The relation between the two variables the NYSE Index and the Inflation appears to be linear.
The regression results were as follows:
Simple Linear Regression
Parameter
Value
S.E.
TSTAT
Notes
Constant
4981.348053
Beta
61.257243
3.125318
19.600321
H0: beta = 0
Elasticity
2.097994
0.107039
10.257911
H0: elast. = 1
Simple Linear Regression  Analysis of Variance
ANOVA
DF
Sum of Squares
Mean Square
Regression
1.000000
3.872313e+8
3.872313e+8
Residual
37.000000
3.729459e+7
1.007962e+6
Total
38.000000
4.245259e+8
1.117173e+7
FTEST
384.172584
Correlation Coefficient:
r = 0.955065457
Residual Sum of Squares: RSS = 37294590.46
Coefficient of Determination( R
2
): 0.9121500272
The regression Coefficients were calculated as follows:
β
1
=
61.257243
β
0
=

4981.348053
The regressed equation is as follows:
NYSE (Y
1
) =
4981.348053
+
61.257243
(CPI) + e
t
β
0
the intercept of 4981.348053, can be interpreted as the value you would predict for NYSE
Index when inflation is 0. Although this statement is not practically applicable as the Index
cannot be negative and the CPI cannot practically assumed to be 0.
When CPI is 100 i.e. price level of the base period, the price would be positive. NYSE can be
determined as:
4981.348053 +
61.257243(100)= 1144.376.
The descriptive statistics of the model is given below:
Simple Linear Regression  Descriptive Statistics
Statistic
Value
Mean X
155.379487
Biased Variance X
2646.006943
Biased S.E. X
51.439352
Mean Y
4536.771026
Biased Variance Y
1.088528e+7
Biased S.E. Y
3299.284769
Mean F
4536.771026
Biased Variance F
9.929008e+6
Biased S.E. F
3151.032916
Mean e
0.000000
Biased Variance e
956271.550169
Biased S.E. e
160.7643520
P Value
<0.0001
P value
T is simply the calculated difference represented in units of standard error. The greater the
magnitude of T (it can be either positive or negative), the greater the evidence against the null
hypothesis that there is no significant difference. The closer T is to 0, the more likely there isn't
a significant difference. In current T statistic of19.600321, we can safely say that β
1
significantly
different from 0 and thus, the model is Significant.
The pvalue of this t statistic is less than
0.0001 or less than 0.01% chance of having an insignificant slope term represents that the slope
term is highly significant and represents the significance of the model.
Standard Error
The 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. Smaller values are better because it indicates that the
observations are closer to the fitted line.
The Standard error of the slope coefficient
3.12which indicated that the estimated value of
NYSE Index varies on average by 3.12 units from the actual value.
Coefficient of Correlation
Coefficient of correlation is a measure of the strength and direction of the linear relationship
between two variables. The correlation coefficient, r=
0.96, suggests a fair high degree of
positive correlation between CPI and NYSE Index. It means a rise in NYSE Index is related to
rising inflation.
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 Fall '08
 Olander
 Finance, Regression Analysis, Inflation, Corporate Finance, Options, Economic Development, gold prices