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Finance & Accounting
Pages 4 (1004 words)
Financial data Analysis PART I This part will involve regression analysis, to establish the nature of the relationship between WHEATSF and WHEATHD. For the purpose of this analysis, WHEATSF will be taken as the dependent variable and WHEATHD as the dependent variable…
A clear linear relationship is not evident, which could be an indicator that WHEATHD is a poor predictor of WHEATSF. Figure 1: the plot of WHEATSF against WHEATHD Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .421a .177 .174 27.2183 .177 53.344 1 248 .000 Table1: Model regression summary Table 1 above presents a summary of the regression summary. From this, adjusted R squared is 0.17, a figure that is very small indicating that the model is not very good in predicting the dependent variable as it is highly subject to chance rather than statistical relationship between the two variables. However, the p-value is less than 0.01, an indicator that the model is statistically significant, or rather we have enough evidence to assert that WHEATHD has some predictive power on WHEATSF. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 500.582 24.519 20.416 .000 WHEATHD(P) -.443 .061 -.421 -7.304 .000 Table 2: a. Dependent Variable: WHEATSF(P) Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 532.035 17.694 30.069 .000 WHEATSF(P) -.400 .055 -.421 -7.304 .000 Table 3: a. ...
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