Pdf factor

Privacy Overview This website uses cookies so that we can provide you with the best user experience possible. Strictly Necessary Cookies Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings. Enable or Disable Cookies. A number is divisible by 5 if the digit at ones place is either 0 or 5. Find the lcm of 40 80 and Q 4 explanation factors and multiples worksheet for grade 5.

Highest common factors hcf least common factors lcm relation between lcm hcf. Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. Work Bun. July 16, In pdf factors and multiples worksheet class 5. Leave a Reply Cancel reply Your email address will not be published.

Previous post Year 2 Money Worksheets Australia. Related Post. Downloads Last Week Report Software. Related Software. Vpn One Click Free to try. Protect your privacy online and unlock all blocked services.

Protected Folder Free to try. Password-protect your folders and files from being seen, read or modified. Random Password Generator Free. Create and manage secure and strong passwords. Cryptomator Free. Protect your documents from unauthorized access.

User Reviews. Show Reviews. It allows you to instantly create PDFs with a few clicks. Users can also combine multiple documents into one PDF. The software features the ability to preview your conversion and allows you to encrypt the PDF files as well.

Finally, Factor 4, composed of the NEI indicator, alone explains The results of the Explained Total Variance Matrix are in Table 7, which presents the eigenvalues of the correlations matrix with the respective percentages of shared variance of the non-rotated factors original variables.

Table 7 shows that the number of factors formed and retained was equal to one, thus meeting the Kaiser criterion. Based on the first results, instead of working with the six indebtedness indicators, only one factor that includes the five indicators was formed. They are responsible for explaining Table 8 shows the factor loads of each variable. As previously discussed, according to Hair et al. Table 8 shows that all the variables analyzed exceed the value of 0.

Therefore, this variable seems to be the one that most deviates from the behavior presented by the other variables grouped in the factor. Besides presenting a communality lower than 0. The KMO analysis result is 0. Thus, these tests point out the adequacy of factor analysis for the analysis and treatment of data. As all the variables were grouped in a single factor, the factor analysis was not tested with the rotated factor, because the results would be the same.

This grouping can be verified in both factor analyses because IND was grouped both in the first analysis with all the variables included in the test and only with those that presented significant correlation with IND. It does not present a high communality value in the second-factor analysis test. Therefore, it was decided not to identify this variable as a variable that has a latent relationship with IND. These results respond to this study's main objective, which sought to evaluate the possibility that the indebtedness indicator proposed by the regulatory model may present a correlation or latent relationship with those identified in the literature.

Significant correlations with IND were verified, and latent relationships were found between the indebtedness variable used by ANEEL and three other indicators recommended in the literature. We chose to not name the Factors, since the objective of this paper is not to create factors to replace the indicators analysis, but to analyze how the indebtedness indicators used by the literature behaves concerning the variable used by ANEEL.

This study compares the financial indicators of indebtedness studied in the literature with the ANEEL indebtedness indicator. The lack of equivalent studies not allowed comparing the results with those of other authors, this being a limitation of this research. This study verifies possible relationships between the indicators proposed by the regulatory agency and the indicators verified in the literature. In general, studies assessing the relevance of performance indicators indicate that those related to indebtedness form one of the most relevant groups for evaluating the performance of companies in the regulated segments in Brazil Bomfim et al.

ANEEL also gives this importance to indebtedness indicators. Our paper fits in the line of research that includes the analysis of economic-financial supervision. This line covers the modeling and systematic supervision of regulatory sectors and, in turn, also consists of the indicators proposed by agencies to monitor these modeling or systematic and obtain the performance of entities.

We use factor analysis, and the division of indicators among the factors pointed out that there are latent relationships between the indebtedness indicator proposed by ANEEL and three other indicators used in the literature. These results, in which it is possible to identify these latent relationships between the agency and the literature indicators, were also found by Soares in the health sector. Thus, it is possible to indicate that the literature does not deviate from the practice in the matter of analyzing performance indicators in some regulated sectors.

It can be concluded that, although ANEEL developed the indebtedness indicator to conduct a sectoral analysis, it presents a pattern similar to other indicators used in the literature. In addition to mainly fulfilling its mission of generating subsidies for the moment of concessions renewal, the indebtedness indicator also presents latent relationships with some others used by literature.

Still, it does not seem to be a fundamental factor to detach it from other indicators, making it typically sectoral. Given the findings of this study, it would be unwise to point out that the indebtedness indicator proposed by ANEEL is relevant for the economic-financial supervision of distributors just because it presents behavior similar to those found in the literature. However, it is possible to point out that the indicators grouped with the ANEEL indicator are probably the most appropriate to analyze the indebtedness of companies in the electric energy sector.

In practice, our results can contribute to the rationality of the agents involved in the electric energy sector. Stakeholders can better understand how commonly used indebtedness indicators can relate to the regulatory agency's indebtedness indicator, ANEEL. In the literature, this paper fills the gap between comparing the indebtedness indicators used by the literature and the regulatory agency in the electric energy sector.

It thus contributes to the development of studies that accompany the dynamics of the economic-financial supervision of SEB from an empirical- quantitative perspective, analyzing the operators' interactions with the modeling of performance indicators both in literature and practice regulatory agency. However, this paper has limitations, and among them, there is the contractual issue of the sector, which can influence the entity's indebtedness analysis.

The electric energy distribution segment has several contractual specificities that were not considered in this paper, such as operational, financial, shareholder public or private control , and regulatory objectives, differentiated for each distributor. There are also problems with the quality of the information, it is verified in the financial statements; for example, operators with negative net equity.

These and other possible specificities have not been addressed in this paper. As a limitation, the approach from a single quantitative perspective is also considered because one can use, for example, Data Envelopment Analysis.

The analyses can also be developed under a new perspective of quantitative methodology. In addition, this paper only analyzes the dimension of indebtedness, and it is known that the new financial supervision of the distributors includes other dimensions. Thus, it is also possible to analyze the other dimensions, such as investment and profitability, each with its respective indicators proposed by ANEEL.

Finally, it is possible to point out that the analysis we present in this paper can also be done in other sectors, using the sectorial indicators of other regulatory agencies. Arocena, P. Generating efficiency: economic and environmental regulation of public and private electricity generators in Spain. International Journal of Industrial Organization, 20 1 , Regulation by contract: a new way to privatize electricity distribution? The World Bank. Bezerra, F. Bomfim, P. Borio, C. Towards a macroprudential framework for financial supervision and regulation?

CESifo Economic Studies, 49 2 , Braga, K. Energy Procedia, , Breitenbach, M. Caldeira, T. Campos, J. Carregaro, J. Castro, N. Rio de Janeiro.