The relation between the two variables the NYSE Index and the Inflation appears

# The relation between the two variables the nyse index

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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. T-STAT 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 F-TEST 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 p-value 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

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