua l. 1 7. Total. 8.

Ex 9
1)
ANOVA
df SS MS F
Significa
nce F
Regres
sion 1
58.74
794
58.74
794
40.54
354
0.00037
9
Residua
l 7
10.14
306
1.449
009
Total 8
68.89
1
Yes there is a linear regression between the consumer spending and income. We
can see from the F value that the ANOVA is significant so there is a significance
in the data provided. Also from the p value we can see that the coefficients are
also significant as the p value is less than 0.05.
2)
The R squared value gives the percentage of consumer spending explained by
the income. The value is 85.2767 %
3)
Since we know that
correlation2=R2
So we get the
correlation2=0.852767
correlation=0.9234
4)
The standard error is given by 1.203748.
Standard error represents the average distance that the observed values fall
from the regression line. Conveniently, it tells you how wrong the regression
model is on average using the units of the response variable
5)
Since the equation of the line is
y=4.563+1.847 × x
Putting the value x = 0, 3, 5 and 6
We get,
y=4.563+1.847 ×0=4,563
y=4.563+1.847 ×3=10.104