This is exhibited by figure 3 in the appendix.This variable was used to measure the average cash compensation awarded to CEO for the 73 companies over the period of the last five years. The analysis of compensation shows that Index 1 companies had a mean growth rate of 78. While on the other hand, Index 2 companies had a compensation growth rate of 33.29% for their CEO’s. Index 3 companies had a compensation growth rate of 16.34% over a period of five years (Healey, 2011). The mean compensation growth rate for all the companies stood at 32.04% and Index 3 companies did not meet this set out criteria. Therefore, we can conclude that companies with long term plans have the capability of compensating their employees in a proper manner. This is shown by figure 4 in the appendix (Moore, 2006).The process of analyzing the data from the 73 small companies was done through the use of SPPS software. Using the software, we first define the five variables used in the data in SPSS. After the definition of variables we conduct analysis by comparing the means under the statistics tab then choose compare means and choose means. Alternatively instead of choosing means you could choose ANOVA and then analyze all the variables as it was done in this assignment. Using SPSS ensures accuracy of the analysis being undertaken. Other tests that can be conducted under the SPSS statistics tab are One-sample T Test, Independent-Samples T Test, Paired-Samples T Test and One-Way ANOVA (Johnson, 2009). The outputs of these results are the ones used in the analysis of data as it was done above in this assignment.In the process of analyzing data, several procedures have to be utilized in a proper and structured manner. From the analysis of statistics using the t-test, we get a mean value of 33. The upper confidence levels stood at 42.19% while the lower levels of confidence stood at 24. The ANOVA analysis of all the variables compared to the index variable show the significance of the entire analysis exhibit a figure of between 2.82 and 19. The significance of the results show that the ranges of figures are quite related. The income variable gives us the best results in terms of its significance to the study being undertaken. For instance, the significance of the income figure is 0.067 and this resonates well with the results of the analysis. ANOVA analysis show that results of
Crawley, M., 2011. Statistics: An Introduction Using SPSS. Chicago, IL: John Wiley and Sons.
Gibilisco, S., 2004. Statistics Demystified. New York, NY: Jones & Bartlett Learning.
Hays, W., 2007. Statistics. Michigan, MA: Elsevier.
Healey, J., 2011. Statistics: A Tool for Social Research. New York, NY: Lippincott Williams & Wilkins.
Johnson, R. and Bhattacharyya, G., 2009. Statistics: Principles and Methods. New York, NY: Routledge.
Moore, D. Holtzbrinck, S. and Notz, W., 2006. Statistics, Concepts and Controversies. Sydney: Cengage Learning.
Figure 1: Showing means for Revenue compared to other variables
Figure 2: Showing means for Income in comparison to Index variable
Figure 3: Showing means for Book variable in comparison to Index variable
Figure 4: Showing means for Comp variable in comparison to Index variable
Figure 5: Showing the ANOVA analysis and one sample t test for the variable revenue
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