The Results Of Heteroskedasticity Test Done For Model II Essay

We first started by examining the different variables in relation to rounds, and seeing their corresponding p-values. Since YARD, RANGE, and WINTER*FEE all have insignificant p-values, we remove them from our model in order to increase the accuracy, Using the Wald test, we confirm they are irrelevant to the model, and are taken out. Then, using our new model, we must test for heteroscedasticity using the White test. We find there is, and so we adjust our model accordingly. Finally, we reanalysed all the variables and made sure they were all still logically correct, and conclude our model is BLUE. From here, we computed

15 In testing the hypotheses: H0 β1 ’ 0: vs. H1: β 1 ≠ 0 , the following statistics are available: n = 10, b0 = 1.8, b1 = 2.45, and Sb1= 1.20. The value of the test statistic is:

b. Then, we also determine the regression equation and correlation between North's engine cost and the average age of fleet. Please refer to Annex 3 for the QM results.

For d2, t-statistic= 1.8774, t-statistic < t-critical. Thus we do not reject Ho and d2 is not significant.

The null hypothesis is rejected since the p-value is below the significance level of 0.05.

The statistical significance of a coefficient tests determines coefficients potential of being zero. The zero potential increases when there is significant variance in the independent variables. A large variance also suggests that the variable used have no effect on the dependent variable.

Because of the method of monthly data collection, absolute randomness could not be obtained; however, it was decided that 5 iterations was sufficient because the sixth iteration showed a decrease in the quality of the residual plots. The first test performed was the p-value test of the individual variables. A p-value is the probability, ranging from 0 to 1, of obtaining a test statistic similar to the one that was actually observed. The only input that did not have a p-value less than 0.05, which was the chosen significance level, was the “Number of Walmarts” variable; the number of Walmarts has no specific effect on the output, property crime rate. The R2 of the analysis, or the coefficient of determination, provides a measure of how well future outcomes are likely to be predicted by the model. R2 values range from 0 to 100% (or 0 and 1) and the

Same as the results of 10-year period, the healthcare excess return is positively correlated with the utility excess return while negatively correlated with the material excess return. Compared to the 10-year results, it is found that beta of X1 decreases from 0.2750 to 0.2045 and beta of X2 decreases from -0.3165 to -0.3382. Also, beta of X0 drops which indicates without incorporating the event risk, more other variable is explained by the explanatory variables. As the p-value is less than 5% significance level, the relationships are both statistically significant. The standard errors of X1 and X2 are both small enough to indicate that the observations are close to the fitted

It tells that the t-statistic with 97 degrees of freedom was 2.14, and the corresponding p-value was less than .05, specifically around 0.035. Therefore, it is appropriate to conclude the research study was statistically significant.

a. What are the estimated sales for the Bryne store, which has four competitors, a

5) From calculations, computed z value is more than -1.65 and falls within Ho not rejected region. Ho is not rejected at α = 0.05 &amp; α = 0.01 significance levels.

As mentioned in class, one commonly employed solution to heteroscedasticity is to adjust the standard errors for the possible presence of heteroskedasticity, i.e. we compute the heteroskedasticity-robust standard errors, which are also referred to as heteroskedasticity-consistent standard errors. Rerun the regression in part (2) with the OLS standard errors replaced by heteroskedasticity-robust standard errors. Comment on the differences between the OLS standard errors in part (2) and the heteroskedasticity-robust standard errors in this part.

Table 4.1 presents the panel unit-root test results. There are two groups of hypotheses that are involved here. In the first four methods, the null hypothesis is: there is panel unit-root and the alternative hypothesis is: there is no panel unit-root and the decision

The two stage regressions are reported in Table 8. Column 1 in Panel A shows that institutional trading frequency is decreasing in the stamp tax, which is consistent with the fact that raised stamp tax produces higher trading cost. Kleibergen-Paap rk Wald F statistic is a standard test for the weak instrument problem, which is ruled out since the p-value is 0.000. Columns 1 and 2 in Panel B suggest that the results from baseline regressions hold in IV regressions, where more frequent trading generates lower price informativeness. Difference-in-Sargan statistics show that the 2SLS and OLS estimates are the same. (p-value ranges from 0.35 to 0.55)

All in all, the results displayed by the analysis of each study correspond to the overall effort of the posed research. For example, supported results aligned with study hypotheses, but signified that certain mechanisms underlie the criterion for each of the tested variables on different levels according to Ashkanasy, Falkus, and Callan (2000). Whereas, Beeri et al., (2013)