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# Statistical Analysis of the Missing Values Essay Example

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## Statistical Analysis of the Missing Values

Statistical Analysis of the Missing Values. Analysis” setup for the F test returns erroneous output, we change the way the variable ranges are defined and obtain a different result this time. The F statistic is now just 1.21 and the associated significance statistic is p > 0. This leads us to assume that the variance of bonuses across gender is equal. Accessing the two-sample t test with equal variances assumed, one finds that the calculated t statistic is 0.99, for which the one-tailed p value is p=0. Since this does not surpass the required threshold of α = 0.05, we are unable to reject the null hypothesis of equal means. Both in the absolute and going by the results of the t test, there is no evidence to believe that the difference in bonus pay between men and women is due to anything more than chance.

It is very likely that men and women receive the same bonus pay. Proceeding to total pay, the result of the F test for variances (Table 7 below) returns a value of F that is too low (0.88) to be associated with the level of significance statistic required for a one-tailed test of difference. Proceeding, therefore, to the t test routine on the assumption that the variances are equal, Excel generates the output shown in Table 8 below. The computed t value, at -1.44, is rather low and is associated with a significance statistic p = 0. Given the result of the t test shown in Table 8 above, the difference between the sexes is not large enough to justify rejecting the null hypothesis that total pay is equal across sexes. We therefore conclude that pay does not materially differ by gender. Total pay is conceivably influenced by the bonus scheme in force at each of the 4 factories, by whether male and female employees have comparable seniority and educational attainment. However, Table 9 below shows that, except at Fareham, the genders are about evenly distributed in the four manufacturing sites. Hence, the effect of differential bonus schemes does not necessarily explain the slight edge men enjoy in total pay.On the other hand, length of service and educational qualifications may explain why total pay for men is marginally better. Nonetheless, the differences are slight enough as not to invalidate direct comparison across genders. The Adjusted R Squared for days’ training and all the other independent variables are shown overleaf. Adjusted R2, otherwise known as the coefficient of determination, suggests that days’ training explains very little (virtually nothing, in. Statistical Analysis of the Missing Values.

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